C.L. Brouwer - Geometric variability of organs at risk in head and neck radiotherapy

Charlotte L. Brouwer

GEOMETRIC VARIABILITY OF ORGANS AT RISK

IN HEAD AND NECK

RADIOTHERAPY

eometric variability of organs at risk in head and neck radiotherapy

Brouwer, C.L. Geometric variability of organs at risk in head and neck radiotherapy PhD dissertation, University of Groningen, The Netherlands ISBN: 978-94-6332-075-7 ISBN (electronic version): 978-94-6332-076-4 Š Copyright 2016 Charlotte L. Brouwer, Groningen, The Netherlands All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by means without prior permission of the author or, when appropriate, of the publishers of the published articles.

Cover: Karin van der Vegt en Marjolijn Brouwer Lay-out: Karin van der Vegt, Revista tekst & ontwerp Printed by: GVO, Ede Printing of this thesis was financially supported by: University of Groningen and University Medical Center Groningen Elekta B.V.

Geometric variability of organs at risk in head and neck radiotherapy Proefschrift

ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen op gezag van de rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden op woensdag 19 oktober 2016 om 12.45 uur

door

Charlotte Louise Brouwer geboren op 1 februari 1983 te Zwolle

Promotor

Prof. dr. J.A. Langendijk

Copromotores

Dr. ir. N.M. Sijtsema Dr. R.J.H.M. Steenbakkers

Beoordelingscommissie Prof. dr. S. Nuyts Prof. dr. C.H.J. Terhaard Prof. dr. B.J.M. Heijmen

Content Chapter 1:

General introduction

Chapter 2:

Variability in the delineation of head and neck organs at risk: Variation between guidelines and impact on dose and NTCP

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2 Chapter 3:

Variability in the delineation of head and neck organs at risk: 3D variation in delineation within guidelines

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Chapter 4:

International consensus delineation guidelines for head and neck organs at risk

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Chapter 5:

Geometric and dosimetric variability in head and neck organs at risk during the course of radiotherapy: Literature review

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Chapter 6:

Geometric and dosimetric variability in head and neck organs at risk during the course of radiotherapy: Patient selection for adaptive radiotherapy

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Chapter 7:

Summarizing discussion and future perspectives

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Samenvatting Dankwoord Curriculum Vitae List of publications

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133 141 145 146

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CHAPTER

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eneral introduction

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General introduction Radiotherapy has a pivotal role in the treatment of head and neck cancer patients [1].

Currently, tumour control and survival have been improved by the combination of radio­ therapy with chemotherapy or biological agents (i.e. cetuximab) [2, 3]. The meta-analysis of Pignon et al. [2] showed that the overall survival improved with 6.5% after 5 years when radiotherapy was combined with concomitant chemotherapy. In addition, the addition of cetuximab to radiotherapy resulted in significantly higher rates of locoregional control and overall survival when combined with radiotherapy as compared to that observed after radiotherapy alone [3, 4].

The last decades, the incidence of patients with oropharyngeal cancer related to the human papillomavirus (HPV) is markedly rising [5–7]. This is relevant as patients with HPV-positive tumours that received radiotherapy either or not combined with concurrent chemotherapy showed significantly better prognosis than patients with HPV-negative tumours [8, 9].

The result of this improved survival is that the prevalence of head and neck cancer survivors at risk for radiation-induced side effects is increasing. In general, radiation-induced side effects are distinguished in early and late effects [10]. Early or acute effects emerge during or immediately after the end of therapy, while late side effects develop after latency periods of at least 90 days to years. In contrast to early effects, the majority of late side effects of radio­therapy are considered to be irreversible [10]. Xerostomia is one of the most severe and frequently reported late side effects that has a major influence on quality of life [11–13]. Furthermore, swallowing problems are frequently reported and have a notorious effect on quality of life [13].

Intensity modulated radiotherapy (IMRT) can be applied to conform doses sufficient to eliminate all viable cancer cells within the target region, while sparing organs at risk (OARs) as much as possible. It has been shown that the introduction of IMRT resulted in a sig­ni­ficant improvement of the recovery of saliva secretion and a reduction of xerostomia [14–16]. Still, with currently used concomitant chemo-radiation regimens, the limits of accep­table toxicity have been reached with more than half of the patients treated with IMRT suffering from moderate to severe xerostomia 6 months after treatment [17].

Normal tissue complication probability (NTCP) models [18] are used to estimate the probability that patients develop radiation induced side effects. Many types of models that relate dose-volume characteristics to outcome have been proposed in the literature. The Lyman-Kutcher-Burman model was one of the earliest proposed [19, 20], followed by other models that attempt to explicitly model tissue architecture [21–23]. All of these models use information only about fractionation and the dose distribution in a single organ at risk. Since the probability of a complication may be affected also by multiple clinical prognostic factors,

8 CHAPTER 1 - General introduction

El Naqa et al. were one of the first who proposed other approaches using multivariable logistic regression analysis [24]. Input parameters to these models are usually clinical and dosimetric factors.

The dose to the parotid glands is the most important factor associated to xerostomia-related endpoints [17, 25, 26]. For swallowing problems, the dose to several swallowing structures are of major importance [27].

NTCP and tumour control probability (TCP) models can be applied to optimize the treatment plan, i.e. minimizing the risk for side effects with equal or higher TCP [28]. Furthermore, NTCP and TCP models can be applied to compare different treatment plans and to select patients for more advanced treatments such as adaptive radiotherapy (ART) and proton therapy [29].

A number of uncertainties in the preparation and delivery of radiotherapy exists, which should be minimized to accurately develop and reliably apply NTCP models [30]. Reducing the uncertainties could also lead to smaller treatment margins or smaller robustness parameters in treatment planning, leading to a higher plan quality (i.e. good target coverage with lower dose to the OARs). Dose distributions of IMRT plans are much more sensitive to these uncertainties than 3D-CRT plans, because of the steeper dose gradients in IMRT treatment plans. The most important uncertainties include setup variation, delineation variability, and anatomical variation (variation in shape/position of targets and organs within the patient) [31]. Setup variation in the head and neck area is in the order of 2 mm standard deviation (SD) [32]. Setup variation can be accounted for in position verification protocols, and will be outside the scope of this thesis.

Variability in delineation Variability in the delineation of targets is found to be the largest source of uncertainty (i.e. the weakest link) in head and neck radiotherapy, with standard deviations of 3.4-7.7 mm for CT-based GTV delineation in nasopharynx cancer [33]. Variability in delineation of target volumes and OARs could result in sub-optimal treatment plans with systematic dose errors for all treatment fractions [33, 34]. This may result in inconsistent patient treatments and inconsistent reported dose parameters, with a potential under- or overestimation of the patients’ TCP and NTCP [35].

Variability in delineation is caused by a number of aspects; limited imaging resolution [36], intra-observer variation (‘observer noise’ and the precision of the delineation tool) [37] and interpretation differences, caused by different guidelines and/or differences with regard to the level of education and frequency of training [38, 39]. More and better quality imaging has become available and CT-slice distance is generally reduced to 2 mm. Also, more and more dedicated software for contouring has become available and education and training sessions are being organised on a regular basis (European training sessions

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and delineation meetings at individual departments). For target volumes, international consensus guidelines have been defined to minimize variability in delineation and optimize consistent radiotherapy practice [40, 41]. For OARs however, such guidelines are lacking.

The introduction of consensus guidelines are expected to result in reduced interobserver variability [42].

Anatomical variation during the treatment The pre-treatment CT on which IMRT plans are based, is only a snapshot of the patient’s anatomy. Barker et al. [43] were one of the first who reported on variability in shape and position of head and neck targets and OARs within the patient during the course of radiotherapy. The median volume loss on the last day of treatment was 69.5% for the GTV and 28.1% for the parotid glands. The parotid glands also shifted medially over time, the median medial shift of the centre of mass was 3.1 mm at the end of treatment [43]. Robar et al. [44] reported shortly thereafter on the consequences of the anatomic changes for the dose distribution. On average, dosimetric changes were moderate, but for a couple of patients outliers were seen (standard deviation and range of dosimetric changes of the parotid gland were 6 and 15% respectively).

Since 2010, the amount of studies reporting on anatomical and dosimetric changes has increased dramatically [45–65]. Variation in anatomy causes more dose deviations in OARs than in target volumes [66–69]. Not all of the studies reported to what extent anatomical changes translate into dosimetric changes, and for which group of patients this would actually be of clinical relevance. In the study of Chen et al. [67], mean dosimetric changes to the parotid gland mean dose of 10.4 Gy were found, whereas Castadot et al. only reported changes of 0.8 Gy [70]. This pleads for a careful introduction of adaptive radiotherapy since only a selected set of patients would benefit from an extremely labour and resource intensive procedure. Theoretically, adaptive radiotherapy could be fully auto­­mated, as Yan et al. introduced in 2005 [71]. The authors predicted that adaptive radio­therapy will become a new treatment standard that will eventually replace the pre­designed treatment plan in routine clinical practice. However, to date and more than 10 years later, online adaptive radiotherapy still remains a future perspective due to its labour and resource intensive nature. Furthermore as yet, no commercial conclusive solution is available for a save and controlled workflow. Therefore, ‘flags’ to select patients for which ana­tomic changes result in relevant dose deviations need to be established, to be able to select specific patients for an adaptive radiotherapy schedule.

10 CHAPTER 1 - General introduction

Outline of the thesis Minimizing the uncertainties in delineation and position and shape of the OARs is expected to result in improved and more general applicable NTCP models. It is of major importance to accomplish generally applicable NTCP models, since these models are essential tools in selecting patients for advanced treatments [29], and in optimizing the treatment plans for every individual patient to guarantee optimal quality of life.

In this thesis, we will focus on variability in delineation and how to minimize its effect on dose in chapters 2, 3 and 4. Chapter 5 and 6 will focus on anatomical variability during the course of treatment and its effect on the dose distribution.

Chapter 2 reports on the differences between several OAR delineation guidelines, and its effect for dose-volume parameters and corresponding predictions of NTCP models.

Chapter 3 subsequently shows the variation in delineation of OARs within a guideline, with detailed analysis on sub-regions of the OARs. Causes of interobserver variability and differences between the different sub-regions are discussed.

With the knowledge of Chapter 2 and 3, in Chapter 4 international consensus guidelines for the delineation of the most important OARs in the head and neck area are presented.

Chapter 5 reports on a systematic review on anatomic and dosimetric changes of head and neck OARs during the course of radiotherapy. Also, factors related to these changes are studied which could potentially be used to select patients at risk for dosimetric changes of OARs in an adaptive radiotherapy protocol.

Finally, the potential pre-treatment selection parameters identified in Chapter 5 are tested in Chapter 6 in a cohort of 113 patients. A method to select patients at risk for dosimetric changes of the parotid glands is established, and validated in another, independent patient cohort.

The findings of this thesis are summarized and discussed in Chapter 7.

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ariability in the delineation of head and neck organs at risk: Variation between guidelines and impact on dose and NTCP

Published as: Differences in delineation guidelines for head & neck cancer result in inconsistent reported dose and corresponding NTCP Brouwer CL, Steenbakkers RJ, Gort E, Kamphuis ME, van der Laan HP, Van’t Veld AA, Sijtsema NM, Langendijk JA. Radiother Oncol. 2014 Apr;111(1):148-52.

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Abstract Purpose To test the hypothesis that delineation of swallowing organs at risk (SWOARs) based on different guidelines results in differences in dose-volume parameters and subsequent normal tissue complication probability (NTCP) values for dysphagia-related endpoints.

Materials and Methods Nine different SWOARs were delineated according to five different delineation guidelines in 29 patients. Reference delineation was performed according to the guidelines and NTCPmodels of Christianen et al. Concordance Index (CI), dosimetric consequences, as well as differences in the subsequent NTCPs were calculated.

Results The median CI of the different delineation guidelines with the reference guidelines was 0.54 for the pharyngeal constrictor muscles, 0.56 for the laryngeal structures and 0.07 for the cricopharyngeal muscle and esophageal inlet muscle. The average difference in mean dose to the SWOARs between the guidelines with the largest difference (maxΔD) was 3.5 ± 3.2 Gy. A mean ΔNTCP of 2.3 ± 2.7% was found. For two patients, ΔNTCP exceeded 10%.

Conclusions The majority of the patients showed little differences in NTCPs between the different delineation guidelines. However, large NTCP differences >10% were found in 7% of the patients. For correct use of NTCP models in individual patients, uniform delineation guidelines are of great importance.

18 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

Introduction In head and neck radiotherapy, reducing the dose to healthy tissues is important, since radiation damage to organs at risk (OARs) may result in severe complications during and after completion of treatment. Some radiation-induced complications, in particular swallowing dysfunction, have a significant impact on health-related quality of life as reported by patients [1, 2].

Several guidelines for OAR delineation have been published [3–11]. However, the definition, selection and delineation of OARs vary widely among the different publications and authors.

This may lead to unjustified comparisons between institutes that apply different guidelines, jeopardizing the translation of results published into routine clinical practice.

Studies on the development of normal tissue complication probability (NTCP) models have identified numerous predictive factors for the development of radiation-induced dysphagia, such as the radiation dose to anatomical structures involved in swallowing dysfunction (e.g. the superior pharyngeal constrictor muscle)[12]. NTCP models can be used to estimate the risk of a given complication. Moreover, the most important dose volume parameters included in these NTCP-models can be used for treatment plan optimization, and thus to compare different radiation treatment plans in order to select the most optimal treatment.

Radiation doses to specific swallowing organs at risk (SWOARs) are main parameters for the calculation of NTCPs of dysphagia. NTCPs directly result from specific dose parameters of the SWOARs. However, if the delineation of SWOARs markedly differs from the guidelines used for NTCP-model development, the translation of the results of such models into routine clinical practice may be incorrect.

Recently, Christianen et al.[12] published delineation guidelines for SWOARs in head and neck radiotherapy that differ at some points from the definitions of SWOARs and sub­sequent delineation guidelines used by other investigators [4–11]. So far, the magnitude of these differences is still unclear, and the possible clinical relevance regarding differences in corres­ponding NTCPs remains to be determined.

Therefore, the main objective of the present study was to test the hypothesis that SWOAR delineations based on different delineation guidelines lead to differences in dose-volume parameters and subsequent NTCPs for dysphagia.

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Materials and Methods Delineation guidelines and patients For the purpose of the present study, the guidelines as proposed by Christianen et al. were used as a reference [3]. We decided to use this publication as a reference as it was the only one dedicated to the description of SWOARs delineation guidelines and because these guidelines were actually used in a subsequent publication that reported on the development of multivariate NTCP-models for different endpoints related to dysphagia [12]. This publication also included an overview of eight other guidelines for delineation of SWOARs that were published between 2000 and 2010 [4–11]. The following SWOARs were included in this overview: the pharyngeal constrictor muscles (PCMs), cricopharyngeal muscle and ‘esophageal inlet muscle’ (EIM) (which was previously described as ‘1 cm of the muscular compartment of the esophageal inlet’ (10) and ‘upper esophageal sphincter’ (9)) and the glottic and supraglottic larynx. For the purpose of the current study, we extracted the definitions from the original papers and defined different delineation groups (DGs) by clustering the structures into groups with corresponding definitions (Table 1). These groups with corresponding definitions will be referred to as ‘DG1’, ‘DG2’, etc..

The information in Table 1 was confined to the definitions of the cranial and caudal borders of the SWOARs, since the definitions of these borders showed the largest variation.

A detailed description of all remaining borders can be found elsewhere [3].

SWOARs were delineated in Pinnacle3 v9.0 (Philips, Madison) in 29 sample patients from our clinic according to the different guidelines of the DGs, resulting in a total number of 899 contoured SWOARs. Contouring was performed by one observer (EG) and checked by two others (MK and RS). The contours according to all DGs of the SWOARs which are input to the studied NTCP-models [12] are shown in Fig. 1.

Patients were randomly selected from our previous cohort [12]. The set comprised 6 l­aryn­ geal, 4 hypopharyngeal, 1 oral cavity, 15 oropharyngeal and 3 nasopharyngeal patients [13]. Planning computed tomography (CT)-scans were acquired in supine position with a 2 mm slice thickness.

20 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

TABLE 1 Overview of delineation guidelines for swallowing organs at risk (SWOARs) and the clustering into delineation groups (DGs) with corresponding definitions. The number of different definitions and therefore the number of DGs varies per structure. SWOARs used as input parameters of NTCP models [12] are marked by *.

SWOAR Delineation groups (DGs) of corresponding definitions Superior PCM*

Middle PCM*

DG1

DG2

DG3

DG4

Christianen

Dirix, Caglar, Caudell, Feng

Levendag

Bhide

Cranial: caudal tips of pterygoid plates

Cranial: caudal tips of pterygoid plates

Cranial: mid C2

Cranial: base of the skull

Caudal: lower edge of C2

Caudal: superior end of hyoid bone

Caudal: upper C3

Caudal: upper edge of hyoid bone

Christianen

Bhide, Dirix, Caglar, Caudell, Feng,

Levendag

DG5

2

Cranial: upper edge Cranial: upper edge Cranial: upper C3 of hyoid bone of C3

Inferior PCM

Caudal: lower edge of hyoid bone

Caudal: lower edge of hyoid bone

Christianen

Bhide, Dirix, Caglar, Caudell

Cranial: first slice caudal to the lower edge of hyoid bone

Cranial: lower edge of hyoid bone

Caudal: lower edge of arythenoid cartilages

Caudal: lower edge of cricoid cartilage

Caudal: lower edge of hyoid bone

Jensen

Dirix, Cagar, Caudell, Feng, Li

Levendag

Bhide

Cranial: caudal tips of pterygoid plates

Cranial: caudal tips of pterygoid plates

Cranial: mid C2

Cranial: base of the Cranial: lower part of skull transverse process of C2

Caudal: lower edge of arythenoid cartilages

Caudal: lower edge of cricoid cartilage

Caudal: lower edge of cricoid cartilage

Caudal: lower edge Caudal: top of of cricoid cartilage cricoid cartilage

Total PCM Christianen

21

SWOAR Supraglottic larynx*

Glottic larynx

Larynx

Cricopha­ ryngeus

Delineation groups (DGs) of corresponding definitions DG1

DG2

DG3

DG4

Christianen

Dirix, Jensen

Cranial: tip of epiglottis

Cranial: top of piriform sinus and aryepiglottic fold

Caudal: first slice cranial to upper edge of arytenoid cartilages

Caudal: upper edge of cricoid cartilage

Christianen

Dirix, Jensen

Cranial: upper edge of arythenoid cartilages

Cranial: upper edge of cricoid cartilage

Caudal: lower edge of cricoid cartilage

Caudal: lower edge of cricoid cartilage

Christianen

Dirix, Jensen

Caglar

Caudell

Cranial: tip of epiglottis

Cranial: top of piriform sinus and aryepiglottic fold

Cranial: upper edge Cranial: epiglottis of thyroid cartilage

Caudal: lower edge of cricoid cartilage

Caudal: lower edge of cricoid cartilage

Caudal: upper edge Caudal: vocal cords of cricoid

Christianen

Li

Levendag

Cranial: first slice caudal to arytenoid cartilages

Cranial: caudal cricoid

Cranial: caudal edge cricoid

Caudal: first Caudal: lower edge tracheal ring, 1 cm of cricoid cartilages caudal from cranial border Cricopha­ Christianen ryngeus & EIM Cranial: first slice caudal to arytenoid cartilages Caudal: 1 cm caudal to the superior border

Caudal: lower border First trachea ring

Dirix

Levendag

Cranial: lower edge cricoid cartilage

Cranial: caudal edge cricoid

Caudal: upper edge Caudal: 1 cm caudal from lower of trachea border first trachea ring

PCM= pharyngeal constrictor muscles, EIM= esophageal inlet muscle

22 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

DG5

Geometric comparison Geometric differences between the DGs were expressed as the Concordance Index (CI) of the different DGs with the reference DG (DG1). The CI provides information on volume as well as on positional differences [14]. The CI is the ratio of the intersection (Volume1∩Volume2) and union (Volume1UVolume2) volume of two delineated volumes. A CI of 1.00 indicates perfect overlap (identical structures), whereas a CI of 0.00 indicates no overlap at all.

Dosimetric comparison Standard clinically acceptable photon intensity modulated radiation therapy (IMRT) treatment plans were available for all patients. Plans were reviewed and/or replanned by a single experienced dosimetrist (HPL) for the purpose of plan consistency. When replanning (plan adjustment) was performed, this was done to make sure that: (1) Coverage of the planning target volumes (PTV) was adequate (exactly 98% of the PTV should receive 95% of the prescribed dose); (2) The mean dose in the parotid glands was as low as possible; (3) The dose outside the PTV was reduced as much as possible (optimized dose conformity).

No efforts were taken to specifically reduce the dose to the SWOARs [13]. Thus, the IMRT treatment plans were not influenced by the SWOARs delineations.

We studied the differences in mean doses in the SWOARs between the different DGs. For each patient the two DGs that resulted in the largest difference in mean dose (maxΔD) for a particular SWOAR were selected. MaxΔD was averaged over all patients to obtain an average maxΔD per SWOAR. Estimates of the variability in this study are always reported as ±1 standard deviation (SD).

NTCP comparison NTCPs were estimated for DG1 and DG2, in order to translate the differences in dose to differences in NTCPs. This will demonstrate the deviation from the model (ΔNTCP) in the situation of a clinical practice in which the contouring guidelines of DG2 are achieved, while the NTCP model belonging to DG1 is adopted. The analysis was confined to DG2 since it contained the most complete set of SWOARs’ description in relation to DG1. Differences in the NTCPs between DG1 and DG2 (ΔNTCP) were calculated for each patient, based on four equations published by Christianen et al. [12]. The NTCP-models contained the endpoints: • swallowing dysfunction grade 2-4 at 6 months after completion of radiotherapy, according to the RTOG Late Radiation Morbidity Scoring Criteria (1) • patient-rated moderate-to-severe problems with swallowing solid (2), soft (3) and liquid (4) food Table 2 lists the various parameters in the four different NTCP models. Radiation technique was IMRT for all patients in this study.

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2

NTCP calculations were based on a standard model of El Naqa [22]. Its mathematical definition was described in (1). The s-value differed for each model and its value was a summation of the dose and weights for the OARs for that specific model. The resulting NTCP gave the chance on complications for the OARS as a value between 0 and 1.

NTCP =

1 (1) (1+e)-s

In which n

S = β0 + ∑ i = 1βi ∙ Xi The S-values of the different models are given in Table 2. The age-factor was 0 for patients of age 18-65 and 1 for patients of an age above 65. In terms of radiation technique a 0 was for 3D-CRT and a 1 for IMRT. The tumour site value was 1 when it was primary located at the oropharynx or the nasopharynx, otherwise it was 0.

TABLE 2 Overview of S-values for physician- and patient-rated swallowing dysfunction according to institutional prediction models [12] .

Swallowing dysfunction

s-value

RTOG grade 2-4

s = -6.09 + (mean dose superior PCM×0.057) + (mean dose supraglottic larynx×0.037)

Liquid food

s = -5.98 + mean dose supraglottic larynx×0.074+ (radiation technique×-1.209)

Solid food

s = -6.89 + mean dose superior PCM×0.049+ (mean dose supraglottic larynx×0.048) + (age×0.795)

Soft food

s= -5.83 + mean dose middle PCM×0.061+age×1.203+ tumour site× 1.122 +(radiation technique×-0.912)

Choking

s=-7.07 + V60 EIM×0.020+ (mean dose supraglottic larynx×0.066)

24 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

FIGURE 1

2

Swallowing organs at risk (SWOARs) according to the different delineation groups (DGs) in one patient. Sagittal view. PCM = pharyngeal constrictor muscle.

Results Geometric Comparison A statistically significant difference in SWOAR volume was observed between the different DGs (p < 0.05, two-way ANOVA, Table 3). Fig. 2 illustrates the CI of the different DGs reference to DG1 for each SWOAR. The average median CI value was 0.54 for the PCMs, 0.56 for the laryngeal structures and 0.07 for the cricopharyngeal muscle and EIM. For the cricopharyngeal muscle no overlap at all with DG1 was seen (CI = 0). CIs of a certain DG reference to DG1 varied between patients due to different anatomy and/or different flexion of the neck.

25

FIGURE 2

Boxplot of the Concordance Index (CI) of the DGs in relation to DG1 in all 29 patients. No data for the cricopharyngeal muscle as a separate structure is presented since the CI with DG1 was 0 for all patients. sup= superior, med= middle, inf= inferior and total= total structure.

Boxplot parameters: The bottom and top of the box are the 25th and 75th percentile (the lower and upper quartiles, respectively) of the data. The error bars represent the minimum and maximum values of the data.

Dosimetric comparison Differences in SWOAR mean dose between the DGs showed moderate to large variations (Fig. 5). Largest maxΔD was found for patient 11, for which the difference in mean dose to the PCM superior between DG1 and DG3 was 19.1 Gy. The average maxΔD of all SWOARs was 3.5 ± 3.2 Gy with the largest differences observed for the total PCM (6.0 ± 3.4 Gy), while differences for the glottic larynx (0.8 ± 0.9 Gy) remained limited.

26 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

NTCP comparison Fig. 3 depicts ΔNTCP between DG1 and DG2 for the four NTCP-models studied. The mean absolute ΔNTCP over all patients and complications was 2.3 ± 2.7%. Differences were related to patient’s anatomy, posture and primary tumour site. Patients with tumours located in the oropharynx or nasopharynx showed higher NTCPs for the DG1-based SWOARs, while for patients with tumours located in the larynx and hypopharynx, the DG2-based SWOARs showed the highest NTCPs (grey vs. white bars in Fig. 3, respectively). This is mainly due to the larger overlap between the planning target volume (PTV) and the DG1-based SWOARs with respect to the DG2-based SWOARs for oropharynx/nasopharynx patients, and vice versa for larynx/hypopharynx patients. For two patients, the absolute ΔNTCP for at least one of the endpoints was larger than 10%. For patient 12 (primary tumour location in oropharynx), the mean dose to the supraglottic larynx according to DG1 was 70.5 Gy and for DG2 57.7 Gy (Fig. 4). The resulting ΔNTCP for RTOG grade 2-4 swallowing dysfunction was 11.6% (61.6% vs. 50.0%). For problems with swallowing solid food, ΔNTCP was 14.5% (47.3% vs. 32.8%). For the other patient (primary tumour located in oropharynx), ΔNTCP was 10.9% (35.0% vs. 24.1%) for the endpoint swallowing soft food (Fig. 3).

FIGURE 3

Difference in normal tissue complication probabilities (ΔNTCP) between delineation group (DG) 1 and 2 for different complications [12]. ΔNTCP>0 means underestimation and ΔNTCP<0 means overestimation of the NTCP using DG2 in relation to DG1. Tumour location is indicated by grey/ white filling of the bars.

27

2

FIGURE 4

Dose distribution (sagittal view) and dose-volume histogram of the supraglottic larynx for a patient showing large differences between DG (delineation group) 1 and 2.

Discussion This is the first study on the effect of variation in delineation guidelines on dose and subsequent NTCPs. We showed that dose parameters and corresponding NTCPs may vary widely depending on the definitions of the SWOARs. For the set of head and neck SWOARs included in the present study, the average maximal dose difference (maxΔD) was 3.5 ± 3.2 Gy. The translation of the dose variation to variation in NTCP for DG1 vs. DG2 resulted in a mean absolute ΔNTCP of 2.3 ± 2.7% (average over all patients and all four NTCP models studied). On average this seems a moderate difference, but it should be stressed that in individual cases ΔNTCP was much larger (>10%), which may lead to incorrect NTCPpredictions and possibly unjustified clinical decisions.

