Uncertainty in clinical radiation oncology

There are numerous uncertainties in clinical radiation oncology. These are accounted for by well-defined clinical processes. Patient-positioning uncertainty is reduced by a simulation process that begins with the treatment-planning CT performed in the treatment position, the construction of customized immobilization devices, and indexing patient position with respect to the geometry of the planned treatment device. The physician is responsible for contouring the target volume and critical normal tissue structures for treatment planning. Targeting has many sources of uncertainty related to the underlying disease process and radiation delivery. The gross tumor volume typically includes the residual tumor and may include the tumor bed. The clinical target volume is the gross tumor volume with an additional margin added to account for subclinical tumor extension. These margins vary according to the disease process and may be on the order of millimeters or centimeters. The planning target volume has an additional geometric margin that is meant to account for positional uncertainty. This margin may be a few millimeters for body sites such as the brain, where fixation is excellent and intra/interfractional positioning differences are small, or larger for extracranial sites, where the nonrigidity of the body makes it challenging to precisely reproduce the treatment position daily. There is uncertainty in radiation-dose calculation. Changes in tissue composition, the volume or shape of the target, or the external contour of the patient due to weight gain or loss over the treatment course may influence the effective path length and attenuation of the beam directed at the target.

Radiation-dose calculations

Calculating the dose of radiation to administer to a patient requires an understanding of the attenuation of the beam and the composition of tissue between the surface of the patient, the tumor, and beyond. In the early treatment-planning systems, tissue heterogeneity was not considered. Clinical photon beams interact with tissue and the Compton effect predominates. Modern photon radiation-dose calculations correct for tissue heterogeneity to reduce uncertainties in absolute dose and rely on calibration curves to map CT numbers (Hounsfield units) to electron or tissue density that is utilized by dose-computation algorithms for generating heterogeneity-correction factors. A potential advantage of dual-energy and dual-layer CT systems is that they further improve the dose-calculation accuracy by considering tissue heterogeneity. These systems produce pixel-by-pixel parametric images of electron density and effective atomic number from which tissue attribution is possible and elemental composition can be estimated. Tissues in the beam path can be better characterized by this new capability than by simple approaches of calibration-curve mapping. The concept of using proton-stopping power images for dose calculation is highlighted by Figure 1.

The ability of dual-energy and dual-layer detector CT systems to improve radiation dose calculation was recognized early by several groups, especially those seeking to improve dose calculation for patients treated with brachytherapy or proton therapy. Dose heterogeneity correction for low-energy brachytherapy sources using dual-energy CT (DECT) images was investigated by Mashouf S et al.1 The new DECT technology overcomes the inaccuracies associated with the practice of tissue segmentation, in which actual tissue composition is ignored during the dose-calculation process. The lower energy range for the radiation sources used in brachytherapy are subject to photoelectric processes and highly dependent on tissue composition for dose calculation. Additional applications of dual-source CT, a type of DECT, were summarized by van Elmpt W et al.2 These applications include tissue characterization, dose calculation and its use in brachytherapy, proton therapy, artifact reduction, and other radiotherapy purposes. The key feature of this review was the potential to improve dose estimation for brachytherapy, photon therapy, and proton therapy by using dual-energy imaging techniques. Monte Carlo calculations, the most accurate method for estimating proton therapy dose, depend on the type of DECT scanner used, and the estimated differences have clinically meaningful implications when calculating proton range.3 The differences were greatest comparing single-energy CT (SECT) and DECT scanners; however, the key finding was that twin-beam scanning was less accurate than dual-spiral or dual-source devices, which is attributed in part to geometric misalignment.

