MD-Paedigree N
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MD-Paedigree | Issue 4-5
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WWW. Md-pa edigree .eu
Medicine:Transformed Personalised healthcare for future adults
SPECIAL INSERT Enhanced consent: a vision for patient data and data management
SCENARIO: Clinical Impact of Cardiomyopathy Models
FOCUS: How does Microbiota work?
Big Data and You: How can your data affect your treatment
2
MD-PAEDIGREE - Newsletter
EDITORIAL I
n the last year Md-Paedigree has been rapidly
segment, establishing key outcomes and parameters for each
approaching it’s expected, highly ambitious out-
model validation. Now it’s time to bring these processes to
comes. With greater data availability, modelling
bear and demonstrate models’ reliability and usability in routine
activities have made substantial progress and
clinical environments. While we yet don’t know how, or if, each
most models are now sufficiently mature to offer a full demon-
system will be applicable in clinical practice, it is fundamental
stration of their intended features. Thanks to concerted efforts
to demonstrate to the EC that all our efforts are headed toward
by all partners in recent months, the core objective of a fully
the implementation of usable tools that address actual clinical
integrated platform is now taking shape. An
needs, with a realistic commercial potential.
example of that was showcased during the
Remarkable progress has been made by
last MD-Paedigree’s Internal Review as the
the genetics and metagenomics study that
integrated workflow running on a single user
achieved important results, operationally
interface that allows users to run CaseRea-
in terms of samples’ analysis and scientifi-
soner as well as the Case-based Retrieval
cally, producing new, impactful insights into
on unstructured discharge summaries. And
links between pathological phenotypes and
speaking of Internal Reviews, we wish to ex-
metagenomics compositions as we explain
tend a special thanks to our Reviewers, for
further in this newsletter. MD-Paedigree’s
their insightful questions, keen attention and
Annual Review is planned for the 12th of
productive comments on our work. During the next Annual Review, we’ll focus on demonstrating that data, analytics tools and models are further integrated, with the goal of a
May in Brussels and it is our Prof. Bruno Dallapiccola C h i e f S c i e n t i f i c O ff i c e r o f t h e O s p e d a l e P e d i a t r i c o B a m b i n o G e s ù R e s e a rc h D i v i s i o n M D - P a e d i g re e P ro j e c t C o o rd i n a t o r
unified and seamless user ex-
intention to further build on positive previous reviews. To this end our goal is to demonstrate that data, analytics tools and models are to a great extent already integrat-
perience for all platform stake-holders. Another main focus will
ed and for the remaining part are moving quickly toward full
be on bringing the current anonymization pipeline to a real-
integration with the infostructure and that we have a solid plan
time, fully automated process, which is a prerequisite for the
to deploy all our components into a unified solution. On a final
implementation of the “privacy-by-design” approach set out at
note I wish to remind that the Final Conference of MD-Paed-
the project’s inception. Based on this, we decided to include
igree is scheduled for February 2017 in Rome, where a large
a special supplement to this newletter, covering the discussion
number of associations, external experts, clinicians and EC
paper “Enhanced Consent: a vision for Patient Data Protec-
representatives are expected to attend. My recommendations
tion and Data Management”, presented at the ICT’15 event by
for this important event are to put the conference’s focus on
Md-Paedigree’s Project Manager, Prof. Edwin Morley-Fletcher,
the technical results and implemented tools, user-friendliness