The magnitude of deviations from the reference volumes, dose, and subsequent NTCPs depended on patient’s anatomy and posture, as well as on primary tumour site. The impact of the variation in patient anatomy was illustrated well in the box plots of the CI of Fig. 2 (large interquartile distances). This spread of CI values may be explained by the fact that for some patient anatomies and postures, the demarcations (e.g. certain bone and muscle structures) of different DGs may be more separated than for other cases. For example, patients with primary tumour sites located in the oropharynx or nasopharynx showed relatively large differences in NTCPs due to dose variation in the supraglottic larynx, while these differences were much smaller for laryngeal and hypopharyngeal cancers. According to DG1, the supraglottic larynx extends to the tip of the epiglottis, while according to DG2

28 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

the cranial border ends at the upper extension of the piriform sinus and aryepiglottic fold.

Therefore, the overlap of the supraglottic larynx with the PTV in oropharyngeal cancer will generally be larger when using DG1 compared to DG2, resulting in higher dose values for DG1 compared to DG2 (Fig. 4). Therefore, the NTCP for patient-rated moderate-to-severe problems with solid, soft and liquid food (for which the model includes the mean dose to the supraglottic larynx) was smaller for DG2 compared to DG1 (Fig. 3). For patient-rated moderate-to-severe problems with swallowing soft food, applying DG2 for contouring the middle PCM also resulted in underestimation of the NTCPs for patients with primary tumours located in the oropharynx in relation to DG1 due to less overlap of the PTV with the SWOAR using DG2.

The large differences in NTCPs in some individual patients emphasize the importance of uniform delineation guidelines. We propose to develop general consensus guidelines, which should be simple and unambiguously described. Probably, current delineation guidelines differ most because of different interpretation of anatomy, and different choices for (derived) structure borders. However, before we will be able to define a pragmatic set of simple delineation guidelines, we believe it is important to study dose-response relationships for swallowing problems more extensively and to understand the physiology of side effects, to be able to include the best predictive parameters in NTCP models. The (superior) pharyngeal constrictor muscles [15, 16] and the supraglottic larynx [15] were, similar to our own research [12], recently associated with late radiation induced dysphagia.

Besides, De Ruyck et al. found that the rs3213245 (XRCC1) polymorphism was associated with radiation induced dysphagia [16]. Integrating biological and genetic (polymorphisms) information is promising to improve and individualize NTCP models.

Consensus meetings, multi-modality imaging, and the use of auto delineation tools could facilitate the introduction of uniform delineation guidelines [17, 18]. The findings of this study may also have implications for the design of clinical trials, especially when radiationinduced dysphagia is a primary or secondary endpoint. In these cases (automated) review of delineations is recommended. Although there still may be differences resulting from interobserver variability, the concordance of head and neck OAR delineations within a guideline appears to be better than those between guidelines (results of this study) [19].

Feng and colleagues [20] reported on the effect of contouring variability and the resulting impact on IMRT treatment plan optimization in oropharyngeal cancer. A contouring variability up to 1.4 cm led to a 0.9 Gy mean difference between optimizations. We can, however, not compare the results of that study with our results, since these investigators studied variation in delineation of repeated delineations by a group of experts, while the current study focussed on inter-guideline variation. Moreover, these investigators studied dose differences between optimizations (thus between different treatment plans) on different contours, while we studied the effect of using different guidelines for NTCP estimation within one treatment plan.

29

2

From a scientific point of view, it is important to externally validate NTCP-models developed in specific institutions, before they can be used in routine clinical practice. The results of the present study clearly illustrate that this external validation may be hampered by inconsistencies in delineation guidelines. This is particularly true for SWOARs with large dose variation and for NTCP-models for which the results are more sensitive to differences in contouring. Previous work has shown that the way we measure dysphagia (physicianrated, patient-reported, or objective measurements) is also of main importance for consistent NTCP modelling [21]. Therefore, clear definitions of organs at risk and endpoints are required to improve the external validity of NTCP-models.

The present study showed the consequences of not applying the matching input data to NTCP-models. In theory, all delineation guidelines would fit their own NTCP models. In practice however, multiple model versions should be constructed and validated, and this would also rule out pooling of dose-volume and follow-up data into large data sets to build a proper NTCP-model. We would therefore strongly advocate the use of uniform guidelines for NTCP-modelling studies as well as for studies on external validation and routine clinical practice.

In the current study, mean dose and corresponding NTCP differences between DGs were compared using IMRT plans that were not optimized based on the dose to the SWOARs, but particularly on the dose to the parotid glands. Therefore, the question arises what happens with the dose differences if the IMRT plans would be optimized for the different DGs. We expect the dose differences between the DGs to be similar or even larger when optimization on SWOARs would be performed, since dose gradients would be located closer to the SWOARs, resulting in larger dose differences between the different DGs. SWOAR optimization for different DGs was performed in two of our study patients, and results confirmed our presumption (see Appendix for a case example).

30 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

FIGURE 5

2

Mean dose to the swallowing organs at risk according to the different delineation groups (DGs), sorted on largest difference (left-right). PCM= pharyngeal constrictor muscle, CP= cricopharyngeal muscle, EIM = esophageal inlet muscle.

31

TABLE 3 Volume of swallowing organs at risk (SWOARs) for the different delineation groups (DGs).

PCM = pharyngeal constrictor muscles. EIM = esophageal inlet muscle.

SWOAR

Volume (cc)* DG1

DG2

DG3

DG4

DG5

p-value**

Total PCM

20.8 (4.9)

29.9 (6.9)

22.4 (6.2)

26.7 (7.2)

9.8 (3.6)

0.000

Superior PCM

7.6 (1.8)

12.5 (3.6)

4.5 (1.4)

9.9 (3.8)

-

0.000

Middle PCM

4.3 (2.2)

2.6 (0.9)

5.3 (2.8)

-

0.000

0.000

Inferior PCM

4.8 (1.3)

10.1 (2.8)

-

-

-

0.000

Larynx

21.1 (6.2)

9.0 (4.0)

18.7 (5.5)

16.2 (5.9)

-

0.000

Supraglottic Larynx

10.9 (3.5)

9.2 (4.1)

-

-

-

0.041

Glottic Larynx

7.4 (2.9)

5.1 (1.8)

-

-

-

0.000

Cricopharyngeus

3.7 (1.4)

1.8 (0.6)

0.8 (0.3)

-

-

0.000

Cricopharyngeus & EIM

5.9 (1.9)

0.6 (0.4)

3.0 (0.9)

-

-

0.000

* Variables are denoted as mean (standard deviation). ** Group differences were tested with Friedmanâ&#x20AC;&#x2122;s ANOVA.

Conclusion The majority of the patients showed little differences in NTCPs for different delineation guidelines. However, large NTCP differences >10% were found in 7% of the patients. For correct use of NTCP models in individual patients uniform delineation guidelines are of great importance. â&#x20AC;&#x192;

32 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

Appendix Standard versus Swallowing sparing IMRT plans This section shows that in standard as well as in swallowing sparing IMRT (when plans are optimized by reducing the dose to the SWOARs as well), deviations in NTCP-values remain if delineations of DG2 are used instead of DG1 (NTCP-model based on DG1).

In this example, we calculated the NTCP-value for grade II-IV dysphagia based on the mean dose to the superior PCM and supraglottic area for two different delineations (DG1 and DG2) (Figure 2). In addition, the treatment plan was optimized by reducing the dose to the SWOARs, resulting in plan DG1-optimized and plan DG2-optimized. The NTCP-values for the different plans were then calculated again (Table S1).

Figure S2 shows dose volume histograms of the PCM superior for the standard and the DG2-optimized IMRT plan. For both the standard and optimized plan (solid and dashed lines, respectively), the dose to the PCM superior based on the DG2 delineation (in red) deviated from the dose according to the DG1 delineation (in green).

For the incorrect situation, the NTCP is calculated based on the dose according to the DG2 delineation (red lines in Figure S2). The corresponding NTCP-value for the DG2-optimized IMRT plan was 9.3%, while the correct NTCP-value for this dose distribution, based on the DG1 delineation (dashed green dose-volume line in Figure S2), should be 6.1% (Table S1).

This absolute difference of 3.3% in NTCP translates into a relative NTCP overestimation of 54%. Although in this case the absolute difference is relatively small, this example proves that optimized IMRT based on SWOARs do not automatically reduces the problem of underor overestimation of NTCP-values.

So for both standard and swallowing sparing IMRT plans, similar errors are made if incorrect NTCP model parameters are used.

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FIGURE S1

Delineations of the PCM superior ad supraglottic larynx (green= DG1, red= DG2) and PTV70 (purple).

FIGURE S2

Dose volume histograms (DVHs) of the PCM superior for two different IMRT plans (solid: standard IMRT, dashed: DG2-optimized IMRT) and the two different delineation guidelines (green: DG1, red: DG2). DG= delineation group.

34 CHAPTER 2 - Variation between guidelines and impact on dose and NTCP

TABLE S1 Dose and NTCP results for grade II-IV dysphagia, for three different treatment plans.

Standard IMRT Swallowing Sparing IMRT DG1-Optimized

DG2-Optimized

PCM superior MD for delineations of DG1 (Gy)

33,2

20,9

19,6

PCM superior MD for delineations of DG2 (Gy)

36,0

25,6

23,4

Supraglottic MD for delineations of DG1 (Gy)

62,1

60,8

60,4

Supraglottic MD for delineations of DG2 (Gy)

68,7

67,8

67,1

NTCP for delineations of DG1 (%)

13,0

6,6

6,1

NTCP for delineations of DG2 (%)

18,3

-

9,3

abs ΔNTCP(%)

5,3

-

-3,3

rel ΔNTCP(%)

40%

-

-54%

2

PCM= pharyngeal constrictor muscles, MD= mean dose, DG= delineation group.

Green and red lines correspond to the dose volume histograms of Figure S2.

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16. De Ruyck K, Duprez F, Werbrouck J, et al. A predictive model for dysphagia following IMRT for head and neck cancer: Introduction of the EMLasso technique. Radiother Oncol.

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18. Chao KSC, Bhide S, Chen H, et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys. 2007;68:1512–21.

19. Brouwer C, Steenbakkers R, van den Heuvel E, et al. 3D Variation in delineation of head and neck organs at risk. Radiat Oncol. 2012;7:32.

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20. Feng M, Demiroz C, Vineberg KA, et al. Normal tissue anatomy for oropharyngeal cancer: contouring variability and its impact on optimization. Int J Radiat Oncol Biol Phys.

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21. Eisbruch A, Kim HM, Feng FY, et al. Chemo-IMRT of oropharyngeal cancer aiming to reduce dysphagia: swallowing organs late complication probabilities and dosimetric correlates.

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22. El Naqa I, Bradley J, Blanco AI, et al. Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys. 2006;64:1275–86.

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CHAPTER

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ariability in the delineation of head and neck organs at risk: 3D variation in delineation within guidelines

Published as: 3D Variation in delineation of head and neck organs at risk Brouwer CL, Steenbakkers RJ, van den Heuvel E, Duppen JC, Navran A, Bijl HP, Chouvalova O, Burlage FR, Meertens H, Langendijk JA, van â&#x20AC;&#x2DC;t Veld AA. Radiat Oncol. 2012 Mar 13;7:32

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Abstract Background Consistent delineation of patient anatomy becomes increasingly important with the growing use of highly conformal and adaptive radiotherapy techniques. This study investigates the magnitude and 3D localization of interobserver variability of organs at risk (OARs) in the head and neck area with application of delineation guidelines, to establish measures to reduce current redundant variability in delineation practice.

Methods Interobserver variability among five experienced radiation oncologists was studied in a set of 12 head and neck patient CT scans for the spinal cord, parotid and submandibular glands, thyroid cartilage, and glottic larynx. For all OARs, three endpoints were calculated: the Intraclass Correlation Coefficient (ICC), the Concordance Index (CI) and a 3D measure of variation (3D SD).

Results All endpoints showed largest interobserver variability for the glottic larynx (ICC = 0.27, mean CI = 0.37 and 3D SD = 3.9 mm). Better agreement in delineations was observed for the other OARs (range, ICC = 0.32–0.83, mean CI = 0.64–0.71 and 3D SD = 0.9–2.6 mm).

Cranial, caudal, and medial regions of the OARs showed largest variations. All endpoints provided support for improvement of delineation practice.

Conclusions Variation in delineation is traced to several regional causes. Measures to reduce this variation can be: (1) guideline development, (2) joint delineation review sessions and (3) application of multimodality imaging. Improvement of delineation practice is needed to standardize patient treatments.

40 CHAPTER 3 - 3D variation in delineation within guidelines

Background Radiotherapy (RT) plays an important role in the treatment of head and neck cancer patients. Many new radiation delivery techniques such as intensity-modulated RT (IMRT) have been developed to allow improved dose conformation with steeper dose gradients compared with conventional three-dimensional conformal RT. Variation in contouring is an important obstacle in the development of high geometric accuracy in the clinical application of these new techniques. Reproducibility in delineation of tumour and normal structures is of importance for optimal patient treatment [1]. As new radiation delivery techniques are increasingly controlled by OAR constraints for normal tissue sparing [2], variations in OAR delineation may unintentionally influence the treatment plan including the dose to these OARs [3]. In a number of publications (e.g. Bortfeld and Jeraj [4]), uncertainties in the contouring of organs is also mentioned as one of the potential causes for uncertainties in historical dose and volume data and therefore reduced performance of predictive models.

Deasy et al. [5] furthermore mentioned that differences in segmentation procedure could be one of the reasons explaining variations between existing models.

Target volume delineation variability in the head and neck area has been investigated in several studies (e.g. Rasch et al. [6]), indicating the need to minimize observer variation for adequate irradiation. However, interobserver variability of OARs in the head and neck area has not been frequently studied. Nelms et al. [3] found significant organ-specific interclinician variation for head and neck OARs. These variations resulted in large differences in dose distribution parameters, especially in high dose gradient regions. The authors stated that the major variations were in each observerâ&#x20AC;&#x2122;s interpretation of the OARs actual size and shape, suggesting the need for basic training (with unambiguous guidelines) on identifying OARs. Our department uses well-defined delineation guidelines to promote the consistency and accuracy of delineation such as the recently described guidelines for the delineation of OARs related to salivary dysfunction and anatomical structures involved in swallowing [7,8]. Interobserver variation in the contouring of OARs is therefore intended to be minimal, but still there will be regions in the OARs which are difficult to interpret for the observer. Accurate determination of variation in OAR delineation expressed in volumetric, positional and local 3D measures is therefore needed to establish current accuracy status, to bring actual weaknesses to light and to establish measures to reduce current redundant variability in delineation practice. More consistency in the delineation of OARs may contribute to more consistent dose volume data, and thus less uncertainty in the usage of dose volume characteristics. With the unambiguous and consistent contouring of OARs we could furthermore generalize the application of normal tissue complication probability (NTCP) models. We might even be able to develop improved models, when more consistent dose-volume data is correlated with clinical outcome. This is particularly important for the rising application of particle therapy, in which the dose gradients are extremely steep.

The obtainable level in accuracy of delineations also provides valuable information for the

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evaluation of tools for automatic (re-)contouring. Qazi et al. [9], for instance, reported high accuracy for automatic segmentation within a clinically-acceptable segmentation time, but also mentioned the need for multi-observer studies to give more insight in the robustness, reliability, and stability of the automated approach. Existing variations in expert delineations could serve as benchmark data.

The aim of the current study was to indicate OAR regions with high interobserver variability in the head and neck area, to subsequently establish possible solutions for this variability in delineation practice.

Methods Patients The study population was composed of 6 head and neck cancer patients. These patients underwent a planning CT scan (CTplan) which was acquired prior to radiation, and a repeat CT scan (CTrep) which was acquired during the course of radiation. CTrep scans were performed 11 to 35 days (range) after the start of radiotherapy. The CT images were made with the patient in supine position on a multidetector-row spiral CT scanner (Somatom Sensation Open, 24 slice configuration; Siemens Medical Solutions, Erlangen, Germany). The acquisition parameters were: gantry un-angled, spiral mode, rotation time 0.5 s, 24 detector rows at 1.2 mm intervals, table speed 18.7 mm/rotation, reconstruction interval 2 mm at Kernel B30 and 120 kVp/195 mA. The matrix size was 512 × 512, with a pixel spacing of 0.97 × 0.97 × 2.0 mm in the x, y and z directions, respectively.

Five specialized head and neck radiation oncologists (R.S., A.N., H.B., O.C. and F.B.), all treating more than 50 head and neck patients per year, delineated five OARs on axial CT slices in all CT images. The radiation oncologist did not have clinical patient information additional to the CT scan. The OAR set included the spinal cord, the parotid and submandibular glands, the thyroid cartilage, and the glottic larynx. For one patient, the right parotid gland contained tumour infiltration and therefore the patient was excluded from analysis for this particular OAR beforehand. The total number of delineated structures was 410.

CTplan and CTrep were delineated under slightly different circumstances, since CTplan was made with contrast-enhancement (iodine containing contrast medium, intravenously applied) while CTrep was acquired without contrast enhancement. Furthermore, the CTplan scan was delineated from scratch and the CTrep scan was delineated using a template obtained from the delineated contours of the CTplan, which were propagated to CTrep after a rigid registration of CTrep to CTplan in each individual patient.

42 CHAPTER 3 - 3D variation in delineation within guidelines

Delineation guidelines The radiation oncologists were instructed to delineate the parotid and submandibular glands according to the delineation guidelines of van de Water et al. [7].

Following these guidelines the parotid gland was demarcated in lateral direction by a hypodense area corresponding to subcutaneous fat and more caudally by the platysma.

The medial border was defined by the posterior belly of the digastric muscle, the styloid process and the parapharyngeal space. The cranial aspect of the parotid gland was related to the external auditory canal and mastoid process. Caudally, the gland protruded into the posterior submandibular space inferior to the mandibular angle. The anterior border was defined by the masseter muscle, the posterior border of the mandibular bone and the medial and lateral part of the pterygoid muscle. The posterior border was delimited by the anterior belly of the sternocleidomastoid muscle and the lateral side of the posterior belly of the digastric muscle. The external carotid artery, the retromandibular vein and the extracranial facial nerve are prescribed to be enclosed in the parotid gland.

Cranial demarcation of the submandibular gland was defined by the medial pterygoid muscle and the mylohyoid muscle, the caudal demarcation by fatty tissue. The anterior border was the lateral surface of the mylohyoid muscle and the hyoglossus muscle, and the posterior border the parapharyngeal space and the sternocleidomastoid muscle. Lateral demarcation was described by the medial surface of the medial pterygoid muscle, the medial surface of the mandibular bone and the platysma. The medial border was finally described by the lateral surface of the mylohydoid muscle, the hyoglossus muscle, the superior and middle pharyngeal constrictor muscle and the anterior belly of the digastric muscle.

The spinal cord was delineated as the actual spinal cord instead of using bony structures as surrogate for the spinal cord, starting at the tip of the dens and ending at the level of the third thoracic vertebra. The thyroid cartilage was delineated as the actual thyroid cartilage.

The cranial border of the glottic larynx was defined as the arythenoid cartilages and the caudal border as the edge of the cricoid.

Statistical considerations For all observers, mean volumes and standard errors (SEs), as well as coefficients of variation (CVs) per OAR were calculated. In addition, an â&#x20AC;&#x2DC;OAR ratioâ&#x20AC;&#x2122; for each observer was determined, which was defined as the ratio of the mean OAR volume per observer divided by the mean volume of that OAR determined by all observers. Friedmanâ&#x20AC;&#x2122;s test was applied to the CTplan data per OAR separately to investigate a possible systematic effect in the determination of volumes by the observers.

We used different endpoints to investigate interobserver variability. Variations in volume were indicated by the Intraclass Correlation Coefficient (ICC), and differences in combined

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volume and positional variations by the Concordance Index (CI). Local variations in delineation were finally described by the regional 3D SD. Integration of these three endpoints could help us to identify the type of variation in delineation.

Intraclass correlation coefficient Analysis of variance (ANOVA) was conducted for estimation of the ICC per OAR. The ICC quantifies how well the observers defined the same size of volumes, without considering the position of the volume of one observer with respect to the other [10]. To assess the ICC for each OAR separately, a three-way mixed effect analysis of variance model was applied to the volume data. All possible interaction terms were included, with patients and observers as random effects and time as a fixed effect. The time effect describes the mean difference in volume during the treatment (CTplan vs. CTrep). The patient and time-patient interaction effects were considered sources of variation that are unrelated to observer variation.

Therefore, in line with Barnhart et al. [11], the ICC was calculated as the ratio of the sum of variance components for patient and time-patient interaction effects and the sum of all variance components. It represents the correlation coefficient of two arbitrary observers measuring the same patient at the same time (the same CT scan). We used a classification of the data as presented by Shrout et al. [12]. Values of 0.00–0.10 represent virtually no agreement (reliability); 0.11–0.40 slight agreement; 0.41–0.60 fair agreement; 0.61–0.80 moderate agreement; and 0.81–1.00 substantial agreement.

Concordance index Another endpoint for interobserver variability used in this study was the ratio of the intersection (Volume1∩Volume2) and union (Volume1 Volume2) volume of two delineated volumes. Terminology for this coefficient varies [13] but we adhered to the term concordance index (CI), as is also done in the overview of Hanna et al. [14] and in the review of Jameson et al. [15]. The CI is both sensitive to positional differences and differences in volume size between observers.

We calculated a mean CI value per OAR by averaging all individual CIs over all ten observer pairs and all twelve CT scans, and we determined the range of CIs. A CI of 1.00 indicates perfect overlap (identical structures), whereas a CI of 0.00 indicates no overlap at all.

Large discrepancies between the ICC and the CI indicate that observers are either more consistent in defining the volume size (ICC>>CI), or more consistent in positioning the volumes (CI>>ICC).

3D analysis of variation The 3D analysis of variation allows quantification of local variation in delineated structures in 3D [16]. For each OAR, a median contour surface of all 5 observer delineations was computed in 3D [16,17]. The local variation in the five distances to the median (SD) was

44 CHAPTER 3 - 3D variation in delineation within guidelines

determined for each surface point, and was averaged over all surface points of the OAR to obtain the global 3D SD. For further analysis, the OARs were divided into several anatomical sub regions. For the parotid glands, the upper and lower 5 slices were assigned as the cranial and caudal sub region, for the submandibular glands the upper and lower 3 slices were used. The anterior and posterior sub regions of the parotid gland were defined to be lateral to the mandibular bone. The spinal cord was defined in a cranial (up to C1), medial (C2 to T1) and caudal (from T2 on) sub region. The cranial and caudal sub regions of the glottic larynx were defined to be the upper and lower slice of the contour, respectively. The cranial sub region of the thyroid ended at the point where the median contour consisted of a closed structure in the transverse view. The caudal unclosed part of the contour was defined to be the caudal sub region. Figure 1 shows a 3D representation of the sub regions (a), together with transverse central slices of the OARs (b-f) to illustrate the anterior, posterior, medial and lateral sub regions. Calculation of the SD of a particular sub region resulted in a regional 3D SD. Note that these 3D SD results provided additional information to the ICC and CI, because these results quantify in which region of the OAR the highest variability in volume sizes and position was observed.

Results Descriptive statistics Table 1 and Figure 2 present an overview of the volume data. Variation between observers in individual patients as well as between patients was seen. Planning and repeat CT sets are depicted separately, but no general trend in the differences between both CT scans was observed. For each OAR a certain systematic observer effect in the determination of the volumes was seen. Observer 1 and 2 defined significantly smaller volumes for the parotid and submandibular glands than the other observers (Friedman Test, p < 0.005).

For the glottic larynx and spinal cord observer 1 seem to define the smallest volumes while observer 5 defines the largest volumes (Friedman Test, p < 0.006). These results were in line with the mean OAR ratios (Table 1).

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FIGURE 1

Division of the studied OARs in sub regions for studying the regional 3D variations in delineation. (a) Left side- (left), frontal (middle) and rear (right) 3D view of the studied organs at risk (OARs); the parotid and submandibular glands, spinal cord, thyroid cartilage and glottic larynx, divided in sub regions according to the colour legend. (b-f) Transverse central slices of the parotid gland (b), submandibular gland (c), glottic larynx (d), thyroid cartilage (e) and spinal cord (f) showing the division in sub regions according to the colour legend

46 CHAPTER 3 - 3D variation in delineation within guidelines

TABLE 1 Mean volume, coefficient of variance, and mean OAR ratio per observer and organ at risk

OAR

Mean volume

CV

[cm3] (SE)

Mean OAR ratio* obs. 1

obs. 2

obs. 3

obs. 4

obs. 5

Spinal cord

17.6 (1.1)

16%

0.98

0.98

0.91

0.92

1.17

Parotid gland left

28.4 (2.6)

15%

0.92

0.85

1.10

1.08

1.03

Parotid gland right

29.6 (3.9)

12%

0.88

0.90

1.06

1.04

1.02

Submandibular gland left

10.6 (0.9)

16%

1.06

0.86

1.12

1.06

0.95

Submandibular gland right

10.4 (0.9)

16%

1.09

0.88

1.06

1.03

0.90

Thyroid cartilage

11.4 (1.5)

14%

0.96

1.11

1.11

0.97

0.93

Glottic larynx

10.3 (2.8)

56%

0.45

0.60

1.13

1.38

1.66

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OAR = organ at risk, SE = standard error of the mean, CV = coefficient of variance, obs. = observer SE and CV were determined by the results of ANOVA, the CV represents variability due to observers *OAR ratio = the volume of the OAR as determined by each observer divided by the mean volume of all five observers

The CVs in Table 1, which indicate an observer relative standard deviation for observing a volume, clearly showed highest variability for the glottic larynx (56%), while the other CVs varied from 12 to 16%.

Intraclass correlation coefficient The ICCs (Table 2) indicated largest observer variation (lowest ICC) in the volumes of the glottic larynx (ICC = 0.27) and the spinal cord (ICC = 0.32). Both OARs were classified to have slight agreement for delineation of volume sizes. The submandibular glands showed fair to moderate agreement (ICC = 0.60 and 0.61) for consistent volume delineation while the parotid glands (ICC = 0.65 and 0.86) and the thyroid cartilage (ICC = 0.83) showed moderate to substantial agreement.

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FIGURE 2

Variation in the definition of organ at risk volume. Volumes of the organs at risk according to observer 1(○), 2(□), 3(×), 4(∆) and 5 (+). CTplan is planning CT and CTrep is repeat CT scan. The right parotid gland data contains 5 patients instead of 6 because of the exclusion of one gland (of patient 4) due to tumour infiltration

48 CHAPTER 3 - 3D variation in delineation within guidelines

TABLE 2 Interobserver variability of the organs at risk described by 3 different endpoints

OAR

ICC

CI (min-max)

3D SD (mm) Global

Region cranial

caudal

medial lateral anterior posterior

Spinal cord

0.32

0.64 (0.41–0.83)

1.5

2.1

2.4

0.9

-

-

-

Parotid gland left

0.65

0.69 (0.43–0.83)

2.6

3.3

2.8

2.8

1.2

2.0

1.9

Parotid gland right

0.86

0.71 (0.50–0.86)

2.0

2.8

2.4

2.4

0.9

1.5

1.5

Submandibular gland left

0.61

0.70 (0.46–0.85)

1.8

2.9

0.7

1.7

1.7

1.6

1.4

Submandibular gland right

0.60

0.71 (0.36–0.83)

1.5

2.7

0.8

1.4

1.1

1.4

1.3

Thyroid cartilage

0.83

0.66 (0.30–0.80)

0.9

1.0

0.9

1.0

0.8

-

-

Glottic larynx

0.27

0.37 (0.11–0.81)

3.9

3.7

5.0

5.0

2.4

-

4.3

3

OAR = organ at risk, ICC = intraclass correlation coefficient, CI = concordance index and SD = standard deviation

Concordance index The mean CI values varied from 0.64 to 0.71, except for the glottic larynx for which the mean CI was 0.37 (Table 2). A large range in the CI of different observer pairs was seen (min-max, 0.11–0.86, Table 2).