Dose calculations in clinical radiation therapy require the accurate assessment of electron density because of the energy spectrum and a sufficiently large field of view that encompasses not only the patient but also the immobilization devices and positioning systems, including the treatment couch top. In a study using the IQon Spectral CT system, the electron-density measurements, effective atomic number determination, and iodine quantitation were highly accurate and not sensitive to scan or reconstruction parameters.4 For kilovoltage-dose calculations, Vaniqui A et al. compared SECT and DECT methods of tissue segmentation. They found that SECT material segmentation may lead to an underestimation of the dose to organs at risk in the proximity of bone.5 Reduced errors in dose calculations near metal implants,6 and decreased metal artifact in all types of radiotherapy planning have also been investigated.7 Small differences (1.2%) were observed when the value of DECT in proton-dose calculations was assessed for simulations of patients with head and neck cancer.8

Range of uncertainty in proton therapy and accuracy of proton-stopping power

To create more accurate electron density and effective atomic number images, a number of groups have researched the use of spectral CT data to calculate proton-stopping power and reduce range uncertainty in clinical proton therapy. The current practice in radiation oncology is to map Hounsfield units via a single calibration curve to calculate the proton-stopping power ratio, which requires error-inducing assumptions. Error estimates are as high as 3% to 3.5%, depending the types of tissues in the beam path. Many believe that more accurate estimates of proton-stopping power, through more accurate estimates of electron density and effective atomic number, will reduce uncertainty to less than 1%. Considering that reducing side effects is a primary goal for clinical trials involving children with cancer and further optimizing proton therapy is a primary goal for treating all patients, minimizing uncertainty will reduce the dose to critical normal tissues in a broad range of clinical scenarios.

Realizing the clinical goal to implement dual-layer CT technology directly in the treatment planning process requires calibration of materials and stopping-power calculation, tissuemimicking phantom imaging and irradiation, application of results to relevant clinical conditions by using patient data sets, assessing benefit by using accepted dose-effects models, and integrating mapped values to clinical treatment-planning systems to perform comparative planning studies. DECT has been studied to assess its role in estimating proton-stopping power ratio9,10, including ex vivo measurements that showed the stoppingpower ratio calculation of DECT is more accurate than that of SECT. 11 Wohlfahrt P et al. found that, compared to standard approaches, direct stopping-power ratio predictions in a validated model were more robust and accurate for large target volumes, and could play a direct role in particle therapy-planning applications.12 Compared to SECT, DECT may be used to reduce the bias in water-equivalent range predictions.13 A previous study by Wohlfahrt P et al. also showed in cohorts of patients with head and neck cancer or prostate cancer that the respective differences in water-equivalent range shifts measured 1.1 mm (1.2%) and 4.1 mm (1.7%) and were clinically significant.14 In a unique clinical scenario where other materials were considered, the mean percent error in stopping-power ratio for silicone implants was reduced from 11.46% for SECT to 0.49% for DECT.15 Zhu J and Penfold SN showed that DECT-based proton treatment planning in a commercial treatment-planning system was attractive, owing to the ability of DECT to characterize density and chemical composition of patient tissues.16 Yamada S et al. showed that dose distributions on virtual, unenhanced images generated from post-contrast DECT scans were almost equivalent to those on true unenhanced images.17 Virtual unenhanced images remove the iodine signals from the enhanced images, avoid incorrect calculated doses because of increased beam attenuation, which is caused by the high-attenuation characteristics of iodine, and permit the use of a single post-contrast scan for tumor delineation and dose calculation. Instead of using virtual unenhanced Hounsfield unit images, our group showed that iodine enhancement could also be greatly reduced on directly calculated proton-stopping power images from post-contrast DECT scans.18

Because inaccurate conversion of CT data to water-equivalent path length is an important source of uncertainty in ion-treatment planning, DECT may help reduce CT number ambiguities and deviations in stopping-power ratio. As shown by Hünemohr N et al., uncertainty was reduced from 3.1% to 0.7% for soft tissue and 0.8% to 0.1% for bone.19 DECT also facilitates tissue segmentation as inputs to Monte Carlo simulations for in vivo PET-activation studies evaluating range uncertainty.20