that fosters the rich debate on Data Protection Regulation. Vali-
and acceptability testing; the impact on clinical practice and
dation will be a very significant area of work. Correspondent
newly-defined clinical workflows and, finally, the exploitability
methodologies have been refined and specified in each clinical
of project’s results. MD-PAEDIGREE - Newsletter
3
INDEX
EDITORIAL PROJECT NEWS 6 |
Third Annual Meeting in Rome
6 |
Third Biannual Meeting in Crete
7 |
2nd Training Session
7 |
Workshop on Clinical Data Management and Sustainability, Amsterdam
7 |
Box: Alberto Martini is the New Scientific Director of IGG
8 |
WOHIT-HIMSS’15 – Ehealth week Riga
8 |
ICT 2015 Lisbon
9 |
Bio-Techno-Practice Workshop on In Silico Medicine: Philosophical Foundations And Biomedical Applications
10 |
Project website
11 |
Project Videos
12 |
RDA Alliance: A ‘Health Data’ Research Group Foundations And Biomedical Applications
WORKFLOWS, SCENARIOS AND VALIDATION 13 |
Clinical use of MD-Paedigree: Cardiomyopathies
14 |
Clinical impact assessment scenario
Modelling 15 |
Haemodynamic Modelling of the Heart
17 |
Development of a scaling method and Adaptation of the existing musculoskeletal model in motion analysis
18 |
The application of computational anatomy to fat quantification and biomarker extraction
INFOSTRUCTURE
p. 3
p. 6
p. 13
p. 15
p. 20
20 |
Similarity Search Functionalities of ATHENA’s DCV tool
22 |
High Performance Computing in Scientific Applications Employing massively parallel processors for speeding-up
computer intensive algorithms
24 |
Case Reasoner
4
MD-PAEDIGREE - Newsletter
MD-Paedigree MD-Paedigree | Issue 4-5 WWW. Md-pa edigree .eu
GENETICS AND METAGENOMICS FOCUS
p. 25
25 |
MD-Paedigree’s Genetic and Metagenomic Analysis Study
26 |
Microbiota and Microbiome: an introduction
27 |
GUT Health
28 |
Press release (OPBG): Microbiota: scientists discover bacteria that cause obesity and fatty liver disease
30 |
Hereditary Cardiomyopathies: Why Genetic Testing matters?
31 |
Book Review Gut: The inside story of our body’s most under-rated organ
Enhanced Consent: a vision for Patient Data Protection and Data Management
p. 32
Discussion paper drafted by Edwin Morley-Fletcher, Lynkeus - ICT2015 Networking Session
Other Dissemination Activities
p. 46
NEXT INITIATIVES
p. 48
48 |
AEPC 2016: Cardioproof’s Final Conference
48 |
MD-Paedigree’s Final Conference
49 |
Scientific publications
51 |
International Conferences & Events
NEWSLETTER INFO
p. 52
MD-PAEDIGREE - Newsletter
5
PROJECT NEWS Third Annual Meeting in Rome MD-Paedigree’s Third Annual Meet-
requirements (more in the next page).
sues, highlight solutions and new re-
ing was held in Rome on the 15th,16th
Moreover the business model was pre-
search opportunities and consolidate
and 17th of February 2016. The Meet-
sented to different partners/stakehold-
Consortium’s interoperativeness and
ing hosted the 2nd Training event that
ers, to validate key concepts and as-
collaborative spirit.
included two hands-on training ses-
sumptions and glean feedback. Further-
sions on user-facing software, aiming
more thanks to the high participation of
Thanks to all of those who participated
to familiarise physicians with the MD-
all the consortium the three days were
and see you in Leuven!
Paedigree platform and further refine
fully dedicated to discuss relevant is-
Third Biannual Meeting in Crete The MD-Paedigree consortium attended the third biannual meeting, kindly hosted by ATHENA, on the 1st and 2nd of October 2015, in Crete (Greece). The meeting was divided in parallel sessions, for each of the project’s subgroups: Coordination & Management, Cardiomyopathies & Obesity, JIA, Neurologic & neuromuscular diseases (NND), Infostructure. Parallel sessions were enriched by user-testing and validation of the platform and the meeting was concluded by a productive brainstorming on goals and preliminary plans for the final conference in 2017.