3D analysis of variation Largest interobserver variability in the 3D SD evaluation was found for the glottic larynx (global 3D SD of 3.9 mm, Table 2). Regional 3D SD values were up to 5.0 mm for the caudal and medial part of the glottic larynx. Figure 3(d,e,f) illustrates the variations in delineation of the glottic larynx for a typical patient CT. Best observer agreement was found for the thyroid cartilage (global 3D SD of 0.9 mm, Table 2). For all OARs, the regional 3D SD analysis showed largest variations in the cranial regions.

Furthermore, medial regions tended to show more variation than lateral regions.

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FIGURE 3

Delineation variation of a parotid gland and a glottic larynx. Left parotid gland and glottic larynx delineations in a typical cranial (a and d), central (b and e) and caudal (c and f) transverse CT slice. Each colour corresponds to one observer

Figure 4 illustrates the 3D regional variations of a typical patient for the spinal cord, and for a parotid and a submandibular gland. The predominating cranial, caudal and medial parotid gland variations can also be seen in Figure 3(a,b,c).

50 CHAPTER 3 - 3D variation in delineation within guidelines

FIGURE 4

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3D delineation variation of a spinal cord, a right parotid and submandibular gland. 3D Standard Deviations (SDs) for a typical patient plotted in colour scale on the median contour surface of the organ. Note the different scalings. Spinal cord (a), right parotid gland (b) and right submandibular gland (c), frontal view

Discussion This study included an extensive 3D analysis of variation in delineation of a set of OARs in the head and neck area. All OARs, except from the glottic larynx, showed moderate interobserver variability with ICC values of 0.32–0.83, CI values of 0.64–0.71, and 3D SD values of 0.9–2.6 mm. Cranial, caudal, and medial regions of the OARs showed largest variations. The glottic larynx showed larger variation in delineation (ICC = 0.27, mean CI = 0.37 and 3D SD = 3.9 mm). All endpoints provided support for improvement of delineation practice.

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The inaccurate results for consistency in delineation of the glottic larynx were mainly caused by poor compliance to the delineation guidelines. The guidelines prescribe the glottic larynx to end at the caudal edge of the cricoid, and to include the arythenoid cartilages in the glottic larynx contour. As illustrated in Figure 3 (d,e,f), this description was not consistently followed. Reduction of the interobserver variability might be accomplished by joint delineation review sessions in which all radiation oncologists who are involved in head and neck cancer participate. These sessions are nowadays current practice at our institutes (UMCG, NCI-AVL).

The salivary glands showed moderate interobserver variability. Visual inspection of the parotid gland contours showed that the guidelines for these organs were also not consistently followed. The protocol prescribed that superficial temporal vessels should be enclosed in the delineated parotid gland, because they are generally hard to distinguish from the parotid gland tissue on scans with no or poor contrast. Still, some observers did not include the vessels in their delineation of the parotid gland. Joint delineation review sessions and enlightenment of the guidelines could help here. Yi and colleagues [18] for instance showed that clear stepwise delineation guidelines resulted in minimal variability.

Our volume analysis of the parotid gland data showed similar CV values (12 and 15%) as data of Geets et al. [19] (17%). Nelms et al. [3] found larger CV values (34% and 29%), evaluated for 1 patient by 32 observers. Our 3D SD evaluation reflects valuable information on specific regional variations. Largest discrepancies for the parotid glands were located at the cranial, caudal and medial sub regions of the gland. For the submandibular glands, the cranial parts of the organ clearly showed largest discrepancies. Poor discrimination between tissues at the medial borders of the parotid gland (e.g. distinction from the posterior belly of the digastric muscle) and the cranial parts of the submandibular gland (e.g. distinction from the medial pterygoid muscle and the mylohyoid muscle) could be a reason for these larger 3D SD values. The addition of MRI might improve the visibility of borders between tissues [19,20]. The cranial and caudal variations for the parotid glands could partly be explained by the image resolution in the cranial-caudal direction of 2 mm (the slice thickness) and the fact that observers could only delineate on transverse CT slices, which limits the resolution in both the cranial and caudal part of the delineations. These limitations do, obviously, apply to the delineations of all OARs. Possibly the availability of delineation on multiple orientations could help to diminish these variations, as is also suggested by Steenbakkers et al. [17]. The use of a standardized delineation environment and tools for automatic contouring might further contribute to reduce interobserver variability [17,21,22].

Interobserver variability of the spinal cord was predominantly caused by variations at the cranial and caudal part of the structure, due to indistinctness of the guidelines and low compliance. This problem could be reduced by clearer delineation guidelines although it is unlikely that these variations will have major consequences in clinical practice as long as the spinal cord is accurately delineated in the vicinity of the irradiated volume and the maximum dose to the cord is considered as the leading parameter for treatment planning.

52 CHAPTER 3 - 3D variation in delineation within guidelines

We analysed the interobserver variability in contouring on twelve CT sets, which consisted of six CTplan scans and six CTrep scans. The three endpoints of interobserver variability did not indicate a trend in the differences between CTplan and CTrep (for example see Figure 2). The correspondence of interobserver variability between the scans may suggest that the use of contrast (in CTplan) and the use of a(n) (observer specific) delineation template (in CTrep) have effects of comparable magnitude on the variation in delineation amongst observers, for the considered OARs. Besides, the guidelines are developed to be applicable to non-contrast as well as to contrast enhanced CT data, which will minimize possible variation in delineation due to (lack of) contrast. According to our experience the addition of contrast in delineating OARs is limited, because the uptake of contrast by the selected OARs is deniable.

We used different endpoints to quantify interobserver variability in head and neck OAR delineation. Variations in volume were indicated by the ICC and differences in combined volume and positional variations by the CI. Local variation in delineation was finally described by the regional 3D SD. The results showed that the variation in the determination of the volume alone (ICC) can be rather large while the combined volume and positional variations (CI) did not point to such a large variability (e.g., the spinal cord showed ICC = 0.32, CI = 0.63). This implies that the variations are situated at the borders of the OAR rather than in positional mismatches of the centres of gravity. In another case the ICC indicated substantial agreement while the CI was relatively moderate (e.g., the thyroid cartilage showed ICC = 0.83 and CI = 0.66), which could indicate a substantial consistency in defining volume size while the centre of gravity of the volumes are shifted in position with respect to each other. So information of the ICC combined with the CI could help to identify the type of interobserver variation (in volume and position). To study variations between delineations in detail, the 3D SD provides most complete information.

Some of the endpoints to describe interobserver variability as used in the current study have also been applied in studies dealing with head and neck target volume interobserver variability. Geets et al. [19] found CVs of 4% and 20% for oropharyngeal and laryngealhypolaryngeal GTVs, which are more or less similar to the CVs we found for OARs (2â&#x20AC;&#x201C;16%), excluding the glottic larynx (56%). Rasch et al. [20] described 3D SD variability for head and neck target volume delineation in the same range as our OAR results; 3.3â&#x20AC;&#x201C;4.4 mm for the CTV and 4.9â&#x20AC;&#x201C;5.9 mm for the elective nodal areas, while our global 3D SD results varied from 0.9 to 3.9 mm. Our results thus strengthen earlier findings (e.g. of Nelms et al. [3]) that interobserver variability is not only an important issue in the delineation of target volumes but also plays a role in the delineation of OARs.

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Conclusion Cranial, caudal, and medial regions of the studied head and neck organs at risk showed largest interobserver variability, due to indistinctness of the delineation guidelines, the larger image resolution in the cranial-caudal direction, the limitation of delineation on transverse slices, and poor discrimination in contrast from adjacent tissues. Potential measures to reduce current redundant variability in delineation practice are: (1) guideline development, (2) joint delineation review sessions, and (3) application of multimodality imaging. Other aspects that could contribute to more consistency in delineation are a standardized delineation environment with standard delineation tools, the possibility to delineate on multiple orientations and automatic contouring tools. The latter should however carefully be validated using base line data of contouring variability such as the results of this study. Minor interobserver variability could ultimately benefit radiation oncology practice since it may contribute to more general applicability and improvement of TCP and NTCP models.

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18. Yi SK, Hall WH, Mathai M, et al. Validating the RTOG-endorsed brachial plexus contouring atlas: an evaluation of reproducibility among patients treated by intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2012;82:1060-1064.

19. Geets X, Daisne J, Arcangeli S, et al. Inter-observer variability in the delineation of pharyngolaryngeal tumor, parotid glands and cervical spinal cord: Comparison between CT-scan and MRI. Radiother Oncol. 2005;77:25–31.

20. Rasch CR, Steenbakkers RJ, Fitton I, et al. Decreased 3D observer variation with matched CT-MRI, for target delineation in Nasopharynx cancer. Radiat Oncol. 2010;5:21.

21. Chao KSC, Bhide S, Chen H, et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys. 2007;68:1512–1521.

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22. Teguh D, Levendag P, Voet P, et al. Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (Swallowing/Mastication) structures in the head and neck. Int J Radiat Oncol Biol Phys. 2011;81:950â&#x20AC;&#x201C;957. â&#x20AC;&#x192;

56 CHAPTER 3 - 3D variation in delineation within guidelines

CHAPTER

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nternational consensus delineation guidelines for head and neck organs at risk

Published as: CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines Brouwer CL, Steenbakkers RJ, Bourhis J, Budach W, Grau C, Grégoire V, van Herk M, Lee A, Maingon P, Nutting C, O’Sullivan B, Porceddu SV, Rosenthal DI, Sijtsema NM, Langendijk JA. Radiother Oncol. 2015 Oct;117(1):83-90 Online Supplemental Materials : http://dx.doi.org/10.1016/j.radonc.2015.07. 041

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Abstract Purpose The objective of this project was to define consensus guidelines for delineating organs at risk (OARs) for head and neck radiotherapy for routine daily practice and for research purposes.

Methods Consensus guidelines were formulated based on in-depth discussions of a panel of European, North American, Asian and Australian radiation oncologists.

Results Twenty-five OARs in the head and neck region were defined with a concise description of their main anatomic boundaries. The online Supplemental material provides an atlas of the consensus guidelines, projected on 1 mm axial slices. The atlas can also be obtained in DICOM-RT format on request.

Conclusion Consensus guidelines for head and neck OAR delineation were defined, aiming to decrease interobserver variability among clinicians and radiotherapy centers. â&#x20AC;&#x192;

58 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk

Introduction In recent decades, the quality of radiotherapy imaging, planning and delivery has improved markedly. To fully utilize the benefits of these new technologies in radiation oncology practice, consistent delineation of targets and OARs has become increasingly important.

However, delineation accuracy of targets and OARs is limited by interobserver and trial protocol variability. By reducing this variability, the generalizability and clinical utility of Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) models in routine practice can be improved. To reduce treatment variations among clinicians and radiotherapy departments in the delineation of target volumes, guidelines for the delineation of the neck node levels for head and neck tumors have been developed [1]. The interobserver variability in the delineation of head and neck OARs is similar to the variation in the delineation of target volumes [2].

OAR delineation guidelines vary widely between publications and authors, resulting in inconsistent dose-–volume reporting [3]. These inconsistencies hamper the comparison of dose-–volume effect relationships as reported in studies using different delineation protocols [3]. We propose that both daily clinical practice and future multi-institutional clinical trials will benefit from improved consistency in delineation guidelines for OARs.

4

Therefore, the aim of this project was twofold: (1) to attain international consensus on the definition and delineation of OARs for head and neck radiotherapy and (2) to present consensus guidelines for CT-based delineation of a set of OARs in the head and neck region that are considered most relevant for radiotherapy practice.

Methods To reach consensus on OAR guidelines, a panel of experts in the field of head and neck radiation oncology was established (WB, CG, VG, AL, PM, CN, JB, SP, DIR, BOS, JAL). The panel consisted of representatives from Europe, North America, Australia/ New Zealand and Asia and members of the cooperative groups DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG. For the purpose of this project, a number of group meetings were held during international conferences. First, the panel agreed on an OAR set considered relevant for the most common acute and late side effects of head and neck radiotherapy. We did not discuss dose-–volume effects or side effects for the OAR set in this paper, but focussed on a concise description of consensus guidelines for delineation.

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Second, each member of the panel delineated the OARs in a CT set from one patient without any predefined guidelines. The CT images (2 mm slice thickness) were made with the patient in a supine position on a multidetector-row spiral CT scanner (Somatom Sensation Open, 24 slice configuration; Siemens Medical Solutions, Erlangen, Germany).

The delineation environment used is dedicated to study interobserver variability [4].

Subsequently, the outcome of this procedure was presented to and discussed with the experts in order to identify the most prevalent inconsistencies and to formulate consensus guidelines.

Finally, consensus delineations were depicted on axial CT slices of an atlas of head and neck anatomy with 1 mm slice thickness. The CT images were registered with T2-weighted MRI images of the same anatomy for clarification. Since multimodal imaging is not the general standard at present, the atlas description was based on CT only.

Results After the panel delineated the proposed OAR set (Fig. 1), variability in delineation for each OAR was discussed. Subsequently, the panel agreed on consensus definitions for each OAR and formulated the final consensus guidelines for the following 25 head-and-neck OARs:

Anterior segment of the eyeball The anterior segment of the eyeball consists of the structures ventral from the vitreous humor, including the cornea, iris, ciliary body, and lens.

Posterior segment of the eyeball The posterior segment of the eyeball is located posteriorly to the lens, and consists of the anterior hyaloid membrane and all of the posterior optical structures including the vitreous humor, retina, and choroid. The optic nerve is excluded from this contour. The entire retina is included in the posterior segment of the eyeball.

Lacrimal gland The lacrimal gland is located superolateral to the eye and lies within the preseptal space.

The gland is molded at its inferomedial aspect to the globe, giving it a concave outline. The gland can be visualized on CT by its location partly encased in the bone and enveloped in low-density fat.

60 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk

FIGURE 1

Delineation results of 7 members of the panel for the parotid glands, spinal cord, pharyngeal constrictor muscles and the oral cavity, projected on an axial CT slice.

4 Parotid glands The parotid glands were delineated according to previously published guidelines [5]. In these guidelines the retromandibular vein is included in the parotid gland contour, since it is difficult to discriminate it from the parotid gland tissue in non-contrast enhanced CT images.

Anatomic borders are listed in Table 1. The use of a planning CT with intravenous contrast is however strongly recommended to be able to distinguish the extension of the glands from its surroundings.

Submandibular glands The submandibular glands were delineated according to previous published guidelines [5].

Anatomic borders are listed in Table 1.

Extended oral cavity The delineation of the extended oral cavity was based partly on Hoebers et al. [6]. For the sake of simplicity and consistency, the extended oral cavity structure was defined posterior to the internal arch of the mandible and maxilla. The mucosa anterior to the mandible and maxilla is included in the contour of the lips, and the mucosa lateral to the mandible and maxilla is included in the buccal mucosa (see next items and Fig. 3). Anatomic boundaries of the extended oral cavity contour are listed in Table 1. For research purposes, the extended oral cavity can be subdivided into oral tongue and anterior oropharynx, by drawing a vertical line from the posterior hard palate to the hyoid (circumvallate line).

61

Buccal mucosa The buccal mucosa is defined according to the borders listed in Table 1.

Lips The lip contour extends from the inferior margin of the nose to the superior edge of the mandibular body. The lip contour was defined to include the lips as well as the inner surface of the lips (for delineation details concerning inner surface of the lips refer to Van de Water et al. [5]). Detailed anatomic boundaries of the lip contour are listed in Table 1.

Mandible The mandible was defined as the entire mandible bone, without teeth. The use of CT bone view settings is recommended.

Cochlea The cochlea is embedded in the temporal bone, located lateral to the internal auditory meatus, which can best be recognized in CT bone view settings (Fig. 2).

FIGURE 2

Delineation of the cochlea in CT bone settings (left), matched to MRI-T2 (right).

62 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk

Pharyngeal constrictor muscles (PCM) For the delineation of the PCM, many delineation guidelines are available in the literature.

These are particularly variable regarding the cranial and caudal demarcation [3,7]. For the sake of simplicity and reproducibility, we defined the PCM as a single OAR. The cranial border was defined as the caudal tip of pterygoid plates (according to previous studies [7â&#x20AC;&#x201C;12]), and the caudal border as the lower edge of the arytenoid cartilages. For pragmatic reasons, a thickness of 3 mm was assumed (Fig. 3).

Supraglottic larynx The supraglottic larynx is delineated according to Christianen et al. [7]. Anatomic borders are listed in Table 1. An axial slice of the supraglottic larynx is depicted in Fig. 4a.

FIGURE 3

4

Axial (left) and sagittal (right) view of the consensus delineations of the parotid glands (1), pharyngeal constrictor muscles (2), carotid arteries (3), spinal cord (4), mandible (5), extended oral cavity (6), buccal mucosa (7), lips (8), brain (9), chiasm (10), pituitary gland (11), brainstem (12), supraglottic larynx (13), glottic area (14), crico-pharyngeal inlet (15), cervical esophagus (16) and thyroid (17). (For the full atlas, the reader is referred to the online Supplemental material.)

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64 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk Hard palate (lateral), anterior nasal spine (at the midline)

Bottom of maxillary sinus

Lips

Buccal mucosa

Upper edge teeth Lips, teeth sockets

Lower edge teeth Outer surface of sockets, cranial the skin edge mandibular body

Medial pterygoid muscle

Mandibular body, teeth, tongue, air (if present)

Buccal fat

Depressor anguli oris m., buccinator m., levator anguli oris m./risorius m. Buccinator

Inner surface of the mandible and maxilla

Post. borders of soft Inner surface of the mandible and palate, uvula, and more inferiorly the maxilla base of tongue

The base of tongue mucosa and hyoid posteriorly and the mylohyoid m. and anterior belly of the digastric m. anteriorly

Hard palate mucosa and mucosal reflections near the maxilla

Posterior to mandible and maxilla, no inner surface of the lips.

Extended oral cavity

Outer surface of the mandible and maxilla, oral cavity/ base of tongue/ soft pallate

Lat. Surface mylo足 huoid m., hyoglossus m., superior and middle pharyn足geal con足stric足tor m., anterior belly of the digastric m.

Med. Surface medial pterygoid m., med surface mandibular bone, platysma

Parapharyngeal space, sternocleidomastoid m.

Post. belly of the digastric m., styloid process, parapharyngeal space

Subcutaneous fat, platysma

Ant. belly sternocleidomastoid m., lat. side post. belly of the digastric m. (posterior-medial)

Masseter m., post. border mandibular bone, medial and lateral pterygoid m.

Lat. Surface mylohyoid m., hyoglossus m.

Medial

Lateral

Posterior

Anterior

Fatty tissue

External auditory Post. part submandibular canal, mastoid space process

Caudal

Medial pterygoid m., mylohyoid m.

Include carotid artery, retromandibular vein and extracranial facial nerve.

Parotid gland

Cranial

Submandibular gland

Remarks

Organ at risk

Anatomic boundaries

Organs at risk with specification of anatomic boundaries.

TABLE 1

Cranial border of T3, vertebral body

1 cm caudal to the lower edge of the cricoid cartilage Cranial border of C5, vertebral body

Cervical esophagus

Brachial plexus

If the brachial plexus is wrapped around the vascular bundle on the most inferior slices, the vascular structure is included in the contour.

Caudal edge of C7

Caudal edge of arytenoid cartilages

Crico-pharyngeal inlet

1 cm caudal to the lower edge of the cricoid cartilage

Caudal edge of ant. part of thyroid cartillage

Cranial edge of arytenoid cartilages

Cranial edge of arytenoid cartilages

Caudal edge of arytenoid cartilages

Glottic area

Caudal tips of pterygoid plates

Tip of epiglottis

Thickness ~3mm.

Supraglottic larynx

Pharyngeal constrictor muscle

Vertebral body

Ventral border of: middle scalene muscle, seratus anterior muscle, subscapularis muscle Dorsal border of: anterior scalene muscle, subvlavian artery, axillary vein

Cricoid, anterior border arytenoids

Inferior PCM, pharyngeal lumen

Tracheal lumen

Hyoid bone, pre-epiglottic space, thyroid cartilage

Superior: hamulus Prevertebral muscle of pterygoid plate; mandibula; base of tongue; pharyngeal lumen. Middle: base of tongue; hyoid. Inferior: soft tissue of supraglottic/ glottic larynx

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Lateral border of: anterior and middle scalene muscles, pectoralis major, teres major

Inter vertebral foramen (bony vertebral body), lateral border of 1st rib

Thyroid cartilage Pharyngeal lumen (lumen excluded)

Superior: medial pterygoid muscle. Middle: greater horn of hyoid bone.

Inferior: superior horn of thyroid cartilage

Glottic area We decided to define the glottic area structure, including the vocal cords and paraglottic fat.

Air should be excluded from the contour. Cranial, caudal and posterior borders can be found in Table 1. An axial slice of the glottic area is depicted in Fig. 4b.

Arytenoids The arytenoids (or arytenoids cartilage) are defined as a separate structure. The base (caudal edge) of each arytenoid is broad for articulation with the cricoid cartilage. The apex (cranial edge) is pointed.

Cricopharyngeal inlet The crico-pharyngeal inlet represents the transition from the PCM to the cervical esophagus (Table 1). An axial slice of the crico-pharyngeal inlet is depicted in Fig. 4c.

Cervical esophagus The cervical esophagus starts 1 cm caudal to the lower edge of the cricoid cartilage, and ends at the caudal edge of C7 (Table 1). An axial slice of the cervical esophagus is depicted in Fig. 4d.

Brachial plexus It is difficult to localize the brachial plexus on CT. Anatomical borders are listed in Table 1, and a step-by-step technique, based on the guideline of Hall et al. [14], can be found in the online Supplemental Materials.

Thyroid gland The thyroid gland has two connected lobes and is located below the thyroid cartilage. It has considerable contrast compared to its surrounding tissues.

Brain The delineation of the brain includes brain vessels, and excludes the brainstem. CT bone settings are recommended. In the case of nasopharyngeal cancer, a subdivision of brain structures could be made, i.e. delineation of the hippocampus and temporal lobe with the use of a brain atlas [15,16].

Brainstem The cranial border of the brainstem was defined as the bottom section of the lateral ventricles, the caudal border as the tip of the dens of C2 (cranial border of the spinal cord).

MRI is recommended for delineation of the brainstem. The bottom section of the lateral ventricles is clearly visible on both CT and MRI. For research purposes, the brainstem could be further subdivided, for example according to Kocak-Uzel et al. [17].

66 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk

FIGURE 4

4

Axial CT slices showing the delineation of the supraglottic larynx (A) (a), glottic area (B) (b), crico-pharyngeal inlet muscle (C) (c), and cervical esophagus ( D) (d). Other organs at risks visible are the submandibular glands ( 1), pharyngeal constrictor muscles (2), carotid arteries (3), brachial plexus (4), spinal cord (5), arytenoids (6) and thyroid (7). (For the full atlas, the reader is referred to the online Supplemental material.)

67

Pituitary gland The pituitary gland is a very small OAR, which in general cannot be identified easily on CT. Alternatively, however, the inner part of the sella turcica can be used as surrogate anatomical bony structure. The borders of the pituitary gland can be defined best in the sagittal view.

Optic chiasm The optic chiasm is located in the subarachnoid space of the suprasellar cistern. Typically, it is located 1 cm superior to the pituitary gland, located in the sella turcica . MRI is recommended for delineation of the optic chiasm. It is demarcated laterally by the internal carotid arteries and inferiorly to the third ventricle (Fig. 5) [19,20].

Optic nerve The optic nerve is usually 2â&#x20AC;&#x201C;5 mm thick and in general is clearly identifiable on CT [20].

It has to be contoured all the way from the posterior edge of the eyeball, through the bony optic canal to the optic chiasm. MRI is recommended for a better delineation of the optic nerve, at least close to the optic chiasm.

Spinal cord The spinal cord is delineated as the true spinal cord, not the spinal canal. The cranial border was defined at the tip of the dens of C2 (the lower border of the brainstem), and the caudal border at the upper edge of T3. With caudally located tumours or lymph node areas, we advise extending the spinal cord contours by at least 5 cm caudal to the PTV.

Carotid arteries The carotid arteries include the common and internal carotid artery (external carotid artery was omitted). The left and right common carotid arteries follow the same course with the exception of their origin. The right common carotid originates in the neck from the brachiocephalic trunk. The left arises from the aortic arch in the thoracic region. The bifurcation into the external and internal carotid arteries occurs around the level of C4. The upper border of the internal carotid artery is the cranial part of the optic chiasm.

The resulting consensus delineation guidelines were depicted on 1 mm axial CT slices from an anatomy atlas in Mirada RTx (Mirada Medical Ltd., UK) (online Supplemental material).

The atlas in DICOM-RT format can be retrieved via the different co-operative groups.

68 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk

FIGURE 5

Delineation of the optic nerves (blue and purple), optic chiasm (green) and carotid arteries (yellow and brown) on CT (left) and MRI-T2 (right). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4

Discussion With the introduction of these consensus guidelines for delineation of OARs, we aim to decrease interobserver variability among clinicians and radiotherapy centers. These guidelines complement the previously published guidelines for neck node levels for head and neck tumours [1]. These two guidelines combined should contribute to reduce treatment variability and should also aid the design and implementation of multiinstitutional clinical trials. The OAR guidelines are particularly useful when radiation-induced side effects are considered relevant endpoints. Moreover, the current consensus guidelines could facilitate the generalizability and clinical utility of Normal Tissue Complication Probability (NTCP) models.

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We decided not to describe all possible OARs in great detail. Consequently, for some OARs we did not use single anatomic structures, but amalgamated surrogate structures involved in combined functions (e.g. the extended oral cavity). Nevertheless, the current guideline contains a comprehensive list of OARs. At the individual patient/center level one should decide which OAR to include, a decision that may depend on the tumour location, for example. In general, it is helpful to always include the delineation of the parotid and submandibular glands, spinal cord and PCM. For research purposes, OARs could be further subdivided (e.g. as described in case of the extended oral cavity, PCM, brainstem and brain).

There are natural variations for some OARs, such as the location of the bifurcation of the common carotid artery [21], which is used for contouring the brachial plexus. In addition, anatomic changes in OARs may occur due to tumor extension, or an OAR may be infiltrated by tumor. Therefore, a basic understanding of the normal anatomy remains essential.

For primary tumors of the nasopharynx, oral cavity and oropharynx, we strongly recommend the use of MRI in addition to CT. This will facilitate the delineation of OARs in this area, which includes the brainstem, spinal cord, pituitary gland, lacrimal glands, optic chiasm and optic nerves. MRI is ordinarily also beneficial for delineation of the parotid glands and PCM. For primary tumors in close vicinity of the brain, we also recommend defining the temporal lobe and hippocampus (but delineation guidelines for these OARs are beyond the scope of these current guidelines) [15,16].

Some of the atlas structures are very small, such as the cochlea, pituitary gland, lacrimal glands and chiasm, with volumes <0.5 cm3. Volume and doseâ&#x20AC;&#x201C;volume histogram (DVH) data calculated over such small volumes is susceptible to differences in the calculation algorithm (i.e. sampling and interpolation strategy), and also depend on CT slice thickness, pixel width, dose grid voxel width and DVH dose resolution, and may differ widely between the various methods [22]. Consequently we recommend expanding small structures such as the cochlea, pituitary gland, chiasm and arytenoids by 5 mm to calculate reliable and more consistent DVH data (but avoid overlap with the PTV). Additionally, we recommend acquiring constrast-enhanced CT scans with â&#x2030;¤ 2mm slice thickness to improve delineations of such very small structures.

For some, serial OARs, ICRU recommends the addition of a PRV margin, which depends on planning technique and patient population [23]. For the spinal cord for example, it is common practice to add a 5mm PRV margin [24]. In the case of OARs in close proximity to, or overlapping with the PTV, derived OAR structures can help to guide the planning process (i.e. OAR with subtraction of the PTV). For dose evaluation, however, the original OAR contour should be used. We advise to adhere to the standardized OAR naming conventions as proposed by Santanam et al. [25].

70 CHAPTER 4 - International consensus delineation guidelines for head and neck organs at risk

We recommend incorporating the current guidelines on a large scale to support consistent reporting of dose–volume data in addition to encouraging consistent radiotherapy practice for treatment prescriptions. Considering the increasing use and availability of MRI as well as the increasing knowledge and understanding about the OARs that are most relevant for side effects in radiotherapy, we anticipate updating these recommendations in the near future to a full MRI-based delineation guideline, incorporating as much anatomical and functional information as possible.

References 1. Grégoire V, Ang K, Budach W, et al. Delineation of the neck node levels for head and neck tumors: A 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines. Radiother Oncol. 2014;110:172–81.

2. Brouwer CL, Steenbakkers RJHM, van den Heuvel E, et al. 3D Variation in delineation of head and neck organs at risk. Rad Oncol. 2012;7:32.

3. Brouwer CL, Steenbakkers RJHM, Gort E, et al. Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP. Radiother Oncol. 2014;111:148-52.

4. Steenbakkers RJHM, Duppen JC, Fitton I, et al. Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction: A “Big Brother” evaluation. Radiother Oncol. 2005;77:182–190.

5. Van de Water T a, Bijl HP, Westerlaan HE, et al. Delineation guidelines for organs at risk involved in radiation-induced salivary dysfunction and xerostomia. Radiother Oncol.

2009;93:545–52.

6. Hoebers F, Yu E, Eisbruch A, et al. A Pragmatic Contouring Guideline for Salivary Gland Structures in Head and Neck Radiation Oncology. Am J Clin Oncol. 2013;36:70–76.

7. Christianen MEMC, Langendijk J a, Westerlaan HE, et al. Delineation of organs at risk involved in swallowing for radiotherapy treatment planning. Radiother Oncol. 2011;101: 394–402.

8. Dirix P, Abbeel S, Vanstraelen B, et al. Dysphagia after chemoradiotherapy for head-and-neck squamous cell carcinoma: dose-effect relationships for the swallowing structures. Int J Radiat Oncol Biol Phys. 2009;75:385–92.

9. Caglar HB, Tishler RB, Othus M, et al. Dose to larynx predicts for swallowing complications after intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys. 2008;72:1110–8.

10. Caudell JJ, Schaner PE, Desmond RA, et al. Dosimetric factors associated with long-term dysphagia after definitive radiotherapy for squamous cell carcinoma of the head and neck.

Int J Radiat Oncol Biol Phys. 2010;76:403–9.

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11. Feng FY, Kim HM, Lyden TH, et al. Intensity-modulated radiotherapy of head and neck cancer aiming to reduce dysphagia: early dose-effect relationships for the swallowing structures. Int J Radiat Oncol Biol Phys. 2007;68:1289–98.

12. Li B, Li D, Lau DH, et al. Clinical-dosimetric analysis of measures of dysphagia including gastrostomy-tube dependence among head and neck cancer patients treated definitively by intensity-modulated radiotherapy with concurrent chemotherapy. Rad Oncol. 2009;4:52.

13. Schwartz DL, Hutcheson K, Barringer D, et al. Candidate dosimetric predictors of long-term swallowing dysfunction after oropharyngeal intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys. 2010;78:1356–1365.

14. Hall WH, Guiou M, Lee NY, et al. Development and validation of a standardized method for contouring the brachial plexus: preliminary dosimetric analysis among patients treated with IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2008;72:1362–7.

15. Scoccianti S, Detti B, Gadda D, et al. Organs at risk in the brain and their dose-constraints in adults and in children: A radiation oncologist’s guide for delineation in everyday practice.

Radiother Oncol. 2015;114:230–238.

16. Sun Y, Yu X-L, Luo W, et al. Recommendation for a contouring method and atlas of organs at risk in nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy.

Radiother Oncol. 2014;110:390–7.

17. Kocak-Uzel E, Gunn GB, Colen RR, et al. Beam path toxicity in candidate organs-at-risk: Assessment of radiation emetogenesis for patients receiving head and neck intensity modulated radiotherapy. Radiother Oncol. 2014;111:281–288.

18. De Groot C, Steenbakkers RJ, Dorgelo B et al. (submitted). The clinical relevance of delineation of the optic pathway in radiotherapy treatment planning. Radiother Oncol. 2015.

19. De Moraes CG. Anatomy of the visual pathways. J Glaucoma. 2013;22 Suppl 5:S2–7.

20. Celesia GG, DeMarco PJ. Anatomy and physiology of the visual system. J Clin Neurophysiol.

1994;11:482–92.

21. Ashrafian H. Anatomically specific clinical examination of the carotid arterial tree. Anat Sci Int. 2007;82:16–23.

22. Ebert MA, Haworth a, Kearvell R, et al. Comparison of DVH data from multiple radiotherapy treatment planning systems. Phys Med Biol. 2010;55:N337–46.

23. ICRU Report 62. Prescribing, Recording, and Reporting Photon Beam Therapy (Supplement to ICRU Report 50). 1999.

24. Purdy JA. Current ICRU definitions of volumes: limitations and future directions.

Semin Radiat Oncol. 2004;14:27–40.

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5

eometric and dosimetric variability in head and neck organs at risk during the course of radiotherapy: Literature review

Published as: Identifying patients who may benefit from adaptive radiotherapy: Does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help?

Brouwer CL, Steenbakkers RJ, Langendijk JA, Sijtsema NM. Radiother Oncol. 2015 Jun;115(3):285-94.

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Abstract Background The last decade, many efforts have been made to characterize anatomic changes of head and neck organs at risk (OARs) and the dosimetric consequences during radiotherapy. This review was undertaken to provide an overview of the magnitude and frequency of these effects, and to investigate whether we could identify criteria to identify head and neck cancer patients who may benefit from adaptive radiotherapy (ART). Possible relationships between anatomic and dosimetric changes and outcome were explicitly considered.

Methods A literature search according to PRISMA guidelines was performed in MEDLINE and EMBASE for studies concerning anatomic or dosimetric changes of head and neck OARs during radiotherapy.

Results Fifty-one eligible studies were found. The majority of papers reported on parotid gland (PG) anatomic and dosimetric changes. In some patients, PG mean dose differences between planning CT and repeat CT scans up to 10 Gy were reported. In other studies, only minor dosimetric effects (i.e. <1 Gy difference in PG mean dose) were observed as a result of significant anatomic changes. Only a few studies reported on the clinical relevance of anatomic and dosimetric changes in terms of complications or quality of life. Numerous potential selection criteria for anatomic and dosimetric changes during radiotherapy were found and listed.

Conclusions The heterogeneity between studies prevented unambiguous conclusions on how to identify patients who may benefit from ART in head and neck cancer. Potential pre-treatment selection criteria identified from this review include tumour location (nasopharyngeal carci­ noma), age, body mass index, planned dose to the parotid glands, the initial parotid gland volume, and the overlap volume of the parotid glands with the target volume. These criteria should be further explored in well-designed and well-powered prospective studies, in which possible relationships between anatomic and dosimetric changes and outcome need to be established.

74 CHAPTER 5 - Literature review

Introduction Radiotherapy is a commonly applied treatment modality in head and neck cancer patients.

Intensity modulated radiotherapy treatment plans with steep dose gradients are currently considered standard. These treatment plans are constructed on planning CT images, acquired prior to the start of radiotherapy. To account for patient positioning errors relative to these planning CT images, position verification procedures are generally applied during the course of radiotherapy. However, because of different patient postures and anatomic changes during the course of radiotherapy, the dose actually given to the patient can deviate from the planned dose [1]. These dose differences may lead to underdosage to target volumes and/or overdosage to organs at risk (OARs) [2].

Radiation-induced complications have a significant adverse impact on health-related quality of life [3]. Hence, it is important to monitor radiation doses to OARs during treatment. This is particularly salient in the head and neck area, where OARs are in close proximity to target volumes. However, at present, verification of the dose actually given to the patient is not considered routine clinical practice. Adaptive radiotherapy (ART) could be applied to reduce dose to OARs and eventually to improve quality of life [4â&#x20AC;&#x201C;8]. ART is a formal approach to correct for daily tumour and normal tissue variations through streamlined online or offline modification of original target volumes and plans [9, 10]. Implementation of ART is challenging both from clinical and logistic points of view and generally requires many resources. Clear guidelines are needed on the timing of rescanning and replanning, and an increasing amount of data needs to be acquainted, handled, transferred and stored. It is unlikely that every patient will benefit from ART and therefore tools to select patients who are expected to benefit most from plan adaptation during treatment become increasingly important [11].

In previous studies, it was shown that anatomic changes cause more dose deviations in OARs than in target volumes [12â&#x20AC;&#x201C;15]. Clinical target volume (CTV) coverage is usually more robust to changes because of the use of the PTV concept, while planning volumes at risk (PRV) margins are generally applied for spinal cord and brain stem, but are not common practice for all OARs. Only 13% of the studies in this review reported PRV margins around the spinal cord and/or the brainstem [4, 5, 11, 16â&#x20AC;&#x201C;18], and only 4% of the studies reported on PRV margins for all OARs [5, 16]. In addition, position verification mainly focuses on correcting for set-up errors of targets, and for that reason might lead to increased doses to distant OARs. Therefore, it is expected that the largest gain of ART would therefore be the monitoring and reduction of the dose to OARs. For a strategic selection of patients who may benefit from adaptive strategies, identification of selection criteria that are associated with dosimetric changes and resulting complications is necessary. Patient selection for ART can be realized by selection prior to treatment,

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5

i.e. based on pre-treatment characteristics, and by selection during treatment based on geometric and/or dosimetric changes early in treatment, either by non-imaging related factors (e.g. weight loss) or by imaging related factors (e.g. density changes).

Castadot et al. [19] have summarized the results of seven studies reporting on anatomic modifications of head and neck target volumes and OARs during radiotherapy in 2010. The authors concluded that radiotherapy induces major volumetric and positional changes in CTVs and OARs during treatment. Parotid glands tend to shrink and to shift medially towards the high dose region, potentially jeopardizing parotid sparing [19]. Not all of these studies reported to what extent these anatomic changes actually translate into dosimetric changes.

Furthermore, no unambiguous effect of anatomic changes on dose has been found. Since 2010, the amount of studies reporting on anatomic and dosimetric changes has increased dramatically.

The main objective of this review was to evaluate the current literature on anatomic and dosimetric changes of head and neck OARs during radiotherapy. Furthermore, implications of these changes for the rate and severity of complications and quality of life were reported.

In addition, we tried to identify selection criteria for changes during radiotherapy and recommended on the conduction of further studies on this subject. Results of this review could provide useful information for the development of strategies for patient selection in ART.

Methods We performed a literature search in MEDLINE and EMBASE according to PRISMA guidelines [20] using the following keywords: ((synonyms for anatomic changes) OR (synonyms for dosimetric changes)) AND (synonyms for organs at risk) AND (synonyms for head and neck radiotherapy). The search was completed by March 1, 2015.

In addition, reference lists of papers were screened in order to retrieve additional relevant papers. Both prospective and retrospective studies published in journals part of the Thomson Reuters journal citation reportsÂŽ were included. Studies in languages other than English, and studies only available in abstract form were excluded from this review.

Studies had to fulfil the following eligibility criteria to be selected for this review: â&#x20AC;˘ report on anatomic and/or dosimetric changes of adult head and neck organs at risk during the course of photon radiotherapy, and â&#x20AC;˘ at least ten patients included.

76 CHAPTER 5 - Literature review

We present overviews of anatomic changes, dosimetric changes, and report potential selection criteria of either one. In addition, we report on studies describing the effects of anatomic and dosimetric changes during radiotherapy on side effects and quality of life. The results are presented by volume changes in percentages and dose changes in Gray in order to make comparisons across studies easier to interpret. Associations are presented in five ways; by the Pearson correlation coefficient (R), the coefficient of determination (R2), the Spearman’s rank correlation coefficient (ρ), linear regression analysis (r or r2), and by the odds ratio (OR), according to the study methodology.

Results Literature search Figure 1 presents the outcome of the search strategy. Fifty-two potentially eligible records were found in MEDLINE and EMBASE, and forty-four additional papers were extracted from reference lists. We excluded twenty-eight records as they were conference abstracts, two studies because only the abstract was available [21, 22], and two papers because their journal was not part of the Thomson Reuters journal citation reports® [23, 24]. Furthermore, three papers were excluded as they were general reviews on image guided radiotherapy (IGRT) and ART [9, 19, 25]. Eventually, sixty-one full text articles were assessed for eligibility.

Five of these papers did not meet the eligibility criteria since they reported on other subjects and five papers included less than ten patients [26–30]. Hence a final number of fifty-one studies could be included in the analyses for this review [2, 4–7, 11–18, 31–68].

Reported organs at risk In the fifty-one original studies at least one of the following organs at risk was included in the analysis: the parotid gland (PG), submandibular gland, spinal cord, spinal canal, brainstem, mandible, oral cavity, larynx, pharyngeal constrictor muscles, sternocleidomastoid muscles, masticatory muscles, masseter muscles, medial pterygoid muscles, thyroid gland, optic chiasm, optic nerve, eyeball and lens.

Timing and frequency of imaging during radiation treatment OARs were assessed on different time points during radiotherapy (tenth fraction to end of treatment, see also Figure 2, Table 4-6) mainly by cone beam CT (CBCT), helical repeat CT and megavoltage CT (MVCT), but also by in-room CT [14, 17, 44, 51] and MR imaging [52]. Two studies applied repeat MR imaging in addition to the repeat CT scans [15, 16].

In most studies, the re-delineation of OARs was performed manually or automatically using deformable image registration (DIR) with visual inspection and manual corrections if needed. The frequency of imaging varied between studies. Most of the studies reported on

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FIGURE 1

PRISMA flow diagram of literature search.

multiple time points during radiotherapy. Fourteen studies performed at least weekly repeat imaging [7, 12, 17, 18, 37, 43â&#x20AC;&#x201C;46, 51, 53, 62, 63, 65]. Three of these studies had daily MVCT imaging at their disposal [18, 45, 46], and one performed daily in-room CT imaging [7]. If results of multiple time points were reported, anatomic and dosimetric changes from the last time point were included in this review.

The authors of the different studies reported a variety of time points during treatment that could be optimal for re-scanning and re-planning. There are several indications that anatomic changes are more pronounced in the first half of treatment, and therefore re­­peated imaging and replanning should be performed in this first time period [34, 60, 62, 65].

78 CHAPTER 5 - Literature review

FIGURE 2

5 Author 1. Ahn et al. [31] 2. Bhide et al. [34] 3. Capelle et al. [36] 4. Castadot et al. [11] 5. Chen et al. [15] 6. Cheng et al. [16] 7. Duma et al. [38] 8. Duma et al. [13] 9. Fung et al. [41] 10. Hansen et al. [2] 11. Height et al. [42] 12. Ho et al. [43] 13. Jensen et al. [44] 14. Lee et al. [46] 15. Marzi et al. [48]

Time point fx 11 (mean) end of tx fx 15 fx 20-25 end of tx fx 25 fx 25 fx 16 (median) fx 30-35 fx 29 Âą 9 fx 20-25 fx 25-30 fx 5-35 end of tx end of tx

16. Nishi et al. [4] 17. Robar et al. [17] 18. Wang et al. [59] 19. Wu et al. [12] 20. Zhao et al. [6] 21. Ajani et al. [32] 22. Barker et al. [33] 23. Broggi et al. [35] 24. Lu et al. [47] 25. Richetti et al. [53] 26. Sanguineti et al. [62] 27. Vasquez et al. [57] 28. Wang et al. [60] 29. Cozzolino et al. [66] 30. Hunter et al. [65] 31. Castelli et al. [63]

fx 10-20 end of tx fx 18 fx 25-30 fx <20 fx 25-30 end of tx end of tx fx 20 fx 30-35 fx 16(median) fx 23 end of tx fx 25 end of tx fx 25-30

(a) Parotid volume loss vs. patientâ&#x20AC;&#x2122;s weight loss (22 studies), (b) parotid mean dose increase (repeat CT - plan CT) vs. weight loss (16 studies) , (c) parotid volume loss vs. planned parotid mean dose (20 studies) , and (d) parotid mean dose increase (repeat CT plan CT) vs. parotid volume loss (23 studies) during radiotherapy. The size of the data points is proportional to the number of patients included in the study (minimum 10, maximum 87 patients). fx= fraction, tx= treatment. Time point: time of the repeat scan analysed.

79

Anatomic and dosimetric changes Twenty-six papers described both anatomic and dosimetric changes during the course of radiation [2, 4, 6, 11–13, 15–17, 31, 34, 36, 38, 39, 41–44, 46, 48, 54, 59, 61, 63, 65, 66]. Two studies reported on dosimetric changes without referring to anatomic changes [18, 51]. Twenty studies described the relationship between several parameters and anatomic and/or dosimetric changes [4, 7, 11, 31–33, 35, 36, 40, 43, 46, 48, 57, 59, 60, 62–65, 68] (Table 1 and 2). Twelve studies reported on the association between anatomic and dosimetric changes with complications and quality of life [5–7, 48, 50, 54, 55, 61, 63, 65, 67, 68] (Table 3). In two of these studies, a significant reduction of side effects was found when replanning was performed vs. no replanning [5, 6]. The findings of specific changes during radiotherapy and the corresponding correlations and associations are summarized per organ in the next paragraphs.

1. Parotid gland The majority of the studies included anatomic and/or dosimetric changes of the parotid glands (PGs). This interest for PG changes during radiotherapy can be explained by the fact that radiation dose to the PGs was associated with reduced saliva production [69] and xerostomia [70, 71].

When all studies were taken into account, the average volume decrease of the PGs during radiotherapy was 26 ± 11% (note: volume loss reported on different time points during radiotherapy, Table 4). Some studies presented the PG shrinkage rate per treatment day or treatment week [15, 33, 37]. Sanguineti et al. [62] studied weekly CT scans of eighty-five patients and concluded that the PG shrinkage is not linear (PGs shrunk most during the first half of treatment).

Thirty-eight papers reported on PG anatomic changes [2, 4, 6, 7, 9, 12, 13, 15–17, 31–45, 47, 48, 50, 53, 56, 57, 59, 60, 62–66]. The most common reported anatomic changes were volume loss (Table 4) and medial shifts of the PGs [17, 32–34, 37, 42, 44, 45, 48, 51, 57, 59, 64]. Jensen et al. [44] found a medial, cranial, and dorsal shift of the PG centre of mass from its original position. In general, a medial shift was observed of the medial and lateral aspects of both PGs [4, 17, 32, 48, 57]. Vasquez-Osorio et al. [57] reported on shape and position changes of six sub-regions of the PG. The medial translation of the inferior region of the irradiated PGs was similar to that of the lateral region (3±4 mm). Fiorino et al.

[40] and Belli et al. [67] found reduced PG densities during IMRT. Twenty-four papers reported on dosimetric changes of the PGs [2, 4, 6, 11–13, 15–18, 31, 34, 36, 38, 41–44, 46, 48, 51, 59, 65, 66]. On average, the PG mean dose increased with 2.2 ± 2.6 Gy as compared to the dose calculated on the planning CT at baseline. Not all papers reported on absolute dose values. The studies that reported the highest dose

80 CHAPTER 5 - Literature review

increase consisted of (a majority of) (naso)pharyngeal carcinoma patients [4, 6, 15, 16, 34, 46, 58]. The largest PG dose increase was found by Chen et al. [15] and Cheng et al. [16] (on average mean dose increase of 10.4 Gy in the sixth week of radiotherapy, and median dose increase of 7.8 Gy at the twenty fifth fraction, respectively). Both prospective studies included stage III-IV nasopharyngeal carcinoma patients.

1.1. Factors correlating with parotid gland volume loss and parotid mean dose increase Eighteen studies reported on factors that correlated significantly with PG anatomic and/ or dosimetric changes (Table 1). The most frequently reported factors were weight loss, PG dose and PG volume loss. All data available is presented in Figure 2. On average, no clear relation between these factors and changes was found. The strongest association found was between PG dose and PG volume loss; three of the larger studies (more than eighty patients included) reported a significant correlation of PG dose with PG volume loss [35, 60, 62]. Still, large variety of volume loss was observed between studies (Figure 2c).

Table 1 includes all factors that demonstrated significant correlations (p<0.05) with corresponding correlation/association measures.

1.2. Anatomic and dosimetric changes of the parotid gland and outcome Significant associations between PG volume change and the occurrence of complications were found in five studies [7, 48, 55, 67, 68] (Table 3). In general, more PG shrinkage was associated with higher complication rates [48, 67]. On the contrary, Sanguineti et al. [68] observed that the patients who received mean doses over 35.7 Gy to the PG developed more physician-reported GR2+ xerostomia if the shrinkage of the combined volume of parotid glands was lower than 19,6%. Nishimura et al. [50] found out that the patients with initially small parotid glands (â&#x2030;¤ 38.8 ml) had significantly more severe xerostomia three to four months after the start of IMRT than patients with initially larger parotid glands (p= 0.040), while the correlation between the shrinkage of PG and the grade of xerostomia was not significant (p= 0.186) (Table 3). Belli et al. found that apart from volume decrease, also early density decrease was associated with significantly higher acute xerostomia scores (Table 3). Weight loss >5% and/or decrease of neck diameter >10% was associated with higher xerostomia scores in the study of You et al. [61]. Hunter et al. concluded that dosimetric changes were small relative to the standard deviations of the dose and saliva flow data [65].

Yang et al. [5] studied global quality of life in addition to the different side effects for IMRT replanning vs. no replanning, and reported better quality of life and less side effects for the IMRT replanning group (Table 3).

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5

82 CHAPTER 5 - Literature review

23

13

14

87

84

10

10

85

85

24

10

Ajani et al. [32]

Barker et al. [33]

Broggi et al. [35]*

Fiorino et al. [40]

Ho et al. [43]

Reali et al. [64]

Sanguineti et al. [62]

Sanguineti et al. [68]

Schwartz et al. [7]

Vasquez-Osorio et al. [57]

PG Dmean

Weight loss

Body mass index PG Dmean

Weight loss PG Dmean Age

PG Dmean

Weight loss

PG volume loss

PG volume loss

r=0.68

n.r.

ρ = -0.234 ρ = 0.258

OR= 1.160 (1.04-1.29), Multivariable analysis OR= 1.080 (1.01-1.17), “ OR= 0.960 (0.93-0.99), “

PG volume loss

PG volume loss

r2= 0.31, 0.41 (left, right PG)

ρ= 0.83

ρ = 0.23

PG volume loss

PG volume loss

PG volume loss

p< 0.001

p= 0.04

p= 0.031 p= 0.017

p= 0.007 p= 0.038 p= 0.033

p< 0.001

p< 0.0001

p= 0.003

p= 0.012 p< 0.00001 OR= 1.034 (1.0075-1.061), Multivariable analysis OR= 0.863 (0.809-0.921), “

PG volume loss (%)

PG V40 ΔBody thickness (%) PG density decrease

p= 0.0001 p= 0.0001 p= 0.0002 p= 0.006 p= 0.004 p= 0.0002 p= 0.038

OR= 0.845 (0.78-0.92), Univariable analysis OR= 0.181 (0.078-0.422), “ OR= 1.191 (1.086-1.306), “ OR= 1.038 (1.011-1.066), “ OR= 1.059 (1.018-1.100), “ OR= 1.100 (1.056-1.158), Multivariable analysis OR= 1.059 (1.003-1.118), “

PG volume loss (cc)

Weight loss ΔBody thickness (cc) OVP PG V40 Overall treatment time Initial PG volume PG Dmean

p< 0.01

p< 0.01

ρ= 0.66

PG volume loss

n.r.

p value

ρ= 0.931, Spearman two-tailed correlation

R= 0.52-0.67

Correlation/ Association

PG volume loss

Endpoint

PG center of mass shift

Weight loss

Weight loss PG Dmean ≥ 31Gy vs. PG Dmean < 31Gy

Weight loss

# Pts Parameter

Ahn et al. [31]

Anatomic endpoints:

Study

Studies reporting on parameters associated with anatomic or dosimetric changes of parotid glands during the course of radiotherapy. Only statistically significant correlations are shown. Results of Spearman correlation were denoted with ρ, Pearson correlation with R, linear regression analysis by r or r2 and odds ratio by OR.

TABLE 1

83

15

Wang et al. [59]

20

10

15

18

10

15

15

Capelle et al. [36]*

Castadot et al. [11]

Castelli et al. [63]

Hunter et al. [65]

Lee et al. [46]

Marzi et al. [48]

Wang et al. [59]

ρ= 0.93, 0.85 (left, right PG)

R= 0.41 R= 0.42 R= 0.22-0.39

PG volume loss

PG D50%% increase

p < 0.01 p< 0.001

ρ= 0.64 0.62, correlation measure n.r. n.r., Linear mixed effect model ρ= 0.92

Combined PG mean dose increase Contralateral PG mean dose increase PG mean dose increase

Weight loss

ΔGTV (cm3)

PG COM distance decrease Weight loss

r2= 0.88 r2= 0.58 r2= 0.43, Stepwise multiple regression ρ= 0.85

PG mean dose increase PG mean dose increase PG mean dose increase

p< 0.001

p= 0.015

n.r. n.r.

p= 0.006

p= 0.002

n.r. n.r. n.r.

n.r. n.r. n.r. n.r. n.r. n.r. p= 0.002 p= 0.0010.006 p= 0.07-0.08

p< 0.001

p< 0.001 p< 0.001

PG D50% overdose (D50% >26Gy)

R= 0.22 R= 0.30-0.35

R= 0.17-0.28

r= 0.41 r= 0.39

PG volume loss SMG volume loss

PG mean dose difference first PG mean dose fraction (Gy) difference (end of treatment)

CTV70 shrinkage Reduction of neck diameter

Contralateral PG shrinkage slope (cc/day)

Reduction of neck diameter mid-PTV level

Anatomic isocenter AP Mandible vector, AP, SI Reduction of lateral neck diameter at C2-C3

Cochlea vector increase Mandible vector increase Reduction of lateral neck diameter at mandibular joint Reduction of lateral neck diameter at C1-C5 Parotid volume decrease Weight loss

Weight loss

PG Dmean SMG Dmean

* only the strongest associations were listed.

Pts= patients, PG= parotid gland, ipsi= ipsilateral, contra= contralateral, n.r.= not reported, AP=anterior-posterior, SI=superior-inferior, OVP=overlap volume parotid gland and lymphnodal tumour, COM= center of mass, OR= odds ratio (95% confidence intervals).

23

Ahn et al. [31]

Dosimetric endpoints:

82

Wang et al. [60]

5

2. Submandibular glands In contrast to the parotid glands, information on anatomic and dosimetric changes of the submandibular glands is scarce. On average, a submandibular gland volume reduction of 22% (15-32%) was found [37, 57, 60]. Wang et al.[60] found a significant correlation between the planned submandibular gland dose (r=0.389, p<0.001) and submandibular gland volume reduction, while no such correlations were found by others [57]. Irradiated submandibular glands tend to move superiorly, in particular the caudal and lateral sub-regions with displacements of on average 3-4 mm [57]. Similar results were found by Castadot et al. [11]. These authors also observed superior as well as medial shifts of the submandibular glands. In this study, the mean approximated actually delivered doses to the submandibular glands increased compared with the planned doses (52.8 vs. 51.9 Gy) (Table 6).