6
MD-PAEDIGREE - Newsletter
Project News
2nd Training Session The third annual meeting was also
service (CBR), CasiReview and Case
summaries, etc. to be queried using
a concrete occasion to hold MD-
Reasoner.
a
Paedigree’s second ‘hands-on’ Training
a high level of commitment during
engine that can cope with multiple data
Session. A number of refined tools
the training sessions and gave useful
types and determine similarity using a
were
by
suggestions and feedback to help the
range of data-appropriate algorithms.
the end-users, which could navigate
developers refine the tools for both
This
the key features of the tools, from
user-friendliness
functionality.
more precise, relevant and clinically
basic operations of the platform to its
The expected outcome of this ongoing
meaningful results from the types
file sharing functionalities, through
process will be a robustly integrated
of data that they handle routinely in
realistic
The
tool accessible through a single user
clinical practice
featured tools were data curation and
interface, allowing diverse datasets,
validation (DCV), case based retrieval
such as imaging, clinical, discharge
implemented
use-case
and
tested
scenarios.
Clinicians
and
demonstrated
comprehensive
will
provide
similarity
clinicians
search
with
2 n d Tr a i n i n g s e s s i o n i n R o m e
Workshop on Clinical Data Management and Sustainability, Amsterdam The VPH Institute, sponsored by MD-Paedigree, P-medicine and VPH-Share, has organised a workshop on Clinical Data Management and Sustainability held in Amsterdam on March 1718th 2015. The two and a half days were dedicated to a variety of topics from regulatory issues and legal frameworks of data management to long-term market sustainability and chances for exploitation.
Alberto Martini is the New Scientific Director of IGG On the 11th of January 2016, the Board of Directors of the Istituto Giannina Gaslini in Genoa, named Prof. Alberto Martini, a worldwide authority on child rheumathic diseases, the new Scientific Director of the paediatric hospital. Professor Martini is renown for his scientific and managerial competence and is held great esteem by the international scientific community. The MD-Paedigree Consortium wishes Prof. Martini all the best in this new endeavour, congratulations!
MD-PAEDIGREE - Newsletter
7
Project News
WOHIT-HIMSS’15 – Ehealth week Riga A few months later, on 11th-13th May 2015, MD-Paedigree attended the e-health week 2015 in Riga (Latvia), where a presentation of the Project as a ‘Big Data Infostructure for Paediatrics’ was given at the Speaker’s Corner. The e-health week programme featured multiple topics and initiatives relevant to MD-Paedigreee such as helping address healthcare challenges, empowering patients through eHealth services, as well as better privacy and data protection and cost effectiveness of health IT solutions.
ICT 2015 Lisbon In the same period, the consortium
Consent: a vision for Patient Data Pro-
personal sensitive data. The booth was
successfully submitted a proposal for
tection and Data Management” (includ-
eventually retained by the EC and was
a joint booth (together with its cognate
ed in the current issue) was presented.
offered an excellent position, next to
project CARDIOPROOF and Géant) at
The discussion paper, drafted by Edwin
the EC central circular booth.Within
the ICT2015 event, which took place
Morley-Fletcher of Lynkeus, focused
the booth, MD-Paedigree shared a
in Lisbon (Portugal) on 20th-22nd Oc-
on patients’ privacy and new forms of
screen running all day long project-
tober 2015. During the ICT2015 event,
consent and on the latest technologi-
related videos
a discussion paper entitled “Enhanced
cal solutions to ensure the security of
8
MD-PAEDIGREE - Newsletter
Project News
Bio-Techno-Practice Workshop on In Silico Medicine: Philosophical Foundations And Biomedical Applications On June 3rd, Md-Paedigree partners from
Annamaria Carusi, Ludovica Durst, M a rc o V i c e c o n t i , E d w i n F l e t c h e r, Miles MacLeod, Marta Bertolaso, Viola S c h i a ff o n a t i , M a t t e o C e r r i , E m a n u e l e S e r re l l i @ B i o - Te c h n o - P r a c t i c e I n S i l i c o M e d i c i n e Wo r k s h o p , B a rc e l o n a
Lynkeus
of
overcome the limitations of in vitro
Sheffield attended a Philosophy of
and in vivo experimental models
Science Workshop organised by the
used to represent human biological
Bio-Techno-Practice
in
systems, as well as also the limits
collaboration with the IESE Business
on our cognitive capacities to store,
School of the University of Navarra,
analyse and represent the enormous
in
fantastic
information needed to reliably and
premises (http://www.iese.edu/). The
accurately capture system complexity
aim of the workshop was to “reflect
and variability”. The workshop was
on
foundations
chaired by Prof Marta Bertolaso, from
of in silico modelling and on the
the Campus Bio-Medico, Rome, and was
various implications of its biomedical
the occasion of a series of presentations
applications:
and of a lively and very interesting
Barcelona,
the
and
at
Univeristy
the “promises of in silico modelling to
network
their
philosophical
drugs,
devices,
and
clinical trials”, taking into account
discussion.