3. Brainstem and spinal cord A number of authors reported on changes in maximum doses or D1% to the brainstem [2, 6, 15, 16, 18, 31, 34, 41â&#x20AC;&#x201C;43, 59] and the spinal cord [2, 6, 11, 13, 15, 16, 18, 31, 34, 36, 41â&#x20AC;&#x201C;43, 59, 66] during the course of treatment (results are listed in Table 6). Zhao et al.[6] found the highest dose increase in D1% of the spinal cord and the brainstem of on average 0.20 and 0.09 Gy per fraction, which would result in an accumulated excess dose of 5.6 and 2.5 Gy, respectively, for the entire treatment course. Cheng et al. [16] reported that if no replanning was performed, for 11% and 16% of the patients the tolerance dose of 54 Gy for the maximum dose to the brainstem was exceeded after 30 and 50 Gy, respectively, and for 11% of the patients the maximum dose to the spinal cord was higher than 45 Gy after both 30 and 50 Gy.

3.1. Factors correlating with brainstem and spinal cord dose increase A number of variables correlated significantly with an increase in dose parameters (D2%, Dmax) to the spinal cord, including changes in GTV volume, weight loss, change in neck diameter at the level of the thyroid notch and a few other anatomic, CT related variables (Table 2).

TABLE 2 Studies reporting on parameters associated with contour reduction (anatomic endpoints) and dosimetric changes of spinal cord and mandible (dosimetric endpoints) during the course of radiotherapy. Only statistically significant correlations are shown. Results of Spearman correlation were denoted with Ď , Pearson correlation with R.

84 CHAPTER 5 - Literature review

85

84

Fiorino et al. [40]

20

10

20

15

Capelle et al. [36]*

Castadot et al. [11]

Nishi et al. [4]

Wang et al. [59]

n.r. n.r. n.r. n.r. n.r.

ρ = 0.27

R= 0.27 R= 0.30 R= 0.17-0.27 R= 0.18 R= 0.26

Absolute neck thickness reduction at C2 level

Spinal cord Dmax increase

Mandible V60 increase

Mandible vector increase Reduction of lateral neck diameter at mandibular joint

Reduction of lateral neck diameter at C4-C6

ρ= 0.73 ρ= 0.71 0.75, correlation measure n.r. ρ=0.91 ρ= 0.652

Spinal cord Dmax increase Normal tissue V50 increase Spinal cord D2% increase Spinal cord D2% increase Spinal cord Dmax increase

Weight loss

ΔGTVp (cm3)

GTV shrinkage slope (cc/day)

p< 0.05

n.r.

p= 0.001

p< 0.0001 p< 0.0001

p= 0.001-0.002 p= 0.013-0.039 p= 0.004-0.028 n.r. n.r. n.r.

Mandible overdose

Anatomic isocenter vector RL Cochlea/ incisive AP, RL, roll Mandible vector/yaw/roll/pitch

Reduction of lateral neck diameter at the level of the thyroid notch

p= 0.024-0.054 p= 0.034 p= 0.002-0.043 p= 0.001-0.005 p= 0.001 p= 0.001 n.r. n.r. n.r. n.r. n.r. n.r.

Spinal cord Dmax overdose (Dmax >45Gy)

Cochlea/incisive roll, pitch, RL, AP Mandible roll, yaw C3-C7 vector C2-6 pitch C2-C5 AP Lordosis

ΔGTV (cm3)

Reduction of lateral neck diameter at midtumor level

PG density decrease

p= 0.0005

ρ= 0.917 and 0.936, respectively, p< 0.01 Spearman two-tailed correlation

External contour volume reduction at level of C2 and at base of skull

Weight loss

*only the most predictive parameters were listed.

Pts= patients, PG= parotid gland, n.r.= not reported, AP=anterior-posterior, SI=superior-inferior, RL=right-left, GTVp= the volume of primary gross tumour.

23

Ahn et al. [31]

Dosimetric endpoints:

14

p value

Correlation/ Association

Endpoint

# Pts Parameter

Barker et al. [33]

Anatomic endpoints:

Study

5

86 CHAPTER 5 - Literature review

46

15

33

20

85

24

33

31

Marzi et al. [48]

Nishimura et al. [50]

Teshima et al. [55]

Sanguineti et al. [68]

Schwartz et al. [7]

Senkus-Konefka et al. [54]

You et al. [61]

# Pts

Belli et al. [67]

Anatomic endpoints:

Study

Degree of mucositis

Duration of PEG tube use

Time to reduction of the acute xerostomia grade (CTCAE v3.0) (from ≥ grade 2 to grade 1)

Saliva reduction amount (g)

Xerostomia score at 3-4 months

ΔNTCP grade 3 or higher RTOG toxicity 12 months after RT*

Mean xerostomia (CTC v3.0) score (treatment course) ≥ 1.57

Endpoint

Weight loss>5% and/or decrease Acute xerostomia ≤ grade 1 vs. of neck diameter >10% grade 2

Lateral dimension changes at beam axis

PG shrinkage (%) end of treatment

ΔPG (%) mid treatment

PG volume ratio post-RT/pre-RT (%)

Initial volume of PG ΔPG (%)

ΔGTV (cm3) ΔPG (%)

ΔPG rate (mm3/day) ΔDensity rate PG (HU/day)

Parameter

n.r.

r= n.r.

p= 0.02, t-test

p= 0.017

p= 0.025

p= 0.024

HR= 1.034 (1.004-1.064), multivariable analysis

ρ= n.r., two-tailed nonparametric Spearman

p< 0.01

p= 0.040 p= 0.186

p= 0.074 p= 0.010

p= 0.02 p= 0.04

p value

ρ= -0.79, Spearman rank correlation and Fisher exact test

ρ= n.r. ρ= n.r.

r2= 0.609, stepwise multiple regression

OR= 0.10 (0.03–0.93) OR= 0.15 (0.99-1.00), Logistic univariable analysis

Association

Studies reporting on side effects or quality of life in relation to anatomic or dosimetric alterations during radiotherapy. Only significant associations are shown. Results of Spearman correlation were denoted with ρ, linear regression analysis with r or r2 and odds ratio by OR.

TABLE 3

18

129

33

Hunter et al. [65]

Yang et al. [5]

Zhao et al. [6]

TxN2,3 replanning vs. no replanning

IMRT replanning vs. no replanning

PG Dmean (planned) PG Dmean (delivered)

IMRT replanning vs. no replanning in over-irradiated PG group

p< 0.0007 p< 0.0004 p= 0.012, ANOVA p= 0.000 p= 0.000 p= 0.031 p= 0.000 p= 0.000 p= 0.015 p= 0.05, Mann Whitney Wilcoxon

Ď = -0.55 Ď = -0.57 n.r. n.r. n.r. n.r. n.r. n.r. n.r. n.r. n.r.

Stimulated selective PG salivary output 6 months post-treatment (cc/min) Global quality of life** Speech problems Trouble with social contact Teeth Opening mouth Dry mouth Sticky saliva Mucosa Xerostomia

p= 0.04

p< 0.01

n.r.

Xerostomia risk*

* Hypothetical difference in NTCP, assessed by LKB model. ** EORTC QLQ-C30 scales role functioning, social functioning, dyspnea, appetite loss, constipation and diarrhea all statistically significant.

PG= parotid gland, Pts= patients, n.r.= not reported, OR= odds ratio (95% confidence intervals).

15

Castelli et al. [63]

Dosimetric parameters:

5

87

TABLE 4 Studies reporting on parotid volume loss during the course of radiotherapy. Pts= patients, fx= fractions, ipsi= ipsilateral, contra= contralateral, - = not reported.

Study

# Pts Mean Parotid volume loss Time point

Weight loss

Correlation Parotid volume loss â&#x20AC;&#x201C; Weight loss?

Nishimura et al. [50] 33

26% (bilateral volume)

10-20 fx

-

-

Nishi et al. [4]

20

18%

10-20 fx

-

-

Ahn et al. [31]

23

24%

11 fx (mean)

mean 8%

YES

Capelle et al. [36]

20

20%

15 fx

mean 5%

-

Duma et al. [13]

11

18% (left) and 2% (right)

16 fx (median)

median 3%

NO

Sanguineti et al. [62] 85

32%

16 fx (median)

median 9%

YES

Schwartz et al. [7]

24

16%

16 fx (median)

-

YES

Wang et al. [59]

15

~20%

18 fx

median 8%

YES

Zhao et al. [6]

33

7% (ipsi) and 1% (contra)

< 20 fx

-

-

Lu et al. [47]

43

36%

20 fx

mean 13%

NO

Castadot et al. [37]

10

23% (contra) and 20% (ipsi)

20-25 fx

-

-

Height et al. [42]

10

median 24% (contra) and 21% (ipsi)

20-25 fx

median 3%, mean 3%

NO

Vasquez-Osorio et al. [57]

10

17% (irradiated) and 5% (spared)

23 fx

-

-

Duma et al. [38]

10

14%

25 fx

mean ~6-8%

NO

Cheng et al. [16]

19

28% (ipsi) and 13% (contra)

25 fx

mean 9%

-

Cozzolino et al. [66]

10

34% (ipsi) and 40% (contra)

25 fx

mean 5%

NO

Wu et al. [12]

11

14%

25-30 fx

-

-

Ho et al. [43]

10

28% (ipsi) and 25% (contra)

25-30 fx

mean 8%

YES

Ajani et al. [32]

13

36%

25-30 fx

median 7%

YES

Castelli et al. [63]

15

28%

25-30 fx

-

-

Hansen et al. [2]

13

19%

29 Âą 9 fx

4-14% (range)

-

Richetti et al. [53]

26

29%

30-35 fx

median 11%

-

Fung et al. [41]

10

33%

30-35 fx

-

-

Barker et al. [33]

14

median 28%

end of treatment

median 7%

-

88 CHAPTER 5 - Literature review

Study

# Pts Mean Parotid volume loss Time point

Weight loss

Correlation Parotid volume loss â&#x20AC;&#x201C; Weight loss?

Robar et al. [17]

15

~30%

end of treatment

mean 8%, median 7%

-

Lee et al. [45]

10

median 21 %

end of treatment

median 3%

-

Wang et al. [60]

82

27%

end of treatment

-

-

Bhide et al. [34]

20

~35%

end of treatment

mean 10%

-

Broggi et al. [35]

87

26%

end of treatment

median 8%

YES

Chen et al. [15]

20

45%

end of treatment

-

-

Fiorino et al. [40]

84

~30%

end of treatment

-

-

Fiorentino et al. [39] 10

58% (ipsi)

end of treatment

-

-

Marzi et al. [48]

15

41%

end of treatment

mean 7%

-

Schwartz et al. [14]

22

26%

end of treatment

-

-

Hunter et al. [65]

18

Median 13%

end of treatment

median 6%

-

Tomitaka et al. [56]

15

29%

2 wk post treatment

-

-

Jensen et al. [44]

72

median 43% (ipsi) and 36% (contra)

5-35 fx

median loss of volume 7%

-

5

89

TABLE 5 Studies reporting on anatomic changes of other organs at risk. Pts= patients, (C)RT= (chemo)radiotherapy

Study

# Pts Organ

Anatomic change

Time point

Volume change (%) Cozzolino et al. [66]

10

larynx

+14.9

25th fraction

Muscle area change, high dose side (%/year) Nichol et al. [49]

14

masseter muscle medial pterygoid

+3.9 +2.3

>1 year post RT

> 50 Gy: thickness change (%) Popovtzer et al. [52]

12

pharyngeal constrictors sternocleidomastoid muscles

+111 -9

3 months post CRT

Volume change (%) Richetti et al. [53]

26

contralateral masticatory muscles ipsilateral masticatory muscles contralateral sternocleidomastoid muscle ipsilateral sternocleidomastoid muscle thyroid gland constrictor muscles larynx

Week 7 RT

-8.2 -5.9 -7.8 -8.4 -8.7 +16.9 +15.7

TABLE 6 Studies reporting on dosimetric changes of submandibular glands, spinal cord, brainstem and other organs at risk.

Study

# Pts Organ

Dosimetric change

Time point

Ahn et al. [31]

23

spinal cord Dmax brainstem Dmax mandible V60

+1.3 -1.5 -3.3%

33th fraction

Bhide et al. [34]

20

spinal cord brainstem

+2.4 +2.4

end of treatment (week 5)

Capelle et al. [36]

20

spinal cord Dmax

+0.2

15th fraction

Castadot et al. [11]

10

submandibular gland Dmean spinal cord D2% oral cavity Dmean larynx D5% mandible D2%

+0.9 +0.8 +0.7 -0.2 -0.7

21-25 fractions

Chen et al. [15]

20

spinal cord Dmax brainstem Dmax

+0.4 +3.7

end of treatment (week 6)

90 CHAPTER 5 - Literature review

dose difference repeat imaging - planning (Gy)

Study

# Pts Organ

Dosimetric change

Time point

Cheng et al. [16]

19

spinal cord Dmax brainstem Dmax mandible D1% mandible Dmean optic chiasm Dmax optic chiasm D1% ipsilateral optic nerve Dmax ipsilateral optic nerve D1% contralateral optic nerve Dmax contralateral optic nerve D1% ipsilateral eyeball Dmax ipsilateral eyeball D1% contralateral eyeball Dmax contralateral eyeball D1% ipsilateral lens Dmax ipsilateral lens D1% contralateral lens Dmax contralateral lens D1%

+2.0 +2.7 +1.9 +0.8 +2.9 +2.2 +7.8 +7.5 +10.2 +9.9 +4.6 +4.2 -0.9 -0.4 +1.4 +1.3 +0.1 0.0

25th fraction

Cozzolino et al. [66]

10

larynx Dmean larynx Dmax spinal cord Dmax mandible Dmax

+1.3 +1.9 +1.4 +2.3

25th fraction

Duma et al. [13]

11

spinal cord Dmax oral cavity larynx

+0.3 +0.5 +0.6

16 (9-21) fractions

Fung et al. [41]

10

spinal cord D1% brainstem D1%

+4.0 +3.7

34-36 fractions

Graff et al. [18]

10

spinal cord D1% brainstem D1% cochlea Dmean mandible D1% larynx Dmean

+1.6 +1.2 +0.4 +0.5 +0.3

average variation in dose across all fractions of MVCBCT

Hansen et al. [2]

13

mandible Dmax mandible V60 mandible V70 spinal cord brainstem

-7.9 +7.2% +4.5% -2.4 -3.3

29th fraction (mean)

Height et al. [42]

10

spinal cord Dmax brainstem

+0.5 -0.4

20-25 fractions

Ho et al. [43]

10

spinal cord Dmax brainstem Dmax

+1.1 +0.3

average variation in dose across weeks 1-6

Wang et al. [59]

15

spinal cord Dmax brainstem Dmax

+2.6 +2.1

18th fraction

Zhao et al. [6]

33

spinal cord D1% brainstem D1%

+3.0 +1.4

15th fraction

dose difference repeat imaging - planning (Gy)

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5

4. Other organs at risk The results of studies regarding anatomic and/ or dosimetric changes of the larynx, masseter muscle, medial pterygoid, pharyngeal constrictor muscles, sternocleidomastoid muscles, masticatory muscles, thyroid gland, mandible, submandibular glands, oral cavity, optic chiasm, optic nerve, eyeball, lens and cochlea are listed in Tables 5 and 6, respectively [2, 11, 13, 16, 18, 31, 49, 52, 53, 66]. Clear swelling of the larynx and pharyngeal constrictor muscles was found by Ricchetti et al. [53] at week seven of radiotherapy. Swelling of the larynx was also reported by Cozzolino et al. [66], five weeks after the start of radiotherapy. Popovtzer et al. [52] observed a thickness change of +111% in parts of the pharyngeal constrictors that received more than 50 Gy, three months post chemoradiation. However, the thickness and volume of the sternocleidomastoid muscles decreased [52, 53].

In most publications, only small changes in the mean dose (< 1 Gy) to organs at risk during the course of radiotherapy have been reported [11, 13, 18] (Table 6). Dosimetric changes to e.g. the mandible are more often caused by head rotation instead of anatomic changes [12]. In contrast, a rather large increase in maximum and D1% was found by Cheng et al.

[16] for some organs after 30 and 50 Gy, with the largest dose increase for the optic nerves after 50 Gy (Dmax increase from 48.5 ± 4.5 to 56.3 ± 5.0 Gy (ipsilateral) and from 26.7 ± 16.1 to 36.9 ± 20.8 Gy (contralateral) (mean ± SD)) (Table 6). This means that for individual cases, the maximum dose to the optic nerve could have been > 60 Gy if no replanning was performed, with a markedly increased risk of toxicity as a consequence [72].

Discussion This is the first review that focused on anatomic and dosimetric changes of head and neck organs at risk during the course of radiotherapy and on the correlation of these changes with side effects. In total, fifty-one papers on this subject were identified according to the PRISMA methodology [20].

The parotid gland was the most studied OAR and showed the largest volume changes during radiotherapy (26% average volume decrease). The dosimetric consequences of PG shrinkage varied widely, with on average a PG mean dose increase of 2.2 ± 2.7 Gy. Only a few studies investigating volume changes of the submandibular glands were found. This could be explained by their location, encompassed by neck lymph node level IB and in close proximity to the frequently irradiated neck lymph node level II. This complicates their sparing and results in high initial submandibular gland doses, possibly less sensitive for dosimetric variations.

92 CHAPTER 5 - Literature review

Introduction of adaptive radiotherapy ART for head and neck patients is currently subject to many studies. Theoretically, ART could be performed by adapting treatment plans to the actual patient anatomy on a daily basis.

Yan and Liang showed in a retrospective planning study of nineteen patients that weekly adaptive inverse planning optimisation already improved the dose distribution of head and neck cancer treatment significantly [8]. Chen et al. [73] suggested from their retrospective study that for appropriately selected patients the theoretical benefits i.e. improved dose distributions of ART may be associated with actual clinical advantages. The authors also concluded that routine replanning is probably not necessary. This is confirmed by our own experience, based on weekly repeat CT scans, showing that in less than 20% of all head and neck patients replanning was needed because of target underdosage or OAR overdosage, usually during the first three weeks of treatment (personal communication).

Only limited data is published so far on the effect of dosimetric changes on the occurrence of side effects or quality of life (current review, Table 3). The optimal frequency and utili­ zation, as well as the ultimate clinical impact of ART still remain to be determined [9, 14, 25]. There are several indications that anatomic changes are more pronounced in the first half of treatment, and therefore repeated imaging and replanning should be performed in this first time period [34, 60, 62]. Schwartz et al. [14] conducted a prospective clinical trial, and concluded that properly timed replanning could result in dosimetric improvement, but that the clinical impact of ART remains to be confirmed. Dawson and Sharpe [25] stated that increased precision and accuracy of radiotherapy are expected to augment tumour control and reduce the incidence and severity of toxic effects after radiotherapy. On the other hand, they mentioned that the desire for sub-millimetre technical precision needs to be balanced with risk of chasing only modest clinical gains and the possibility of imposing an unacceptable workload on radiotherapy planning, delivery, and review processes. There are limited resources at most radiotherapy departments, and identification of patients that benefit from ART would restrict the workload tremendously and thus would be more cost-effective. Results of future prospective clinical trials linking anatomic and dosimetric changes to outcome are needed to confirm the clinical impact of ART.

Application of selection criteria for ART The potential selection criteria for PG volume loss identified in the current review that may improve the selection of patients for ART prior to treatment were tumour location (nasopharyngeal carcinoma), age, body mass index, planned dose to the parotid glands, the initial parotid gland volume, and the overlap volume of the PG with lymph node metastases.

PG volume loss is in this case presumed to result in higher complication rates ([48, 55, 67], Table 3). This could be due to an increase in PG dose, or to a direct relationship between PG volume and complications [67, 68]. For the majority of patients, PG volume loss resulted in an increase of the mean dose to the PGs (Figure 2d). However, we could not confirm a

93

5

clear association between volume loss and mean dose increase (Figure 2d). This can be explained by the fact that the treatment plan and consequential dose distribution depend on many factors such as the location of the boost volume.

Apart from selection of patients prior to treatment, selection during treatment could be performed, based on either detected changes or by parameters related to these changes.

Weight loss, change in body thickness, reduction of lateral neck diameter, PG volume decrease, PG density decrease, distance between PG centres of mass and GTV volume decrease turned out to be potential selection criteria for either PG volume loss, increase of the PG dose and/or differences in the degree of side effects (Tables 1 and 3). Considering the other OARs, weight loss and GTV volume decrease during radiotherapy positively correlated with the maximum dose to the spinal cord (Table 2), and lateral dimension changes at beam axis correlated well with the degree of mucositis [54].

The difficulty with most of the potential selection criteria found in this review to select patients for ART is that although statistical significant associations were found with ana­tomic and/or dosimetric endpoints, these were generally weak (Tables 1 and 2). More­ over, most potential selection criteria resulted from correlation tests, although regression analysis is more suitable to investigate the predictive power. Furthermore, some authors reported Pearson correlation coefficients, although normal distribution of the data and the linear relationship between variables wasn’t proven. If a non-linear but monotonic relationship between variables is observed, the Spearman correlation should be used.

To enable proper selection of patients for re-planning, multivariable prediction models are needed, derived from sufficiently powered prospective study populations. Most studies in this review consisted of limited patient populations and were therefore not suitable for model development. The performance of such models should be sufficient to identify patients that need ART with high sensitivity and specificity.

Bias in individual studies It is likely that the large heterogeneity in results as depicted in Figure 2 can be explained by differences between study designs, such as different patient and tumour characteristics (localization, staging), differences in timing of repeated imaging, treatment (combination with surgery / chemotherapy), treatment technique (IMRT or tomotherapy), margins, specific settings of the radiotherapy treatment planning, and patients’ nutritional status.

Another problem in the comparison of the results of the different studies is that different types of analysis were used, and results were reported in different quantities (e.g. volume changes in % or cm3, dose changes in % of prescribed dose or in Gy). Also, individual patient data and standard deviations of the differences were often lacking. These

94 CHAPTER 5 - Literature review

afore­mentioned differences in study design and the variation in methods of analysis and reported quan­tities are one of the most common problems in systematic reviews of prog­nostic studies [74]. It is therefore not possible to perform a meta-analysis, but it is a strong argument in favour of systematic reviews [74]. On the individual study level, random allocation of patients is seldom performed. Often, detailed study inclusion criteria are lacking, and some of the studies are retrospective, most likely selecting patients that suffer most from anatomic and dosimetric changes during treatment. Two studies from China investigated the effect of replanning on quality of life [5, 6] (Table 3). In both studies, significant lower incidences of side effects were found in the replanning group with respect to the no replanning group. Conversely, patients weren’t randomly allocated to the different treatment groups, and therefore differences in these specific studies might be explained by other factors such as differences in socioeconomic status between the two populations [75].

Inaccuracies in the calculation of dosimetric changes Two factors of inaccuracy in the ART chain that influence the accuracy of the calculation of dose on repeat imaging are contouring variability and the dose recalculation procedure on (MV)CBCT. Earlier studies showed that the coefficients of variance for contouring OARs varied between 12-16% [76] resulting in dose differences of on average 3.5 Gy [77]. Auto­ matic contouring on MVCBCT was found to be comparable to inter-observer uncertainty in delineating parotid glands on CT [78]. Morin et al. [79] showed that dose calculation on MVCBCT could be performed within a 3% / 3mm accuracy. In kV-CBCT imaging the dosimetric error depends on the HU adjustment technique and imaging artefacts. For the spinal cord Dmax¬ the error could be >5% [80]. Some dose differences found in this review are thus in the same order of magnitude as differences that could be introduced because of contouring variability or dose calculation inaccuracy. For instance given a mean dose of 30 Gy to the PG, a deviation of 3% would result in a deviation of 0.9 Gy. We should therefore pay attention to select the most accurate and reproducible segmentation and dose calculation procedures to most accurately predict actual given doses.

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5

Conclusions Despite the relatively large number of studies published so far, the heterogeneity between studies prevented unambiguous conclusions on how to select patients for adaptive radio­ therapy in head and neck cancer. Still, a number of potential selection criteria for anatomic and dosimetric changes were identified that could be included in well-designed and wellpowered studies on anatomic and dosimetric changes during radiotherapy, including tumour location (nasopharyngeal carcinoma), age, body mass index, planned dose to the parotid glands, the initial parotid gland volume, and the overlap volume of the parotid glands with the target volume. There is a need for larger prospective studies including assessment of anatomic and dosimetric changes, and to identify possible relationships between these changes and outcome. Moreover, we hope to draw attention to the paucity of good quality data on this subject so far, and therewith improve the quality of future research.

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57. Vásquez Osorio EM, Hoogeman MS, Al-Mamgani A, et al. Local anatomic changes in parotid and submandibular glands during radiotherapy for oropharynx cancer and correlation with dose, studied in detail with nonrigid registration. Int J Radiat Oncol Biol Phys. 2008;70:875– 882.

58. Wang W, Yang H, Hu W, et al. Clinical study of the necessity of replanning before the 25th fraction during the course of intensity-modulated radiotherapy for patients with nasopharyngeal carcinoma. Int J Radiat Oncol Biol Phys. 2010;77:617–621.

59. Wang X, Lu J, Xiong X, et al. Anatomic and dosimetric changes during the treatment course of intensity-modulated radiotherapy for locally advanced nasopharyngeal carcinoma. Med Dosim. 2010;35:151–157.

60. Wang Z-H, Yan C, Zhang Z-Y, et al. Radiation-induced volume changes in parotid and submandibular glands in patients with head and neck cancer receiving postoperative radiotherapy: a longitudinal study. Laryngoscope. 2009;119:1966–1974.

61. You SH, Kim SY, Lee CG, et al. Is there a clinical benefit to adaptive planning during tomotherapy in patients with head and neck cancer at risk for xerostomia? Am J Clin Oncol.

2012;35:261–266.

62. Sanguineti G, Ricchetti F, Thomas O, et al. Pattern and predictors of volumetric change of parotid glands during intensity modulated radiotherapy. Br J Radiol. 2013;86:20130363.

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63. Castelli J, Simon A, Louvel G, et al. Impact of head and neck cancer adaptive radiotherapy to spare the parotid glands and decrease the risk of xerostomia. Rad Oncol. 2015;10:1–10.

64. Reali A, Anglesio SM, Mortellaro G, et al. Volumetric and positional changes of planning target volumes and organs at risk using computed tomography imaging during intensitymodulated radiation therapy for head–neck cancer: an “old” adaptive radiation therapy approach. Radiol Med. 2014;119:714–720.

65. Hunter KU, Fernandes LL, Vineberg KA., et al. Parotid glands dose-effect relationships based on their actually delivered doses: Implications for adaptive replanning in radiation therapy of head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2013;87:676–682.

66. Cozzolino M, Fiorentino A, Oliviero C, et al. Volumetric and Dosimetric Assessment by Conebeam Computed Tomography Scans in Head and Neck Radiation Therapy: A Monitoring in Four Phases of Treatment. Technol Cancer Res Treat. 2014;13:325–335.

67. Belli ML, Scalco E, Sanguineti G, et al. Early changes of parotid density and volume predict modifications at the end of therapy and intensity of acute xerostomia. Strahlenther Onkol.

2014;11:1001–7.

68. Sanguineti G, Ricchetti F, Wu B, et al. Parotid gland shrinkage during IMRT predicts the time to Xerostomia resolution. Rad Oncol. 2015;10:15–20.

69. Dijkema T, Raaijmakers CPJ, Ten Haken RK, et al. Parotid gland function after radiotherapy: the combined michigan and utrecht experience. Int J Radiat Oncol Biol Phys. 2010;78:449– 53.

70. Beetz I, Schilstra C, van der Schaaf A, et al. NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: the role of dosimetric and clinical factors. Radiother Oncol. 2012;105:101–6.