s h o w c a s i n g M D - P a e d i g re e ’s i n f o s t r u c t u re to EC Commissioner Moedas
O u r b o o t h r u n n i n g p ro j e c t re l a t e d v i d e o s
E d w i n M o r l e y - F l e t c h e r h o l d i n g a p re s e n t a t i o n o n Patient Data during the Networking Session
MD-PAEDIGREE - Newsletter
9
Project News
Project website
MD-paedigree.eu
is
constantly
updated with latest news and events about the Project. Through the website’s public area, you can learn more about MD-Paedigree
and
gain
access
to
infographics and partner information, read or download our newsletters or watch our newly pusblished short explanatory videos of our research areas. The Project website is designed to raise awareness and to promote contents, discussion and suggestions. If you are part of a scientific community (e.g., EGEE, VPH), a parent or patients Association or you simply want to learn more about our research, please fill in the contact request directly from the front page of our web portal, we’ll be happy to hear from you!
10
MD-PAEDIGREE - Newsletter
Project News
Project Videos Six video clips were produced for
from almost all the institutions of the
public. All the project videos are now
each Project Area (Cardiomyopathies,
Consortium generously took part in
available on Md-paedigree’s YouTube
Obesity,
Infostructure
the creation of the video clips in order
Channel
and Genetics) aiming to present in a
JIA,
NND,
to give a broad view of the project
project’s website at the following link:
concise and comprehensive manner the
as a whole. The videos are intended
project’s areas of study by prominent
to explain the Project study to a
MD-Paedigree researchers. Partners
non-expert as well as to a specialist
(subscribe!)
and
on
the
md-paedigree.eu/video-interviews
MD-PAEDIGREE - Newsletter
11
Project News
RDA Alliance: A ‘Health Data’ Research Group challenges, interdisciplinary research needs and potential roadmaps. Futuristic expectations about reaching a “singularity threshold” precisely in healthcare may still have a long way to go, yet ‘Health Data’ certainly represents a major concern in the current Data Regulation debate. Health Data Analytics infact underpin the revolution
in
health
maintenance
and disease treatment that modern IT promises to deliver. Data analytics applications are thus needed to convert these data to biomedical knowledge, knowledge discovery and data mining
I a n n i s I a c o v i d i s g i v i n g a p a r t o f t h e P re s e n t a t i o n i n To k y o , J a p a n .
(KDD), which can deliver a number of things, such as massive volumes of Following two successful events at the
moral issues such as “What is the right
uncertain, streaming heterogeneous
RDA Plenary 6 in Paris in September
use of the data? What is their misuse?”
biomedical data to curate, validate
2015, MD-Paedigree partners Lynkeus
must be undertaken.
and
and Athena RC attended the 7th RDA
During the Working Group presentation
points of view and under different
Plenary Meeting, held on February
three core values were identidied and
assumptions, as well as to include or
29th to March 3rd, 2016, in Tokyo,
addressed: i) The need for open access
exclude dimensions, combine different
Japan.
presentation
them
from
multiple
concerning
to data for research purposes (where
modalities and incorporate existing
the role of Big Data in Healthcare
long term availability is a crucial issue;
knowledge and previous beliefs while
was given proposing the creation of a
ii) The need to develop a vibrant Data
preserving the privacy of the patients
dedicated ‘Health Data’ Interest Group.