71. Deasy JO, Moiseenko V, Marks L, et al. Radiotherapy dose-volume effects on salivary gland function. Int J Radiat Oncol Biol Phys. 2010;76:S58–63.

72. Mayo C, Martel MK, Marks LB, et al. Radiation dose-volume effects of optic nerves and chiasm. Int J Radiat Oncol Biol Phys. 2010;76:S28–35.

73. Chen AM, Daly ME, Cui J, et al. Clinical outcomes among patients with head and neck cancer treated by intensity-modulated radiotherapy with and without adaptive replanning. Head Neck. 2014;36:1541–46.

74. Altman DG. Systematic reviews of evaluations of prognostic variables. BMJ. 2001;323: 224–8.

75. Levy A, Blanchard P, Daly-Schveitzer N, et al. Replanning during intensity modulated radiation therapy improved quality of life in patients with nasopharyngeal carcinoma: in regard to Yang et al. Int J Radiat Oncol Biol Phys. 2013;86:811.

76. Brouwer CL, Steenbakkers RJHM, van den Heuvel E, et al. 3D Variation in delineation of head and neck organs at risk. Rad Oncol. 2012;7:32.

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77. Brouwer CL, Steenbakkers RJHM, Gort E, et al. Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP. Radiother Oncol. 2014;111:148-52.

78. Faggiano E, Fiorino C, Scalco E, et al. An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy. Phys Med Biol.

2011;56:775–91.

79. Morin O, Chen J, Aubin M, et al. Dose calculation using megavoltage cone-beam CT. Int J Radiat Oncol Biol Phys. 2007;67:1201–1210.

80. Fotina I, Hopfgartner J, Stock M, et al. Feasibility of CBCT-based dose calculation: comparative analysis of HU adjustment techniques. Radiother Oncol. 2012;104:249–256.

102 CHAPTER 5 - Literature review

CHAPTER

6

eometric and dosimetric variability in head and neck organs at risk during the course of radiotherapy: Patient selection for adaptive radiotherapy

Published as: Selection of head and neck cancer patients for adaptive radiotherapy to decrease xerostomia Brouwer CL, Steenbakkers RJ, Van der Schaaf A, Sopacua CTC, Van Dijk LV, Kierkels RGJ, Bijl HP, Burgerhof JGM, Langendijk JA, Sijtsema NM. Radiother Oncol. 2016 Jul;120(1):36-40.

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Abstract Background and Purpose The aim of this study was to develop and validate a method to select head and neck cancer patients for adaptive radiotherapy (ART) pre-treatment. Potential pre-treatment selection criteria presented in recent literature were included in the analysis.

Materials and methods Deviations from the planned parotid gland mean dose (PG Î&#x201D;Dmean) were estimated for 113 head and neck cancer patients by re-calculating plans on repeat CT scans.

Uni- and multivariable linear regression analyses were performed to select pre-treatment parameters, and ROC curve analysis was used to determine cut off values, for selecting patients with a PG dose deviation larger than 3 Gy. The patient selection method was validated in a second patient cohort of 43 patients.

Results After multivariable analysis, the planned PG Dmean remained the only significant parameter for PG Î&#x201D;Dmean. A sensitivity of 91% and 80% could be obtained using a threshold of PG Dmean of 22.2 Gy, for the development and validation cohorts, respectively. This would spare 38% (development cohort) and 24% (validation cohort) of patients from the labourintensive ART procedure.

Conclusions The presented method to select patients for ART pre-treatment reduces the labour of ART, contributing to a more effective allocation of the department resources.

104 CHAPTER 6 - Patient selection for adaptive radiotherapy

Introduction During the course of head and neck radiotherapy, anatomical changes such as body weight and/or tumour volume may result in underdosage or dose inhomogeneity in targets, and overdosage in organs at risk (OARs) [1–4]. The largest dose differences between (estimated) delivered and planned OAR dose that have been reported are for the parotid glands (PGs).

However there is a substantial difference in findings between studies, the median of the mean dose difference over 25 studies is 1.7 [interquartile range -1.9;10.4] Gy [5]. A larger PG dose than planned will increase the risk of xerostomia with subsequent deterioration of quality of life [6]. Adaptive radiotherapy (ART) is a strategy used to limit or even decrease the dose to the PGs. ART, however, comprises a labour intensive procedure, requires additional imaging and does not lead to a clinically relevant benefit for all patients [7]. It would there­ fore be helpful if the patients with expected clinically relevant PG dose deviations could be selected prior to radiotherapy. With such a method in place, the selected patients would receive an ART procedure to monitor and/or minimize the delivered PG dose. The non-­ selected patients would be spared from this extensive procedure. Many attempts have been made to find parameters associated with anatomical and dosimetric changes of PGs [5], but there is no general consensus yet on how to select patients for ART to decrease xerostomia.

The aim of this study is therefore to develop a method using pre-treatment parameters to predict dose deviations from the planned PG mean dose, which can be used to select patients for ART pre-treatment. Two different patient cohorts were used to develop and validate the method, respectively.

6

Materials and Methods Patient cohort A One-hundred and thirteen head and neck cancer patients were enrolled in a previous prospective cohort study [8–11]. All patients were treated between 2008 and 2012 with curative intent. They received primary conventional three-dimensional conformal RT (3D-CRT) or intensity-modulated RT (IMRT) up to a dose of 70 Gy in fractions of 2 Gy delivered over 6-7 weeks (5 or 6 fractions per week), following ICRU recommendations, either alone or in combination with concomitant chemotherapy (chemoradiation) or cetuximab (bio-radiation). All patients received a planning computed tomography scan

105

(plan-CT) as well as a post-radiotherapy response CT scan (post-CT) in the treatment position, acquired 6 weeks after RT with a slice thickness of 2 mm. This cohort was used to develop the patient selection method.

Patient cohort B Data from 43 patient plans were used to validate the patient selection method. This patient group was treated in our department in 2014-2015 with definitive radiotherapy or concurrent chemoradiation or bio-radiation using IMRT or Volumetric Modulated Arc Therapy (VMAT). The dose prescription was up to 70 Gy in fractions of 2 Gy delivered over 6-7 weeks (5 or 6 fractions per week) according to ICRU recommendations. For each patient, the plan quality was monitored during treatment by recalculation on weekly repeated CT scans. In cases of relevant dose deviation (repeat-CT with respect to the plan-CT, as judged by the treating physician), the treatment plan was adapted. All CT scans were acquired in the treatment position, with a slice thickness of 2 mm.

Parotid gland dose deviations For both cohort A and B, the PGs were delineated on the plan-CT by a dedicated radiation therapist, and were warped to the post- and repeat-CTs respectively by deformable image registration using Mirada RTx (Mirada Medical Ltd., Oxford, UK). The warped PG contours were manually corrected if necessary. For cohort B, 2 ipsilateral PGs were excluded because of tumour invasion.

For cohort A, the clinical treatment plan was re-calculated on the post-CT. Subsequently, ΔDmean[A] of the PG for each patient was the mean dose of the PG on the post-CT minus that of the planned mean dose : ΔDmean[A] = Dmean_post[A] - Dmean_plan[A]. Since previous studies showed that the volume of the parotid gland does not significantly change after the last fraction of RT [12, 13], we assume that ΔDmean[A] is an accurate estimate of the dose deviation between end and start of treatment.

For cohort B, the delivered dose was estimated by dose accumulation of the re-calculated dose distribution on weekly repeat-CT scans using deformable image registration (Raystation, Raysearch Laboratories AB, Stockholm, Sweden). Next, ΔDmean[B] for the PG per patient was calculated by subtracting the planned mean dose from the accumulated mean dose : ΔDmean[B] = Dmean_accumulated[B] - Dmean_plan[B].

Candidate pre-treatment factors Previously identified candidate pre-treatment factors [4, 5] that were considered in the analysis were: initial weight, BMI, age, chemotherapy (yes/no), surgery (yes/no), T-stage (T3+ vs. T3-), N-stage (N2+ vs. N2-), planned dose to the PG (mean dose and V20, V30 and V40), initial PG volume, initial gross tumour volume (GTV), tumour location (pharynx vs. other) as well as overlap volume (OV) of the PG with the target (high dose) and elective (low dose) planning target volume (PTV); OVPG-PTVhigh and OVPG-PTVlow.

106 CHAPTER 6 - Patient selection for adaptive radiotherapy

Statistical analysis The endpoint for the linear regression analysis was defined as the absolute value of ΔDmean, since anatomic changes can result in positive as well as negative dose deviations (see Figure 1), which are both of importance for a correct prediction of xerostomia.

To test whether pre-treatment parameters and endpoints significantly differed between cohort A and B, independent samples t-tests, Mann-Whitney U tests and Fisher’s exact tests were performed for normally distributed continuous variables, for continuous variables with skew distribution and for categorical variables, respectively. A p-value of ≤ 0.05 was considered statistically significant.

Univariable and multivariable linear regression analyses were applied to the endpoint |ΔDmean[A]| for the contralateral and the ipsilateral parotid gland. For the continuous explanatory variables we checked for linear relationship with the endpoint using scatter plots of the variables vs. the endpoint, for the final model, we checked normality and constant variance of the residuals. Pre-treatment factors with a p-value <0.2 in the univariable analyses were included in the multivariable analysis using forward selection (Likelihood ratio test, threshold p<0.05). If the pre-treatment factors had a Pearson mutual correlation (R) > 0.80, only the factor with the highest correlation to the endpoint was included in multivariable analysis. Model performance was scored with the coefficient of determination (R2).

The pre-treatment factor(s) from the final multivariable linear regression model were applied to the data to select patients for ART, i.e. patients with a | PG ΔDmean | > 3 Gy (both ipsiand contralateral PGs included), which was assumed to be the minimum level of clinical relevance. Three Gy would result in NTCP differences of 3-10% for xerostomia (depending on the applied model and the steepness of the curve for the particular dose value) which is assumed as a clinical relevant threshold to select patients for advanced treatments [15]. Cut off values were determined by means of receiver operating characteristic (ROC) curve analysis, for sensitivities of 70%, 80%, 90% and 100%. The cut off values found were applied to dataset B. The sensitivity, specificity, and positive and negative predictive value were calculated and used to assess the performance and efficiency of the method.

Statistical analysis was performed using Statistical Program for Social Sciences (SPSS Inc., Chicago, IL, USA) and the Statistics Toolbox in Matlab R2014a (MathWorks, Natick, MA, USA).

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FIGURE 1

Distribution of ΔDmean (Gy) against the initial Dmean (Gy) of the contralateral (left) and ipsilateral (right) parotid glands.

FIGURE 2 Fit for t he ipsilat eral parot id gland model (R = 0.621)

Fit for t he cont ralat eral parot id gland model (R= 0.759)

4

3

3

2

2

1

1

0

0

Real

Real

4

-1

-1

-2

-2

-3

-3

-4

-4

-5 -4

-3

-2

-1

0

Predicted

1

2

3

4

-5 -4

-3

-2

-1

0

Predicted

1

2

3

4

Resulting models from the multivariable linear regression analysis (Table 3) for ln|ΔDmean| of the contralateral (left) and ipsilateral (right) parotid gland. Predicted values (x-axis) are the results from the model, real values (y-axis) are the real data (ln|ΔDmean|).

108 CHAPTER 6 - Patient selection for adaptive radiotherapy

Results Patient characteristics of cohort A and B were significantly different regarding gender, weight, BMI, T-classification, N-classification, tumour location, use of chemotherapy, Dmean of the contra- and ipsilateral PG, GTV volume, and |ΔDmean| of the contralateral PG (Table 1).

The endpoint |ΔDmean| and the pre-treatment factor GTV volume were transformed by the natural logarithm to improve linearity and normality. In the univariable analysis, all pre-treatment factors were significantly associated (α=0.05) with the endpoint ln|ΔDmean| of the parotid glands (Table 2), with the exception of the initial patient weight (for the ipsilateral PG), age, surgery and initial PG volume.

The parameters included in the multivariable linear regression for both the contra- and ipsilateral PG were BMI (weight excluded due to the mutual correlation), chemotherapy, T-stage, N-stage, PG Dmean (PG V20, V30, V40 excluded due to the mutual correlation), tumour location, ln (GTV volume) and overlap PG-PTV56 (overlap PG-PTV70 excluded due to mutual correlation). From the multivariable linear regression analysis, the planned mean dose to the PG was the only significant factor (Table 3). The coefficient of determination for the final model was R2 = 0.59 (contralateral PG) and R2 = 0.39 (ipsilateral PG) (Figure2).

For 20% of the parotids in cohort A, |ΔDmean| of the parotid gland was higher than 3 Gy (Figure 3, Table 4). The results of the ROC curve analysis for sensitivities of 100, 91, 80 and 71% are shown in Table 4. For a sensitivity of 91%, the specificity was 45%, and 62% of the parotids would be selected for the ART procedure. The positive predictive value was 29%, meaning that 29% of the parotids that were selected for ART with our method were correctly classified. The negative predicted value was 95%.

The cut off values of PG Dmean (Gy) found for cohort A were applied to cohort B to validate the selection method, resulting in the performance measures for cohort B (Figure 3, Table 4).

For 18% of the parotids in cohort B, |ΔDmean| of the parotid gland was higher than 3 Gy (Figure 3, Table 4). Applying the cut off value for PG Dmean of 22.2 Gy, the sensitivity was 80% and 76% of the parotids would be selected for the ART procedure (Table 4). The positive predictive value was 19% and the negative predicted value 81%.

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TABLE 1 Patient characteristics of cohort A and B (percentages for categorical variables, means (sd) for normally distributed continuous variables and medians [inter quartile range] for continuous variables with skew distributions). PG= parotid gland, GTV= gross tumour volume, ipsi= ipsilateral, contra= contralateral. P-values resulting from independent samples t- test ((1), normally distributed continuous variables), Mann-Whitney U test ((2), continuous variables with skew distributions) and Fisher’s exact test ((3), categorical variables).

Statistically significant values presented in bold.

Cohort A

Cohort B

p-value

Gender Male Female

87% 13%

63% 37%

Age (y) Initial weight (kg) BMI (kg/m2)

61.0 (10.4) 85.7 (16.1) 27.2 (4.6)

59.8 (9.8) 77.0 (21.1) 24.5 (6.4)

Tumour classification T1-T2 T3-T4

69% 31%

29% 71%

Node classification N0-N1 N2a-N3

67% 33%

36% 64%

Tumour location/ primary site Pharynx Other

35% 65%

70% 30%

Chemotherapy yes no

20% 80%

58% 42%

PGcontra Dmean (Gy) PGipsi Dmean (Gy)

19.9 (14.1) 28.9 (20.4)

25.6 (11.0) 36.0 (13.2)

0.008(1) 0.012(1)

Initial volume PGcontra (cm3) Initial volume PGipsi (cm3) Initial GTV volume (cm3)

31.3 (9.2) 30.7 (10.1) 8.8 (18.3)

30.0 (11.5) 30.5 (12.6) 24.5 (33.9)

0.398(1) 0.738(1) 0.002(2)

PGcontra Δvolume (%) PGipsi Δvolume (%) |PGcontra ΔDmean| |PGipsi ΔDmean|

-17.4 (13.1) -18.0 (13.5) 0.8 (1.9) 1.3 (2.7)

-15.4 (12.5)* -17.1 (13.2)* 1.4 (2.1) 1.6 (1.0)

0.398(1) 0.738(1) 0.008(2) 0.633(2)

0.001(3)

0.489(1) 0.010(1) 0.029(1) <0.001(3)

0.001(3)

<0.001(3)

<0.001(3)

* difference in volume between start and final week of radiotherapy

110 CHAPTER 6 - Patient selection for adaptive radiotherapy

FIGURE 3

Cohort

Cohort A Cut off value 22.2 Gy

Cut off value 22.2 Gy

Cohort B Cut off value 22.2 Gy

6

Deviation from the planned parotid gland mean dose (|Î&#x201D;Dmean|) vs. the planned parotid gland mean dose. Selection of patients for |Î&#x201D;Dmean| > 3 Gy with 91% and 80% sensitivity could be performed by using a threshold of Dmean = 22.2 Gy, for cohort A and B, respectively (for more information refer to Table 4).

111

TABLE 2 Univariable linear regression for the endpoint ln|Î&#x201D;Dmean| of the contralateral (contra) and ipsilateral (ipsi) parotid glands. Statistically significant values presented in bold.

Pre-treatment factor Regression coefficient Standard error

p-value

Pearson R

Contra

Ipsi

Contra

Ipsi

Contra

Ipsi

Contra

Ipsi

BMI

-0.136

-0.172

0.041

0.043

0.0015

0.0001

-0.317

-0.380

Weight

-0.030

0.558

0.011

0.465

0.0136

0.2336

-0.240

0.121

Age

-0.018

-0.031

0.017

0.018

0.3053

0.0959

-0.096

-0.157

Chemotherapy

1.604

1.397

0.441

0.463

0.0004

0.0030

0.323

0.275

Surgery

-0.234

0.670

0.615

0.679

0.7044

0.3257

-0.036

0.094

T-stage

1.426

1.200

0.371

0.392

0.0002

0.0027

0.340

0.279

N-stage

1.685

1.678

0.356

0.371

<0.0001

<0.0001

0.407

0.395

PG Dmean

0.107

0.063

0.009

0.007

<0.0001

<0.0001

0.759

0.621

PG V20

0.029

0.038

0.004

0.004

<0.0001

<0.0001

0.531

0.691

PG V30

0.037

0.039

0.004

0.004

<0.0001

<0.0001

0.638

0.644

PG V40

0.041

0.037

0.007

0.005

<0.0001

<0.0001

0.483

0.569

PG volume

-0.017

-0.002

0.020

0.019

0.3899

0.9331

-0.080

-0.008

Ln(GTV volume)

0.781

0.698

0.121

0.134

<0.0001

<0.0001

0.529

0.453

Tumour location

1.720

1.325

0.362

0.391

<0.0001

0.0010

0.408

0.306

Overlap PG -PTV70

0.502

0.120

0.205

2.154

0.0159

0.0335

0.226

0.203

Overlap PG -PTV56

0.270

0.160

0.062

0.033

<0.0001

<0.0001

0.384

0.419

TABLE 3 Results of the multivariable linear regression analysis for the endpoint ln|Î&#x201D;Dmean| of the contralateral (contra) and ipsilateral (ipsi) parotid gland.

Regression coefficient Standard error

p-value

Contra

Ipsi

Contra

Ipsi

Contra

Ipsi

Intercept

-2.78

-2.22

0.231

0.264

<0.001

<0.001

PG Dmean

0.107

0.063

0.009

0.007

<0.001

<0.001

112 CHAPTER 6 - Patient selection for adaptive radiotherapy

FIGURE 4

Parotid gland volume change against the initial Dmean (Gy) of the contralateral and ipsilateral parotid glands (cohort A). Line represents the linear fit of the parotid mean dose vs. the parotid volume change for the ipsilateral parotid glands with initial parotid mean dose level < 60 Gy (R2=0,274).

TABLE 4 Performance of the classification of patients for |Î&#x201D;Dmean| of the parotid glands > 3 Gy by using the planned mean dose of the parotid glands (PG Dmean). n.a.= not applicable.

Cut off value PG Dmean (Gy)

0

3.6

22.2

24.7

27.0

Sensitivity (%)

100

100

91

80

71

Specificity (%)

0

33

45

50

60

% selected for ART

100

74

62

56

46

Positive predictive value (%)

20

27

29

29

30

Negative predictive value (%)

n.a.

100

95

91

89

Sensitivity (%)

100

100

80

60

40

Specificity (%)

0

0

25

38

46

% selected for ART

100

100

76

62

51

Positive predictive value (%)

18

18

19

17

14

Negative predictive value (%)

n.a.

n.a.

81

81

78

Cohort A (n=226)

6

Cohort B (n=82)

113

Discussion In univariable analysis, many of the pre-treatment parameters were significantly associated with the change in mean dose to the parotid gland (Table 2). Still, Pearson correlation with the endpoint was low for most of the parameters. The only parameter that remained significant in a multivariable analysis was the planned mean dose to the parotid gland. By selecting only patients with a planned PG mean dose ≥ 22.2 Gy for an ART procedure, 87% of patients in the validation cohort who needed replanning (i.e. having PG |ΔDmean| > 3 Gy) were selected (Table 4). This would spare 24% of patients in the validation cohort from the ART procedure, which would contribute to a more effective allocation of the department resources.

In our previous review study [5] we found a number of candidate pre-treatment parameters to identify patients that might benefit from ART. In seven studies, the PG mean dose was significantly associated with PG volume loss [12, 16–21], suggesting its potential as a predictor for dose changes. In the multivariable analysis of the current study, the direct relationship between the planned PG mean dose and deviations from the planned PG mean dose was confirmed (R2=0.59 and 0.39 for contra- and ipsilateral PG, respectively). The BMI [19] and initial parotid volume [16] were previously significantly associated with PG volume loss in multivariable analysis. In the current study these factors were only significantly correlated to PG volume loss in univariable analysis (Table 2), but not in multivariable analysis.

The inferior performance of the linear model for the ipsilateral PG compared to the contra­ lateral PG could be explained by the fact that the ipsilateral PGs are often encompassed by an (elective) target volume. Consequently, the ipsilateral PGs are more often receiving a high, homogenous dose, and are located further away from high dose gradients. Anatomical changes will therefore have little or no dosimetric impact for the ipsila­teral PGs receiving high doses (>~50 Gy). Furthermore, we found that PG volume loss was linearly associated with the PG mean dose up to a level of ~60 Gy, but for PG doses >60 Gy, little or no PG volume loss (i.e. anatomical changes) occurred (Figure 4).

Brown et al. [4] also attempted to predict the need for ART in head and neck cancer patients. The authors concluded from their multivariable analysis that oropharyngeal and nasopharyngeal patients with N2-3 disease, an initial weight > 100 kg and larger initial nodal sizes have a probability ≥ 80% of requiring replanning. In our multivariable analysis, only the mean dose to the PG persisted. While Brown et al. did not include this factor in their analysis, in our data the PG mean dose was significantly associated with N2-3 disease (R=0.51) and larger initial nodal sizes (R=0.43). Furthermore, the difference in findings between our study and those reported by Brown et al. can be explained by a number of

114 CHAPTER 6 - Patient selection for adaptive radiotherapy

reasons. First, Brown et al. used a different endpoint, i.e. ‘the need for replan’, which was defined by the treating radiation oncologist. As the authors stated in their discussion, the need for replan was mostly defined by the dose to the optic structures and brachial plexus.

In our study, the need for replan was determined by the dose to the parotid gland. Secondly, our study cohort contained fewer patients with oro- and nasopharyngeal cancer. Thirdly, our patients with pharyngeal cancer had lower initial weights, not exceeding the 100 kg threshold described by Brown et al.. It will be necessary to repeat the multivariable analyses in sufficiently large, properly selected patient cohorts, in order to untangle the confounding factors.

In the study by Brown et al. [4], only 5 of 110 patients (4.5%) were selected for replanning.

In our study, 18-20% of the patients were selected for replanning if their difference in mean dose to the PG was larger than 3 Gy. The question arises whether the arbitrary threshold of 3 Gy is the most clinically relevant threshold. In the linear range of the mean dose NTCP model (from a mean dose of ~25 up to 55 Gy) [14], a difference of 1 Gy in mean dose to the PG corresponds to a difference of about 3% in NTCP. For dose values <25 or >55 Gy however, a difference of 1 Gy in mean dose to the PG corresponds to a difference of about 1% in NTCP. Still, it is hard to prove that a dose increase of 3 Gy will result in more complications. Some authors [22, 23] compared the intensity of side effects and global quality of life of a group of patients selected for replanning with those without replanning.

They found significantly fewer side effects and improved quality of life for the replanned group. However, it should be noted that in these studies patients were not randomly allocated to the different strategies. Therefore the differences found in these studies can also be explained by other factors, such as differences in socioeconomic status between the two populations [24].

The strengths of our study are the relatively large number of patients included and the use of two independent patient groups for development and validation of the selection method.

We were therefore able to perform multivariable analysis to select the most important pre-­ treatment parameter(s) related to the endpoint, and eventually to validate this approach in an independent subsequent patient cohort.

A limitation of our study is the absence of imaging during the course of RT for cohort A. Therefore, the approximated delivered dose to the PG for cohort A may be overestimated.

However, we expect the post-CT scan acquired six weeks after RT to be a valid approximation for the anatomy of the parotid gland, since previous research showed no significant change in volume of the parotid glands 6-8 weeks after treatment in relation to the end of treatment [12, 13]. We choose to calculate ΔDmean[B] using all available imaging during the course of radiotherapy, to obtain the best approximation of the actual given dose. This approach is expected to result in lower values of ΔDmean than the approach taken in cohort A. The fact that resulting ΔDmean values for cohort B are even

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higher than the values in cohort A can be explained by the differences between the cohorts, i.e. tumour location and initial GTV volume (cohort B having a larger amount of pharynx patients, and a larger initial GTV volume).

The focus of this study was the dose deviations of the PG, for two reasons. The first is that previous studies have shown that this organ receives the largest dose deviations [1-3]. The second is because the dose to the PG is related to one of the most important long term side effects of head and neck radiotherapy. At present, we are conducting studies investigating the consequences of dose inhomogeneities of target volumes, and potential overdosage of other organs at risk due to anatomical changes. The same method as described in this study could be used to study pre-treatment parameters associated with dose changes and to classify patients suitable for ART. Absolute changes from the planned PG mean dose were considered in this study, i.e. positive as well as negative dose changes. If the dose change to the PG is positive, a plan adaptation is needed to prevent a higher risk of xerostomia than predicted at the planning stage. Although negative dose changes result in a lower risk of xerostomia, it is also important to monitor since the original treatment plan might become compromised, requiring further optimization of the target volume or other organs at risk.

The patient characteristics of patient cohort A and B showed several significant differences, with regard to tumour location, tumour and node classification, chemotherapy, GTV volume and planned PG mean dose and |Î&#x201D;Dmean| of the contralateral PG (Table 1). The performance of the classification of patients for PG |Î&#x201D;Dmean| > 3 Gy was moderate for cohort B (Table 4). With the low threshold of 3.6 Gy, all patients were selected for ART (the minimum PG mean dose was 5.4 Gy). With high thresholds however (24.7 and 27.0 Gy), the sensitivity was only 60% and 40% respectively, which would generally not be accepted in clinic. Nevertheless, the threshold of 22.2 Gy is applicable to both cohort A and B with similar sensitivity (91% and 80%, respectively). Therefore a threshold for the mean dose to the parotid gland of 22.2 Gy seems optimal for the selection of head and neck cancer patients for ART, with reasonable overall performance (Table 4). The sensitivity level accepted is obviously discussable, and depends on the desired level of accuracy and the availability of resources for ART.The somewhat disappointing performance of the selection tool in general pleads for future studies using more specific patient populations, i.e. with focus on a specific tumour location.

In summary, we have presented a method to select patients for ART pre-treatment by using the planned mean dose to the parotid gland. Additional studies focusing on dosimetric changes to target volumes and organs at risk in large specific patient cohorts could help to further specify appropriate parameters to select patients for ART.

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References 1. Wu Q, Chi Y, Chen PY, et al. Adaptive replanning strategies accounting for shrinkage in head and neck IMRT. Int J Radiat Oncol Biol Phys. 2009;75:924–932.

2. Schwartz DL, Garden AS, Shah SJ, et al. Adaptive radiotherapy for head and neck cancerDosimetric results from a prospective clinical trial. Radiother Oncol. 2013;106:80–84.

3. Duma MN, Kampfer S, Schuster T, et al. Adaptive radiotherapy for soft tissue changes during helical tomotherapy for head and neck cancer. Strahlenther Onkol. 2012;188:243–247.