Value industry in health and iii) the
whose data is being analyzed.
The underlying motivation stems from
need to strike the appropriate balance
The value of biomedical big data
the fact itself that there are no RDA
between individual privacy concerns in
repositories coupled with analytics is
groups working on the complexities
the healthcare setting and for research
yet to be exploited: MD-Paedigree and
of health data, and that issues such as
purposes and innovation, which can
it’s cognate Project, Cardioproof, aim
privacy, security and ethical issues are
overall greatly benefit the patient
to foster these topics, standing at the
indeed specific to health data.
population.
forefront of their implementation.
The outcomes of the presentation
The IG will be aiming at triggering
showed that there is a strong need to
strong initiatives under various aspects
strike the right balance between the
of research: Data access and protection,
rights of people and the rights of data. It
personalized medicine, data literacy in
is therefore paramount to disseminate
healthcare, patient data repositories, in
and discuss openly and publicly these
silico drug development, clinical trials
issues and inform patients about the
and policy making. The latter should
need of data sharing along with its
infact represent the interests of the
benefits and its practices. Within the
data-based healthcare community to
growing field of personalised medicine,
policy makers in order to identify the
which aims to perform patient-specific
challenges and potential roadmaps
simulations and predictions, such topics
within
are infact pivotal, and philosophical and
identifying
12
A
analyze
MD-PAEDIGREE - Newsletter
a
common and
framework,
discussing
related
WORKFLOWS, SCENARIOS AND VALIDATION Clinical use of MD-Paedigree: Cardiomyopathies In today’s clinical practice, cardiac pathophysiology information is derived at different times, in different locations, and from different diagnostic modalities and thus dispersed across formats, perspectives and intended uses. The aggregation and management of data is therefore a time-consuming and error-prone process on which, nevertheless clinical outcomes depend. By enhancing existing disease models and bringing predictive power at the point of care, MD-Paedigree is taking a major step towards personalised paediatric care, offering a broad range of prediction and simulation tools to support physicians and clinical researchers in their daily work. Based on diagnostic data-driven models, patient-specific simulations and a scalable patient information repository, MD-Paedigree has the potential to in fact impact the way clinical decisions are made, like in the following clinical applications scenario.
J
onathan is a 12 years old boy who presented at OPBG
two-chamber and four-chamber view) to generate a fully per-
hospital with chest pain. He had no family history of car-
sonalised anatomical model of the heart for the patient, MD-
diac disease and was not taking any medication at that
Paedigree’s allowed Jonathan’s physician to simulate predict-
point. He denied dyspnea at rest, but admitted some fatigue
able clinical scenarios in the medium term and based on those
during mild exercise. Heart rate was 92 bpm and blood pres-
refining clinical decision making.
sure was 70/115 mmhg. 3D echocardiography showed a dilated left ventricle with left ventricular hypertrophy. Systolic func-
Specifically, electromechanical and haemodynamic models
tion was normal, left ventricular filling pressure was increased.