4. Brown E, Owen R, Harden F, et al. Predicting the need for adaptive radiotherapy in head and neck cancer. Radiother Oncol. 2015.

5. Brouwer CL, Steenbakkers RJHM, Langendijk JA., et al. Identifying patients who may benefit from adaptive radiotherapy: Does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help? Radiother Oncol.

2015;115:285–294.

6. Castelli J, Simon A, Louvel G, et al. Impact of head and neck cancer adaptive radiotherapy to spare the parotid glands and decrease the risk of xerostomia. Rad Oncol. 2015;10:1–10.

7. Castadot P, Geets X, Lee JA, et al. Adaptive functional image-guided IMRT in pharyngolaryngeal squamous cell carcinoma: is the gain in dose distribution worth the effort?

Radiother Oncol. 2011;101:343–350.

8. Christianen MEMC, Schilstra C, Beetz I, et al. Predictive modelling for swallowing dysfunction after primary (chemo)radiation: Results of a prospective observational study. Radiother Oncol. 2011.

9. Wopken K, Bijl HP, Van Der Schaaf A, et al. Development and validation of a prediction model for tube feeding dependence after curative (Chemo-) radiation in head and neck cancer.

PLoS One. 2014;9:95–101.

10. Beetz I, Schilstra C, van der Schaaf A, et al. NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: the role of dosimetric and clinical factors. Radiother Oncol. 2012;105:101–6.

11. Boomsma MJ, Bijl HP, Christianen MEMC, et al. A prospective cohort study on radiationinduced hypothyroidism: Development of an NTCP model. Int J Radiat Oncol Biol Phys.

2012;84:e351–e356.

12. Ajani AA, Qureshi MM, Kovalchuk N, et al. A quantitative assessment of volumetric and anatomic changes of the parotid gland during intensity-modulated radiotherapy for head and neck cancer using serial computed tomography. Med Dosim. 2013:1–5.

13. Wang Z-H, Yan C, Zhang Z-Y, et al. Radiation-induced volume changes in parotid and submandibular glands in patients with head and neck cancer receiving postoperative radiotherapy: a longitudinal study. Laryngoscope. 2009;119:1966–1974.

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14. Houweling AC, Philippens MEP, Dijkema T, et al. A comparison of dose-response models for the parotid gland in a large group of head-and-neck cancer patients. Int J Radiat Oncol Biol Phys. 2010;76:1259–65.

15. Langendijk JA., Lambin P, De Ruysscher D, et al. Selection of patients for radiotherapy with protons aiming at reduction of side effects: The model-based approach. Radiother Oncol.

2013;107:267–273.

16. Broggi S, Fiorino C, Dell’Oca I, et al. A two-variable linear model of parotid shrinkage during IMRT for head and neck cancer. Radiother Oncol. 2010;94:206–212.

17. Reali A, Anglesio SM, Mortellaro G, et al. Volumetric and positional changes of planning target volumes and organs at risk using computed tomography imaging during intensitymodulated radiation therapy for head–neck cancer: an “old” adaptive radiation therapy approach. Radiol Med. 2014;119:714–720.

18. Sanguineti G, Ricchetti F, Thomas O, et al. Pattern and predictors of volumetric change of parotid glands during intensity modulated radiotherapy. Br J Radiol. 2013;86:20130363.

19. Sanguineti G, Ricchetti F, Wu B, et al. Parotid gland shrinkage during IMRT predicts the time to Xerostomia resolution. Rad Oncol. 2015;10:15–20.

20. Vásquez Osorio EM, Hoogeman MS, Al-Mamgani A, et al. Local anatomic changes in parotid and submandibular glands during radiotherapy for oropharynx cancer and correlation with dose, studied in detail with nonrigid registration. Int J Radiat Oncol Biol Phys. 2008;70: 875–882.

21. Wang X, Lu J, Xiong X, et al. Anatomic and dosimetric changes during the treatment course of intensity-modulated radiotherapy for locally advanced nasopharyngeal carcinoma. Med Dosim. 2010;35:151–157.

22. Yang H, Hu W, Wang W, et al. Replanning during intensity modulated radiation therapy improved quality of life in patients with nasopharyngeal carcinoma. Int J Radiat Oncol Biol Phys. 2012;85:e47–54.

23. Zhao L, Wan Q, Zhou Y, et al. The role of replanning in fractionated intensity modulated radiotherapy for nasopharyngeal carcinoma. Radiother Oncol. 2011;98:23–27.

24. Levy A, Blanchard P, Daly-Schveitzer N, et al. Replanning during intensity modulated radiation therapy improved quality of life in patients with nasopharyngeal carcinoma: in regard to Yang et al. Int J Radiat Oncol Biol Phys. 2013;86:811.

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ummarizing discussion and future perspectives

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The overall purpose of this thesis was to quantify the effect of two of the major factors of uncertainty in current radiotherapy treatment with regard to head and neck organs at risk (OARs): delineation variability and anatomical variability during the course of treatment.

Reduction of these uncertainties is of major importance since advanced treatment techniques, such as proton therapy, can be delivered with high conformity and therefore require an adequate definition of structures before and during the course of therapy.

Variability in delineation Variation in delineation of anatomical structures in the head and neck region involved in swallowing was demonstrated by major differences between delineation guidelines for OARs in the head and neck region (Chapter 2). The 3-dimensional dose distributions to some of these swallowing structures can be used as input parameters in Normal Tissue Complication Probability (NTCP) models in order to predict the probability of radiationinduced swallowing dysfunction in individual patients. In this chapter, the variability in NTCP values due to the variation between guidelines and subsequent variation in dose parameters was studied. In some cases, the variation in dose parameters resulted in relatively large absolute differences in anticipated NTCP-values (i.e. >10%). These findings emphasize the importance of using the same delineation guidelines as those that were used to develop and validate the NTCP-models. For a broad introduction of NTCP-models into routine clinical practice, generally accepted consensus guidelines and corresponding NTCP-models are required.

In Chapter 3, interobserver variability within specific delineation guidelines was quantified for a set of head and neck OARs, including the spinal cord, parotid and submandibular glands, thyroid cartilage and glottic larynx by different measures of variability and 3D analysis over sub-regions. The results from this analysis revealed the regions with the largest variability for each OAR. Overall, the largest variability was observed for the cranial region of the OARs. A number of reasons for this variability could be identified, including indistinctness of the delineation guidelines, the lower image resolution in the cranial-caudal direction (determined by the slice thickness), and poor difference in contrast with adjacent tissues. Apart from improving the delineation guidelines, the importance of delineation meetings and training was stressed to optimize compliance to the guidelines.

The main difficulty in the interpretation of results in our but also in other studies that investigated interobserver variability, is the lack of ‘gold standard’ contours. Ideally, surgical specimens could be used to verify the accuracy of delineations as has been done for prostate cancer delineation [1], but obviously this is not an option for normal tissues in the head and neck.

Therefore, the accuracy of OAR delineation is generally derived from interobserver variability relative to other observers, measuring the variation amongst the different contours [2–4]. Commonly used measures include the analysis of volumes [5, 6], the conformity / conformality / (dis)concordance index or Jaccard coefficient [2, 7–10], the Dice Similarity

120 CHAPTER 7 - Summarizing discussion and future perspectives

Coefficient (DSC) [5], and the Hausdorff distance [6]. In addition, the distance of each contour to a reference contour is commonly used, giving a common frame of reference to determine the statistical variation of each of the contouring metrics. The choice of this ‘gold standard’ reference contour varies in the literature from a mathematical average contour [11–13], a STAPLE (simultaneous truth and performance level estimation) consensus contour [14], a radiologist- or experienced oncologist-defined contour [15] or a consensus contour that is decided upon by a panel of experts [16, 17].

There is no consensus on a general set of tools to use to describe interobserver variability in contouring. A combination of measures of volume, position and 3D distance, independent of the number of observers, would provide a comprehensive overview of interobserver variability. Objective measures on interobserver variability are useful for guideline development, and could serve as a benchmark against automated contouring procedures.

Valentini et al. [18] presented a set of recommendations with regard to the ontology definition, performance evaluation tools and benchmark evaluation methods. These authors also advice to describe the existing contouring variability from different points of view, using a combination of multiple different tools.

Based on the results described in Chapter 2 and 3, and in collaboration with a number of international experts in the field of head and neck radiation oncology, we proposed CT-based consensus delineation guidelines for head and neck OARs in Chapter 4. These consensus guidelines have been endorsed by all major cooperative head and neck cancer research groups worldwide, including DAHANCA, EORTC Head and Neck Cancer Group, EORTC Radiation Oncology Group, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG. Furthermore, the guidelines have been integrated in ESTRO teaching courses.

In addition, the Dutch Platform for Head and Neck Radiotherapy (LPRHHT) decided to use these guidelines in the Netherlands which was further supported by the organization of a workshop for head and neck radiation oncologists in the Netherlands.

At present, over 900 head and neck cancer patients that have been included in the Head and Neck Standardized Follow-up Program (SFP) of the department of radiation oncology of the UMCG from 2007 on, are re-delineated according to the new consensus guidelines to develop a complete set of new NTCP models for head and neck cancer patients (Dutch Cancer Society Project: CITOR).

After the introduction of the consensus guidelines, we demonstrated that the interobserver variability was reduced for most OARs investigated [19]. The average 3D variation in distance for 6 observers before and after the introduction of the guidelines was 3.7 mm and 2.4 mm (1 SD), respectively. Out of 20 OARs, 16 OARs showed reduced 3D variation (reduction range 0.1-10.8 mm) using the consensus guidelines. The largest reduction of 4.3 mm was seen for the oral cavity, from 6.4 mm to 2.1 mm (Figure 1).

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In the consensus guidelines, the use of MRI in addition to CT is advised in case of limited discrimination between the OAR and its surrounding tissue, in particular for the brainstem, optic chiasm and optic nerve. The addition of MRI to CT has demonstrated to improve delineation accuracy for GTVs in the head and neck [20, 21]. Also for the parotid glands, MRI would facilitate the delineation of the deep lobe, and allow for an easier distinction between the vascular and the glandular structures of the parotid glands [8].

With the increasing use of MR (and PET) imaging in radiotherapy treatment planning, it is of importance to follow established, consistent image registration procedures. Furthermore, improved geometric accuracy of MRI is required. Phantoms and analysis software have been developed to characterize distortions in MRI scans [22, 23]. Moreover, commercial solutions have become available to measure and correct for geometric distortion.

FIGURE 1

Contours of the oral cavity delineated on CT (left) without (above) and with (below) consensus guidelines. Right side shows 3D variation (1SD).

122 CHAPTER 7 - Summarizing discussion and future perspectives

As stated in Chapter 4, we anticipate that the guidelines will be updated in the near future, incorporating new relevant anatomical and functional information, e.g. using (f)MRI and incorporating (subregions of) OARs that are most relevant for side effects. This is illustrated by a recent publication on the parotid gland, demonstrating that the radiation dose to the region containing the stem and progenitor cells correlated better with post-treatment saliva production than the radiation dose to the whole gland [24].

We expect that the updated guidelines will contribute to a further improvement of NTCP modelling (Figure 2).

FIGURE 2

(Updated and improved set of) Consistent guidelines (Chapter 4)

Increased understanding relevant (subregions of) OARs

Account for geometric changes (Chapter 5)

Accurate, consistent, large set of DVH data (Chapter 2)

7 Delineation guidelines (I) and accounting for geometric changes (II) will lead to most accurate and consistent DVH parameters (III). This provides the possibility to develop improved NTCP models (IV), which will in turn define the updated set of delineation guidelines (I).

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Anatomical variation during the treatment As stated in the introduction, the number of publications on anatomic and dosimetric changes increased rapidly in the last decade. Therefore, we systematically reviewed the literature to obtain better insight in anatomic and dosimetric changes of head and neck OARs during radiotherapy, including the implications of these changes for the rate and severity of complications and quality of life (Chapter 5).

The results of this review revealed that the parotid gland was the most studied OAR. The parotid glands normally shrink to 60-80% of their initial volume at the end of treatment.

Various results regarding dosimetric impact of this shrinkage were reported in the different studies, with a median value of the mean parotid gland dose difference over 25 studies of 1.7 [-1.9-10.4] Gy. The reason that the parotid glands have been subject to so many studies is twofold. Firstly, the dose to the parotid glands is related to the most frequently reported late radiation induced side effect [24â&#x20AC;&#x201C;26], i.e. salivary dysfunction and subsequent xerostomia. Secondly, the parotid glands show large anatomic changes over time during the course of treatment (Chapter 5).

The reason of the large variety in the results of the dosimetric effects can be explained by differences in patient population (e.g., nasopharynx vs. other locations) and differences in treatment techniques (e.g., different margins, OAR-based treatment plan optimization versus conventional treatment planning, etcetera). Another limitation is that most studies only consisted of limited patient populations.

Selection of a subgroup of patients for an adaptive radiotherapy approach would enable adaptive radiotherapy for more radiotherapy departments, since the procedure is still highly labour intensive and thus relatively expensive. Therefore, selection of patients that will benefit most from this approach may be more cost effective and may improve a more widely clinical introduction.

Potential selection criteria for adaptive radiotherapy can be divided in pre-treatment and per-treatment selection factors. We identified a number of potential pre-treatment factors (Chapter 5) including primary tumour site, age, body mass index, planned dose to the parotid glands, the initial parotid gland volume, and the amount of overlap volume of the parotid glands with the clinical target volumes around the lymph node metastases. Potential per-treatment selection factors were weight loss, reduction of lateral neck diameter, parotid gland volume decrease, parotid gland density decrease, distances between parotid gland centres of mass and GTV volume decrease.

124 CHAPTER 7 - Summarizing discussion and future perspectives

An important limitation of the studies performed so far is that many of these consisted of relatively small patient populations and were mainly based on univariable analyses.

To identify prognostic factors for volume changes that eventually result in relevant dose distortions, prospective studies are desperately needed including assessment of anatomic and dosimetric changes as well as multivariable analysis. Since only limited data on the effect of dosimetric changes on the risk of radiation-induced side effects and/or on quality of life were found (Chapter 5), these aspects should be taken into account as well in future studies to determine the ultimate clinical impact of adaptive radiotherapy.

FIGURE 3 1

NTCP Xerostomia 6 months after RT

0.9 0.8

6%

0.7 0.6 12% 0.5 0.4 0.3 0.2 0.1 0

10 Gy

10 Gy 10

20

30

40

50

60

Mean dose contralateral parotid gland (Gy)

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NTCP model Xerostomia 6 months after radiotherapy according to Beetz et al. [25].

A mean dose difference of 10 Gy to the contralateral parotid gland results in a different NTCP difference, depending on the initial dose level.

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We conducted a retrospective analysis on prospectively derived data to develop and validate a method to select head and neck cancer patients for adaptive radiotherapy in Chapter 6.

Two different patient cohorts were available for the development and validation of a multivariable prediction model to identify patients with dose deviations to the parotid gland of more than 3 Gy (Δdose3Gy). The only parameter in the multivariable analysis with a significant association with Δdose3Gy was the mean dose to the parotid gland. Using this single parameter, a sensitivity of 80% could be obtained when a threshold of parotid gland Dmean of 22.2 Gy was used. This would spare 24% of the patients from the labour-intensive adaptive radiotherapy procedure.

The question arises whether using a threshold for Δdose to the parotid gland of 3 Gy for patient selection results in a clinically relevant reduction of side effects in terms of the risk of xerostomia. For some patients, a Δdose of more than 3 Gy translates to a clinically relevant ΔNTCP of more than 10%, while in other patients, the same Δdose is not expected to translate in a clinically relevant NTCP reduction (Figure 3). The effect of Δdose on the NTCP depends on the baseline dose level and the NTCP model applied [25, 26]. When proper externally validated NTCP models would be available, the expected ΔNTCP could become part of the selection process for ART, analogous to the selection procedure for proton therapy [27].

Future perspectives The work presented in this thesis focussed on delineation variability for manual contouring and anatomical variability during the course of treatment. Four different other aspects may further reduce uncertainty with regard to head and neck OARs definition and/or administered dose.

First of all, to facilitate uniform contouring procedures, dedicated delineation software may increase consistency among radiation oncologist world-wide. Fortunately, (semi) automatic delineation software for OARs is increasingly applied in routine clinical practice.

This software is designed to integrate different imaging modalities and offers optimized contouring tools, such as the use of threshold contouring and the ability to delineate OARs in every desired image orientation. Software dedicated to contouring often offers the ability for (semi-) automatic and/or atlas-based delineation. Application of (semi-) automatic delineation has proven its efficacy for certain OARs [28–35], but clinicians should still carefully review and if necessary adjust every automatically generated contour. At present, efforts are being made to automate the quality assurance of automatic delineation, by using a database of validated contours and review every new contour by comparison with the database contours [36, 37]. Such a system would also be helpful for training purposes.

For consistency in retrospective analysis and multicentre studies, it is of importance to use similar validated atlases and deformable image registration algorithms for every patient.

126 CHAPTER 7 - Summarizing discussion and future perspectives

Second, by the increasing use of improved fixation and online image guidance, setup variations have been reduced significantly. Online verification using conebeam CT increased the set-up accuracy, allowing a margin reduction between CTV and PTV of 2-3 mm [38].

It should be emphasized that position verification as such is not able to compensate for anatomical changes. An adaptive procedure is necessary in which the treatment plan is re-optimized for the altered anatomy.

A third aspect that will reduce uncertainty in the definition and administered dose to head and neck OARs is the future application of offline or online adaptive (proton) therapy.

Different adaptive radiotherapy strategies including one or two plan adaptations during the treatment have been proposed to be effective to reduce dose to OARs [39â&#x20AC;&#x201C;42]. Potentially, adaptive radiotherapy would also enable margin reduction and therewith further reduce the dose to OARs. We believe that adaptive radiotherapy is an essential tool for a successful introduction of proton therapy, where anatomic changes may have a significantly larger impact on the delivered dose distribution than in photon therapy [43].

Important requirements for adaptive therapy are in-room and/or out-room (CB)CT and/ or MR imaging in combination with advanced patient transport systems from CT to gantry, QA controlled deformable image registration for contour propagation and dose warping, fast treatment planning and plan QA, and integration of hard- and software of all of the aforementioned components. We are currently putting efforts into realizing these different aspects for our proton therapy facility (the UMC Groningen Particle Therapy Center (GPTC)).

Fortunately, more and more vendors provide software for workflow automation of adaptive radiotherapy. On line 3D image guidance is relatively new in proton therapy as the first cone beam-CT-scan systems are only recently installed.

From a clinical perspective, the exact role of adaptive radiotherapy remains to be defined.

Recent studies [44] showed encouraging post-treatment functional recovery with regard to late toxicity (12 months) for 22 patients treated with adaptive radiotherapy. Other studies [45, 46] reported significantly lower incidences of side effects in the patient group treated with adaptive radiotherapy with respect to those observed in the group treated without adaptive radiotherapy. Patients were, however, not randomly selected for the adaptive radiotherapy scheme, and therefore differences between patient groups might be explained by other factors such as differences in socioeconomic status between the two populations. The ESTRO annual meeting of 2016 entitled a proffered paper session â&#x20AC;&#x2DC;Adaptive radiotherapy: Hype or Hope?â&#x20AC;&#x2122; to update the community with recent findings on this subject (including our findings as presented in Chapter 6 [47]). This underlines that the clinical benefit of adaptive radiotherapy is still under discussion in the radiation oncology community. We need to wait for more clinical results on adaptive radiotherapy to get more

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knowledge about the effect of adaptive radiotherapy on tumour control and the occurrence of complications.

A fourth and last aspect that will aid to reduce (uncertainty of) dose to OARs is robust treatment planning. The main objective of robust treatment planning is to produce treatment plans that are robust with regard to 3D dose distributions to the different sources of uncertainty. Robust treatment planning originated in proton therapy to account for different sources of range uncertainty [48], but also finds its application in photon therapy [49]. Including robustness into a multicriteria optimisation framework would provide the possibility to balance robustness and conformity [48]. Current implementations of robust treatment planning simulate geometric changes by applying setup errors [49] using scenario-based treatment plan optimization [50]. The latter accounts for different scenarios of geometric uncertainty, and re-optimizes the treatment plan to cover the plan criteria in all of the proposed scenarios. Apparently, uncertainties will be more complex and not only depend on the setup error, but also on the anatomical location, IGRT workflow, type of geometric changes and use of adaptive planning. Further research including analysis of repeated imaging over the course of treatment will be necessary for each specific indication.

The choice of position verification procedures, adaptive radiotherapy schedules and levels of robustness of the treatment plan involves a hierarchy of decisions, each of which balance complexity versus flexibility and conformity versus robustness.

Apart from reducing factors of uncertainty in current radiotherapy treatment, it is also important to improve the accuracy of endpoints to develop reliable NTCP models. In case of patient or physician rated endpoints, a consistent way of scoring and grading is required, which requires adequate instructions. Objectification of endpoints would also contribute in this perspective. For quantification of xerostomia and sticky saliva by image biomarkers, the first studies showed promising results [51]. Changes of biomarkers over time, such as reduction of the parotid gland volume, have also shown to be related to the severity of late xerostomia [52]. So information concerning changes of OARs during RT are not only important because of their consequential dosimetric changes, it could also help us to identify patients that are at excess risk to develop complications.

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31. Fitton I, Cornelissen SA. P, Duppen JC, et al. Semi-automatic delineation using weighted CT-MRI registered images for radiotherapy of nasopharyngeal cancer. Med Phys.

2011;38:4662.

32. Chao M, Li T, Schreibmann E, et al. Automated contour mapping with a regional deformable model. Int J Radiat Oncol Biol Phys.2008;70:599–608.

33. Chao KSC, Bhide S, Chen H, et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys.2007;68:1512–21.

34. Zhu M, Bzdusek K, Brink C, et al. Multi-institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas. Int J Radiat Oncol Biol Phys.2013;87:809-816.

35. Tao C-J, Yi J-L, Chen N-Y, et al. Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: A multi-institution clinical study. Radiother Oncol.

2015;115:407–11.

36. Altman MB, Kavanaugh JA, Wooten HO, et al. A framework for automated contour quality assurance in radiation therapy including adaptive techniques. Phys Med Biol.

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37. Kalpathy-Cramer J, Awan M, Bedrick S, et al. Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison. J Digit Imaging.

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38. Wang J, Bai S, Chen N, et al. The clinical feasibility and effect of online cone beam computer tomography-guided intensity-modulated radiotherapy for nasopharyngeal cancer. Radiother Oncol. 2009;90:221–227.

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40. Jensen AD, Nill S, Huber PE, et al. A Clinical Concept for Interfractional Adaptive Radiation Therapy in the Treatment of Head and Neck Cancer. Int J Radiat Oncol Biol Phys.

2012;82:590–596.

41. Fung WWK, Wu VWC, Teo PML. Dosimetric evaluation of a three-phase adaptive radiotherapy for nasopharyngeal carcinoma using helical tomotherapy. Med Dosim. 2012;37:92–97.

42. Bando R, Ikushima H, Kawanaka T, et al. Changes of tumor and normal structures of the neck during radiation therapy for head and neck cancer requires adaptive strategy. J Med Invest. 2013;60:46–51.

43. Kraan AC, van de Water S, Teguh DN, et al. Dose Uncertainties in IMPT for Oropharyngeal Cancer in the Presence of Anatomical, Range, and Setup Errors. Int J Radiat Oncol Biol Phys.

2013;87:888-896.

44. Schwartz DL, Garden AS, Thomas J, et al. Adaptive Radiotherapy for Head-and-Neck Cancer: Initial Clinical Outcomes from a Prospective Trial. Int J Radiat Oncol Biol Phys.2011;83:986– 993.

45. Yang H, Hu W, Wang W, et al. Replanning during intensity modulated radiation therapy improved quality of life in patients with nasopharyngeal carcinoma. Int J Radiat Oncol Biol Phys.2012;85:e47–54.

46. Zhao L, Wan Q, Zhou Y, et al. The role of replanning in fractionated intensity modulated radiotherapy for nasopharyngeal carcinoma. Radiother Oncol. 2011;98:23–27.

47. Brouwer CL, Steenbakkers RJHM, van der Schaaf A, et al. Patient selection in head and neck adaptive radiotherapy. Radiother Oncol. 2016:ESTRO abstract.

48. McGowan SE, Burnet NG, Lomax AJ. Treatment planning optimisation in proton therapy. Br J Radiol. 2013;86:20120288.

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52. van Dijk L V, Brouwer CL, Van der Laan HP, et al. CT image biomarker changes of the parotid gland are associated with late xerostomia. Radiother Oncol. 2016;submitted.

132 CHAPTER 7 - Summarizing discussion and future perspectives

amenvatting

Geometrische variaties van risico-organen in hoofd-hals radiotherapie Kan de bestraling van hoofd-halskanker patiënten consistenter en efficiënter?

Radiotherapie is een belangrijk onderdeel van de behandeling van patiënten met hoofdhalskanker [1]. Tumor controle en overleving zijn de laatste jaren sterk verbeterd door de combinatie van radiotherapie met chemotherapie of biologische geneesmiddelen zoals cetuximab [2, 3]. Daarnaast neemt de incidentie van HPV (humaan papillomavirus) gerelateerde orofarynx kanker toe [4–6]. Patiënten met HPV-positieve tumoren die behandeld worden met radiotherapie, eventueel aangevuld met chemotherapie, vertonen een significant betere prognose dan patiënten met HPV-negatieve tumoren [7, 8].

De verbeterde overleving zoals hierboven beschreven zorgt ervoor dat een steeds grotere groep patiënten leeft met bijwerkingen als gevolg van de radiotherapie. Xerostomie (het hebben van een droge mond) is de meest gerapporteerde ernstige en vaak voorkomende bijwerking, welke nog lang na de radiotherapie kan aanhouden [9–11]. Ook slikklachten worden vaak gerapporteerd, en hebben net als xerostomie een groot effect op de kwaliteit van leven [11].

Een belangrijke verbetering van de radiotherapie techniek was de invoering van intensiteitsgemoduleerde radiotherapie (IMRT), waardoor de stralingsdosis op gezonde

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weefsels kon worden verminderd. Het aantal gevallen van xerostomie is hierdoor significant afgenomen (12–14). Zes maanden na afloop van de behandeling, lijdt toch nog altijd meer dan de helft van de hoofd-halskanker patiënten aan xerostomie [15].

Dosis op de speekselklieren, en met name op de glandula parotis, wordt vaak geassocieerd met het optreden van klachten van xerostomie [15–17]. Voor slikklachten is de dosis op de slikstructuren van belang [15–18]. Voorspellende modellen kunnen worden gebruikt voor het berekenen van de kans op het optreden van een bepaalde complicatie, de zogeheten ‘normal tissue complication probability (NTCP)’ [19]. Dit zijn bijvoorbeeld multivariabele logistische regressie modellen met als input klinische en dosimetrische factoren (20).

Een NTCP model kan gebruikt worden voor het optimaliseren van een bestralingsplan, door de dosis op de risico-organen - en daarmee het risico op complicaties - te verkleinen, terwijl de kans op tumorcontrole gelijk gehouden wordt. Een NTCP model kan ook gebruikt worden voor het selecteren van patiënten voor een geavanceerde bestralingstechniek, zoals protonen therapie [21].

Variaties in de intekening van risico-organen en anatomische variaties tijdens de bestraling (variaties in vorm en positie van zowel doelgebieden als gezonde weefsels) zijn belangrijke bronnen van onzekerheid in de bepaling van correcte dosis parameters [22]. Het verkleinen van deze variaties - en dus vergroten van de nauwkeurigheid van dosis parameters - is van belang voor een zo nauwkeurig en generiek mogelijke ontwikkeling en toepassing van NTCP modellen. In dit proefschrift zijn intekenvariatie en anatomische variaties tijdens de bestraling in kaart gebracht. Daarnaast is het effect op de dosisverdeling onderzocht en zijn aanbevelingen gedaan voor het verkleinen van deze variaties.

Intekenvariatie Intekenvariatie is in de orde van 3-8mm, en kan resulteren in suboptimale bestralingsplannen waarbij deze systematische fouten kunnen leiden tot overdosering van risico-organen en onderschatting van de kans op complicaties [23–25].

De oorzaak van intekenvariatie kan liggen in matige beeldkwaliteit, beperkingen van de intekentool en/of interpretatieverschil, veroorzaakt door verschillen in opleiding en training en/of verschillen in gebruikte richtlijnen. De beeldkwaliteit (CT en MRI) verbetert, intekentools worden geavanceerder en er worden al veel intekenbesprekingen georganiseerd. Het invoeren van consensus richtlijnen draagt bij aan het verder reduceren van intekenvariatie [26]. Voor het intekenen van doelgebieden zijn eerder al internationale consensus richtlijnen gedefinieerd [27, 28], eenduidige richtlijnen voor het intekenen van de risico-organen bestonden tot nu toe niet.

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In hoofdstuk 2 van dit proefschrift zijn de verschillen tussen bestaande intekenrichtlijnen van slikstructuren onderzocht, waarbij het effect op dosis parameters en daaruit volgende NTCP waarden is vastgesteld. Voor een aantal patiënten zou een NTCP waarde meer dan 10% kunnen afwijken, wanneer intekenrichtlijnen worden gebruikt die verschillen van die van het NTCP model. Het is daarom van groot belang om te komen tot consensus intekenrichtlijnen, en bijbehorende NTCP modellen.

In hoofdstuk 3 van dit proefschrift is de intekenvariatie binnen een richtlijn in kaart gebracht voor o.a. de speekselklieren, waarbij verschillen per subregio per orgaan zijn geanalyseerd. De craniale regio’s vertoonden de grootste variatie (>3mm). Mogelijke oorzaken voor de gevonden variatie zijn onduidelijkheden in de richtlijn, de lagere CT-resolutie in de cranio-caudale richting (bepaald door de dikte van de CT coupes) en matig onderscheidend vermogen van het orgaan met het omliggende weefsel. De resultaten van dit hoofdstuk zijn waardevol voor het verbeteren van de intekenrichtlijnen, evenals voor het beoordelen van de kwaliteit van automatische intekentools.

De resultaten van hoofdstuk 2 en 3 hebben geleid tot een conceptversie consensus intekenrichtlijnen. Dit concept is besproken met internationale experts in de hoofd-hals radiotherapie en met vertegenwoordigers van verschillende internationale radiotherapie organisaties, wat heeft geleid tot de definitieve consensus richtlijnen zoals beschreven in hoofdstuk 4. Deze intekenrichtlijnen zijn geaccordeerd door de internationale hoofdhalskanker organisaties DAHANCA, EORTC Head and Neck Cancer en Radiation Oncology Group, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology en TROG. In een eerste studie is al aangetoond dat de invoering van de consensus intekenrichtlijnen leidt tot een lagere interobserver variatie voor een groot aantal van de ingetekende structuren [29]. De consensus intekenrichtlijnen zijn geïntegreerd in ESTRO teaching courses en in Nederland is onlangs een intekenworkshop georganiseerd door het Landelijk Platform Radiotherapie Hoofd-Hals Tumoren (LPRHHT) waarin de richtlijnen zijn besproken voor een nationale implementatie.

De meer dan 900 hoofd-hals patiënten die sinds 2007 zijn behandeld op de afdeling radiotherapie van het UMCG zullen opnieuw gesegmenteerd worden volgens de consensus richtlijnen, zodat vervolgens een complete set op de richtlijn aangepaste NTCP modellen ontwikkeld kan worden (KWF project: CITOR).

Anatomische variaties De planning CT scan waarop een IMRT plan wordt gebaseerd is slechts een momentopname van de anatomie van de patiënt. Het gebruik van herhaalde beeldvorming tijdens de radiotherapie heeft variabiliteit van vorm, volume en positie van risico-organen laten zien [30, 31]. Deze variabiliteit veroorzaakt een verandering in de dosis op het orgaan [31].

Volledig geautomatiseerde adaptieve radiotherapie zou in theorie deze variabiliteit moeten

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kunnen ondervangen. Klinisch relevante dosisveranderingen worden echter slechts bij een beperkt aantal patiënten gezien, en de adaptieve radiotherapie workflow is nog altijd zeer complex, arbeidsintensief en wordt onvoldoende ondersteund door commercieel verkrijgbare programmatuur. Het op voorhand kunnen selecteren van patiënten die baat zouden kunnen hebben bij adaptieve radiotherapie op basis van te verwachten dosis afwijkingen zou daarom zeer wenselijk zijn voor een efficiënte inzet van middelen.

In hoofdstuk 5 zijn veranderingen in de anatomie van risico-organen gedurende de bestraling en hun invloed op dosisparameters beschreven in een uitgebreide review. Ook is gekeken of er (potentiële) voorspellers van deze veranderingen zijn aan te wijzen, welke gebruikt zouden kunnen worden voor het selecteren van patiënten die baat hebben bij adaptieve radiotherapie. De parotiden zijn de meest bestudeerde organen, en slinken tot 60-80% van hun initiële volume gedurende de radiotherapie. De meeste studies bestaan uit kleine patiëntengroepen en verschillen in tumor lokalisatie en in bestralingstechniek.

Het effect van de geometrische verandering van de parotiden op de gemiddelde dosis in de parotiden is in 25 verschillende studies gerapporteerd en varieerde met een interkwartielafstand van -1.9 tot 10.4 Gy (mediaan 1.7 Gy). Om de kleine groep patiënten met een klinisch relevante dosisverandering te onderscheiden van de rest is gekeken naar mogelijke selectiecriteria. Potentiële criteria voorafgaand aan de bestraling zijn primaire tumorlocatie, leeftijd, BMI, parotis dosis, parotis volume en de overlap van de parotis met het clinical target volume (CTV). Potentiële criteria gedurende de bestraling zijn gewichtsverlies, diameter reductie van de nek, afname van het volume van de parotis, afname van de dichtheid van de parotis en afname van de afstand tussen zwaartepunt van de parotis en het gross tumour volume (GTV).

In hoofdstuk 6 is een model gemaakt voor het selecteren van hoofd-halskanker patiënten waarbij variaties in de gemiddelde dosis op de parotiden optreden van meer dan 3 Gy. Deze patiëntengroep zou baat kunnen hebben bij adaptieve radiotherapie aangezien een dosisverschil van 3 Gy overeenkomt met een verschil van 3-10% in NTCP voor het optreden van xerostomie (15, 16). De selectiemethode met de gemiddelde dosis op de parotis als input parameter was het beste in staat de patiënten te selecteren.

Vervolgstudies waarbij dosisveranderingen in doelgebieden en kritieke organen worden bestudeerd in grote, specifieke patiëntengroepen zullen nodig zijn om meer factoren te kunnen toevoegen aan de selectiemethode en de performance te verbeteren.

In dit proefschrift is de grootte en het effect van variaties in intekening en anatomie in kaart gebracht en zijn middelen voorgesteld voor het verkleinen van deze variaties dan wel het beperken van de gevolgen. De consensus intekenrichtlijnen zullen intekenvariatie beperken en een generieke toepassing van NTCP modellen mogelijk maken. De methode om patiënten waarbij een grote dosisvariatie in de parotis optreedt te selecteren kan in

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de klinische praktijk worden gebruikt voor het selecteren van patiënten voor adaptieve radiotherapie, waarbij de dosis op de parotis wordt gereduceerd middels een aanpassing van het bestralingsplan. Op deze manier verkleint de kans op xerostomie wat zal resulteren in een verbeterde kwaliteit van leven.

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3. Pignon JP, Le Maître A, Maillard E, et al. Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): An update on 93 randomised trials and 17,346 patients. Radiother Oncol. 2009;92:4–14.

4. Rietbergen MM, Leemans CR, Bloemena E, et al. Increasing prevalence rates of HPV attributable oropharyngeal squamous cell carcinomas in the Netherlands as assessed by a validated test algorithm. Int J Cancer. 2013;132:1565–1571.

5. Chaturvedi AK, Anderson WF, Lortet-Tieulent J, et al. Worldwide Trends in Incidence Rates for Oral Cavity and Oropharyngeal Cancers. J Clin Oncol. 2013;31:4550–4559.

6. Chaturvedi AK, Engels EA, Pfeiffer RM, et al. Human Papillomavirus and Rising Oropharyngeal Cancer Incidence in the United States. J Clin Oncol. 2011;29:4294–4301.

7. Ang KK, Harris J, Wheeler R, et al. Human Papillomavirus and Survival of Patients with Oropharyngeal Cancer. N Engl J Med. 2010;363:24–35.

8. Huang SH, Xu W, Waldron J, et al. Refining American Joint Committee on Cancer/Union for International Cancer Control TNM Stage and Prognostic Groups for Human PapillomavirusRelated Oropharyngeal Carcinomas. J Clin Oncol 2015;33:836–845.

9. Jellema AP, Slotman BJ, Doornaert P, et al. Impact of Radiation-Induced Xerostomia on Quality of Life After Primary Radiotherapy Among Patients With Head and Neck Cancer. Int J Radiat Oncol Biol Phys. 2007;69:751–760.

10. Jensen SB, Pedersen AML, Vissink A, et al. A systematic review of salivary gland hypofunction and xerostomia induced by cancer therapies: prevalence, severity and impact on quality of life. Support Care Cancer. 2010;18:1039–1060.

11. Langendijk JA, Doornaert P, Verdonck-de Leeuw IM, et al. Impact of late treatmentrelated toxicity on quality of life among patients with head and neck cancer treated with radiotherapy. J Clin Oncol. 2008;26:3770–3776.

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12. Nutting CM, Morden JP, Harrington KJ, et al. Parotid-sparing intensity modulated versus conventional radiotherapy in head and neck cancer (PARSPORT): a phase 3 multicentre randomised controlled trial. Lancet Oncol. 2011;12:127–136.

13. Marta GN, Silva V, de Andrade Carvalho H, et al. Intensity-modulated radiation therapy for head and neck cancer: Systematic review and meta-analysis. Radiother Oncol. 2014;110: 9–15.

14. Gupta T, Agarwal J, Jain S, et al. Three-dimensional conformal radiotherapy (3D-CRT) versus intensity modulated radiation therapy (IMRT) in squamous cell carcinoma of the head and neck: a randomized controlled trial. Radiother Oncol. 2012;104:343–8.

15. Beetz I, Schilstra C, van der Schaaf A, et al. NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: the role of dosimetric and clinical factors. Radiother Oncol. 2012;105:101–6.

16. Houweling AC, Philippens MEP, Dijkema T, et al. A comparison of dose-response models for the parotid gland in a large group of head-and-neck cancer patients. Int J Radiat Oncol Biol Phys. 2010;76:1259–65.

17. van Luijk P, Pringle S, Deasy JO, et al. Sparing the region of the salivary gland containing stem cells preserves saliva production after radiotherapy for head and neck cancer. Sci Transl Med. 2015;7:305ra147.

18. Christianen MEMC, Schilstra C, Beetz I, et al. Predictive modelling for swallowing dysfunction after primary (chemo)radiation: Results of a prospective observational study. Radiother Oncol. 2012;150:107–14.

19. Marks LB, Yorke ED, Jackson A, et al. Use of normal tissue complication probability models in the clinic. Int J Radiat Oncol Biol Phys. 2010;76:S10-9.

20. El Naqa I, Bradley J, Blanco AI, et al. Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys. 2006;64:1275–86.

21. Langendijk JA, Lambin P, De Ruysscher D, et al. Selection of patients for radiotherapy with protons aiming at reduction of side effects: The model-based approach. Radiother Oncol.

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22. Jaffray DA, Lindsay PE, Brock KK, et al. Accurate accumulation of dose for improved understanding of radiation effects in normal tissue. Int J Radiat Oncol Biol Phys.

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23. Rasch C, Steenbakkers R, van Herk M. Target Definition in Prostate, Head, and Neck.

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25. Bortfeld T, Jeraj R. The physical basis and future of radiation therapy. Br J Radiol. 2011; 84:485–98.

26. Mukesh M, Benson R, Jena R, et al. Interobserver variation in clinical target volume and organs at risk segmentation in post-parotidectomy radiotherapy: can segmentation protocols help? Br J Radiol. 2012;85:16–20.

27. Grégoire V, Ang K, Budach W, et al. Delineation of the neck node levels for head and neck tumors: A 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines. Radiother Oncol. 2014;110:172–81.

28. Grégoire V, Levendag P, Ang KK, et al. CT-based delineation of lymph node levels and related CTVs in the node-negative neck: DAHANCA, EORTC, GORTEC, NCIC,RTOG consensus guidelines. Radiother Oncol. 2003;69:227–236.

29. Steenbakkers RJHM, Brouwer CL. Improvement of delineation quality of organs at risk in head and neck using the consensus guidelines. Radiother Oncol. 2016:ESTRO abstract.

30. Barker JL, Garden AS, Ang KK, et al. Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/ linear accelerator system. Int J Radiat Oncol Biol Phys. 2004;59:960–970.

31. Robar JL, Day A, Clancey J, et al. Spatial and Dosimetric Variability of Organs at Risk in Headand-Neck Intensity-Modulated Radiotherapy. Int J Radiat Oncol Biol Phys 2007;68:1121– 1130.

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Dankwoord Hierbij wil ik iedereen die een bijdrage heeft geleverd aan het tot stand komen van dit proefschrift, op welke manier dan ook, heel hartelijk bedanken. Een aantal mensen wil ik in het bijzonder noemen.

Beste Hans, hartelijk bedankt voor de mogelijkheid tot promoveren op de afdeling radio­therapie van het UMCG. Ik heb veel geleerd van jouw kritische en klinische blik op onderzoek en kwam vrijwel altijd enthousiast en met nieuwe ideeën bij een overleg weg.

Ik wil je ook bedanken voor de mogelijkheid om de ‘expert’ interobserver studie te mogen uitvoeren, op deze manier heb ik internationaal veel contacten kunnen opdoen binnen het vakgebied.

Beste Marianna, heel fijn dat je mijn co-promotor en kamergenoot was tijdens mijn promotie­periode. Je was altijd bereid tot in detail met me mee te denken en te filosoferen over het onderzoek (en over andere dingen in ons leven).

Beste Roel, jouw komst naar Groningen kwam voor mij als geroepen, met een heel proef­ schrift aan ervaring op het gebied van intekenvariatie op zak was je de ideale co-promotor.

Bedankt voor al je hulp en advies, en het gezamenlijk opstellen van de consensus richtlijnen. Zoals het er nu uitziet zal deze internationaal zijn weg vinden en tot veel waardevolle data gaan leiden.

De leden van de promotiecommissie wil ik bedanken voor het beoordelen van mijn werk en het plaatsnemen in de oppositie.

Beste Aart, ik wil je heel hartelijk bedanken voor je vertrouwen om me na mijn opleiding tot klinisch fysicus een promotietraject in te laten gaan. Bedankt voor de begeleiding tijdens de eerste periode van mijn promotie. Bedankt ook voor de prettige wijze waarop ik onderzoek en kliniek heb kunnen combineren de afgelopen jaren.

Beste Harm, de paragraaf onderzoek die we in eerste instantie opstelden voor in mijn KLIFIO opleidingsplan is uiteindelijk uitgemond in een hele promotie! Je vertelde altijd met passie over het vak en ik heb veel geleerd van je doorzettingsvermogen en kritische blik, bedankt hiervoor.

Beste leden van het hoofd-hals cluster, hartelijk dank voor jullie hulp bij verschillende onderdelen van mijn promotietraject. Henk, dank je wel voor de begeleiding in de beginfase van mijn onderzoek en de uitleg over de anatomie en fysiologie van het hoofd-hals gebied.

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Many thanks to all co-authors for their contribution: Jean Bourhis, Wilfried Budach, Cai Grau, Vincent Grégoire, Marcel van Herk, Anne Lee, Philippe Maingon, Chris Nutting, Brian O’Sullivan, Sandro Porceddu, David Rosenthal, Elske Gort, Marije Kamphuis, Hans Paul van der Laan, Aart van ’t Veld, Arjen van der Schaaf, Chantal Sopacua, Sanne van Dijk, Roel Kierkels, Henk Bijl, Hans Burgerhof, Edwin van den Heuvel, Joop Duppen, Arash Navran, Olga Chouvalova, Fred Burlage and Harm Meertens.

Beste collega’s klinisch fysici (in opleiding) en klinisch fysisch medewerkers, bedankt voor jullie hulp, op welke manier dan ook, het is een genoegen om met jullie samen te mogen werken!

Heidi, bedankt voor de begeleiding tijdens de eerste periode van mijn onderzoek.

Arjen, bedankt voor je hulp en uitleg op het gebied van NTCP modeling, je check op de Nederlandse samenvatting en de altijd grondige review van mijn werk.

Roel, bedankt voor de samenwerking op het gebied van adaptieve radiotherapie en deformable image registration. Leuk dat je het onderzoek verder kunt uitbouwen met de steeds groter groeiende ART dataset.

Peter (van Luijk), ik heb van jou geleerd om zeer kritisch naar de relevantie en opzet van wetenschappelijk onderzoek te kijken. Jouw masterclass over het schrijven van abstracts/ papers heeft er zeker toe bijgedragen dat ik mijn werk heb mogen presenteren op de ESTRO. Bedankt hiervoor.

Hans-Paul, bedankt voor jouw interesse en adviezen gedurende verschillende research bijeenkomsten.

Harriëtte, bedankt voor jouw inteken adviezen aan mij en verschillende stagiaires binnen het project.

Ruurd, leuk dat je de laatste maanden van je promotie bij me op de kamer zat. Nog heel eventjes en het werk zit er voor jou ook op!

Sanne, je bent nog maar kort bij ons op de afdeling, maar het lijkt alsof je er altijd al was door de snelheid en het gemak waarmee je dingen oppakt. Bedankt voor de prettige samenwerking. Heel leuk dat je met jouw promotie-onderzoek op het gebied van image biomarkers vervolg kunt geven aan het verder ontrafelen van veranderingen in de speekselklieren in relatie tot klachten van xerostomie.

Tian-Tian, thanks for your presence in the weekly research meetings, your hard work on the ART data will certainly reveal more valuable insights.

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Alle andere collega’s van de afdeling radiotherapie van het UMCG die ik niet bij naam heb kunnen noemen, bedankt voor jullie interesse, hulp en bemoedigende woorden.

Alle stagiaires Technische Geneeskunde, Biomedische Technologie en MBRT; Marije, Jorn, Thijs, Elske, Chantal, Shirley, Anne-Mare, Issi, Marijn en Maarten bedankt voor jullie inzet en werk dat allemaal op een bepaalde manier heeft bijgedragen aan dit proefschrift!

Karin, bedankt voor de geweldig mooie vormgeving van mijn proefschrift, en de prettige en snelle samenwerking! Gezellig dat je samen met Cian vlakbij bent komen wonen in de Reitdiephaven en we af en toe een potje kunnen tennissen.

Susan en Marieke, leuk dat jullie mijn paranimfen willen zijn. We zijn alle drie in hetzelfde jaar begonnen aan een promotietraject, en nu jullie dit onlangs succesvol hebben afgesloten voelt het heel veilig in jullie midden. Marieke we kennen elkaar al van de middelbare school als maatjes in de exacte vakken. We gingen samen naar de open dag van de Universiteit Twente waar ik een leuke studie vond die mij richting de klinische fysica leidde. Leuk dat je na jouw promotie nu een leuke baan hebt gevonden, en binnenkort ook een gezinnetje start! Susan tijdens de ‘ladies-intro’ op de UT was jij de eerste die ik ontmoette, en je bent altijd mijn beste maatje geweest in Enschede. Jammer dat we elkaar niet veel meer zien, maar het blijft altijd gezellig bij Pieter en jou te komen. Ik heb er respect voor hoe je je eigen werk verder uitbouwt nu met drie kleine kids! Bedankt voor jullie interesse en tips!

Joost, Marjolein, Lotte, Gillian en Brechtje bedankt voor jullie interesse en bemoedigende woorden. Altijd heel gezellig om jullie te zien en hopelijk blijven we nog lang contact houden.

Lieve meiden, Anja, Carleen, Erlijn, Floor, Hannah, Ilona, Karin, Marieke, Marieke, Marjolein, Rieneke, Wikke. Ik zou hier wel een heel boekwerk over een ieder van jullie kunnen schrijven. Heel erg bedankt voor jullie medeleven en gezellige afleiding! Ik kijk alweer uit naar ons volgende jaarlijkse meidenweekendje.

Lieve Miek, we zijn van kleins af aan al beste vriendinnetjes. Uiteindelijk is het gelukkig niet nodig geweest me te isoleren in jullie B&B voor het afronden van dit proefschrift, maar zeer bedankt voor het aanbod. We plannen gelukkig nog regelmatig wat in, ook al hebben we allebei een druk leven en is Vreeland niet om de hoek! Bedankt voor het gezamenlijk filosoferen over de extra stellingen (ook al zijn ze niet allemaal opgenomen in dit proefschrift).

Lieve (schoon)familie, dank jullie wel voor al jullie interesse, steun en aanwezigheid! Het is altijd genieten om samen op vakantie of weekendjes weg te gaan, en ik hoop iedereen regelmatig te kunnen blijven zien.

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Lieve Oma, u toont altijd interesse in mijn werk en bent oprecht geĂŻnteresseerd in de onderwerpen van mijn studie. Heel erg bedankt hiervoor en ik hoop dat u aanwezig kunt zijn bij mijn verdediging en feest op 19 oktober.

Lieve Marjolijn, heel erg bedankt voor het meedenken over en ontwerpen van vorm en concept van de voorkant van dit boekje, maar vooral voor dat je mijn zus bent!

Lieve pap en mam, ontzettend bedankt voor alle kansen en mogelijkheden van de wereld die ik altijd van jullie heb gekregen. En dat jullie tot op de dag van vandaag altijd voor Arnold, mij en de jongens klaarstaan. Leuk dat we nu samen weer gelegenheid hebben voor een feestje!

Lieve Arnold, Youri, Silvijn en Otis, samen met jullie zijn is fijn! We hebben de afgelopen jaren maar gewoon alles tegelijk aangepakt; kinderen krijgen, een grand cafĂŠ openen en promoveren. We hoeven ons in ieder geval nooit te vervelen! Bedankt voor alles, ik hou van jullie.

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Curriculum Vitae

Charlotte Brouwer is geboren op 1 februari 1983 in Zwolle, en groeide op in Groningen en Glimmen.

Na haar VWO-eindexamen op het Maartenscollege in Haren in 2001 is zij Biomedische Technologie gaan studeren aan de Universiteit Twente. In 2006 studeerde ze af op het onderwerp Brain-Computer Interfacing bij het FC Donders Centrum te Nijmegen, en ontving hiervoor de scriptieprijs van de faculteit Technische Natuur Wetenschappen.

In oktober 2006 begon ze haar opleiding tot klinisch fysicus in de radiotherapie bij het Universitair Medisch Centrum Groningen (UMCG), waarvan 1 jaar werd gevolgd in het Radiotherapeutisch Instituut Friesland (RIF). Na afronding van haar opleiding in 2010 vervolgde zij haar onderzoek betreffende interobserver variatie, autosegmentatie en adaptieve radiotherapie voor hoofd-halskanker patiĂŤnten, wat vandaag de dag geresulteerd heeft in dit proefschrift. Een waardevol resultaat van het werk zijn internationaal geaccordeerde intekenrichtlijnen voor risico-organen in het hoofd-hals gebied.

Sinds oktober 2015 heeft Charlotte een aanstelling als klinisch fysicus op de afdeling Radiotherapie van het UMCG en werkt ze onder andere aan de voorbereiding en klinische implementatie van protonentherapie in Groningen.

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List of Publications 1) Brouwer CL, Steenbakkers RJ, van den Heuvel E, Duppen JC, Navran A, Bijl HP, Chouvalova O, Burlage FR, Meertens H, Langendijk JA, van 't Veld AA. 3D Variation in delineation of head and neck organs at risk. Radiat Oncol. 2012 Mar 13;7:32.

2) Hardcastle N, TomĂŠ WA, Cannon DM, Brouwer CL, Wittendorp PW, Dogan N, Guckenberger M, Allaire S, Mallya Y, Kumar P, Oechsner M, Richter A, Song S, Myers M, Polat B, Bzdusek K. A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapy. Radiat Oncol. 2012 Jun 15;7:90.

3) Brouwer CL, Wiesendanger EM, van der Hulst PC, van Imhoff GW, Langendijk JA, Beijert M. Scrotal irradiation in primary testicular lymphoma: review of the literature and in silico planning comparative study. Int J Radiat Oncol Biol Phys. 2013 Feb 1;85(2):298-308.

4) Zhu M, Bzdusek K, Brink C, Eriksen JG, Hansen O, Jensen HA, Gay HA, Thorstad W, Widder J, Brouwer CL, Steenbakkers RJ, Vanhauten HA, Cao JQ, McBrayne G, Patel SH, Cannon DM, Hardcastle N, TomĂŠ WA, Guckenberg M, Parikh PJ. Multi- institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas. Int J Radiat Oncol Biol Phys. 2013 Nov 15;87(4):809-16.

5) Brouwer CL, Steenbakkers RJ, Gort E, Kamphuis ME, van der Laan HP, Van 't Veld AA, Sijtsema NM, Langendijk JA. Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP. Radiother Oncol. 2014 Apr;111(1):148-52.

6) Brouwer CL, Kierkels RG, van 't Veld AA, Sijtsema NM, Meertens H. The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry. Radiat Oncol. 2014 Jul 29;9:169.

7) Bollineni VR, Koole MJ, Pruim J, Brouwer CL, Wiegman EM, Groen HJ, Vlasman R, Halmos GB, Oosting SF, Langendijk JA, Widder J, Steenbakkers RJ. Dynamics of tumor hypoxia assessed by (18)F-FAZA PET/CT in head and neck and lung cancer patients during chemoradiation: Possible implications for radiotherapy treatment planning strategies.

Radiother Oncol. 2014 Nov;113(2):198-203.

8) Brouwer CL, Steenbakkers RJHM, Langendijk JA, Sijtsema NM. Identifying patients who may benefit from adaptive radiotherapy: does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help? Radiother Oncol. 2015 Jun;115(3):285-94.

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9) Brouwer CL, Steenbakkers RJHM, Bourhis J, Budach W, Grau C, GrĂŠgoire V, van Herk M, Lee A, Maingon P, Nutting C, O'Sullivan B, Porceddu SV, Rosenthal DI, Sijtsema NM, Langendijk JA. CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines.

Radiother Oncol. 2015 Oct;117(1):83-90.

10) Brouwer CL, Steenbakkers RJHM, van der Schaaf A, Sopacua CTC, van Dijk LV, Kierkels RGJ, Bijl HP, Burgerhof JGM, Langendijk JA, Sijtsema NM. Selection of head and neck cancer patients for adaptive radiotherapy to decrease xerostomia. Radiother Oncol.

2016;120(1):36-40.

11) van Dijk LV, Brouwer CL, van der Schaaf A, Burgerhof JG, Beukinga RJ, Langendijk JA, Sijtsema NM, Steenbakkers RJ. CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva. Radiother Oncol. 2016;10.1016/j. radonc.2016.07.007.

12) van Dijk LV, Brouwer CL, Van der Laan HP, Burgerhof JGM, Langendijk JA, Steenbakkers RJHM, Sijtsema NM. CT image biomarker changes of the parotid gland are associated with late xerostomia. Radiother Oncol. 2016;submitted.

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