of the heart helped doctors understand Jonathan’s specific
Patient-specific heart morphology was obtained from short-ax-
mechanism of muscle dysfunction, by integrating information
is cine-magnetic resonance images (MRI), showing evidence of
on muscle fibrosis and systolic mechanics, and predict the
mildly reduced ejection fraction, mild diffuse fibrosis of the car-
impact of alternative therapies in reducing mitral regurgitation,
diac muscle and dilated left ventricle. The mitral annular plane
filling pressure and thus relieving symptoms. Jonathan’s treat-
was dilated with mitral insufficiency caused by leaflet tethering,
ment was personalised based on simulations of cardiac mor-
leading his doctor to a first diagnosis of myocarditis. A detailed
phology and function, integrating information on heart geom-
echocardiography 3 months later showed evidence of mark-
etry, ejection fraction, relaxation, ventricular inter-dependence,
edly increased trabeculae of the left ventricular apical and lat-
valve function and cardiac workload. His response to drugs
eral walls, possibly suggesting the presence of left ventricular
was in line with what the MD-Paedigree system predicted,
non-compaction.
therefore further helping physicians in the selection of the most effective treatment at the first onset of symptoms..
The clinical case presented peculiar diagnostic challenges due to the initial stage of the disease and the unclear aetiology, prompting the use a model-driven, clinical simulation instrument. Thanks to the MD-Paedigree model, Jonathan’s physician was able to analyze surface meshes of the left endocardium, left outflow tract, left epicardium, right endocardium, right outflow tract and right inflow tract, reconstructing therefore key features of the patient’s heart anatomy. Using robust 3D echo sequences and MRI data (time-resolved short axis stack, MD-PAEDIGREE - Newsletter
13
Workflows, Scenarios and Validation
Clinical impact assessment scenario
V
irtual Physiological Human technologies are unprec-
clinics
edented in the sense that they enable a leap towards
avoided, quality of life
more predictive, personalised, integrative and efficient
of patients, etc. Other
or
hospitals
healthcare provision. The standard methods of Health Technol-
benefits may in-
ogy Assessment (HTA) were developed to address a technolo-
clude reduced pe-
gy’s incremental value in healthcare and are therefore not suit-
riod of bed-rest at
able to evaluate the kind of progress VPH technologies would
home for patients,
bring about. In order for a VPH-focused meaningful evaluation
reduced readmis-
to become possible, the current concept of HTA needs to be
sion rates due to
reconsidered and adapted.
the avoidance of complications and side effects, fewer drugs
Rainer Thiel
Ph.D. M.A. Communication and Te c h n o l o g y R e s e a rc h , E m p i r i c a
to take, less care to be provided by community nurses, famWorkpackage 19 (Exploitation, HTA, and Medical Device Con-
ily carers and neighbours, fewer side-effects experienced, etc.
formity) deals precisely with this challenge, preparing an appropriate analytical framework and laying the groundwork for
With the case study of cardiomyopathy, together with WP2, we
exploring market access as well as health system and business
developed a care pathway as a tool to link up cost and out-
opportunities. The overall goal is to contribute from a socio-
come indicators to a proposed simple Markov chain model.
economic and commercial perspective towards making mod-
The Markov model will be used as the basis of the cost calcu-
els and simulations readily available at the points of care and
lation and model to enable quantification of health impact as-
to researchers.
sessment of the new technologies within MD Paedigree. In order to estimate the gains resulting from the new technologies,
Reaching an HTA approach amendable towards the specific
we collected the cost and conditions probability data within the
requirements of the VPH modelling and computer aided medi-
existing standard care pathway. This helps the quantification of
cine context involves first an analysis of core HTA methodologi-
standard care scenarios impact and allows comparison with
cal challenges. This contributes to establishing a better defined
the new care pathway within MD Paedigree.
terminological framework for generic HTA work and also explores the role of HTA in the policy making and implementation.
To estimate the costs of the clinical pathways, the exact costs
One of the identified challenges consists in the fact that stand-
of the technologies used in each pathway are needed as well
ard HTA usually concerns itself with more or less proven tech-
as the percentages of the risk patients that go through each
nologies and compares them with already matured and widely
pathway. The percentages (or probabilities) of patients moving
diffused ones. This poses a problem in the swiftly changing
from one pathways to the other and ultimately receiving treat-
technological environment of VPH. The complete life cycle of
ment, moreover, form the basic assumption behind the overall
a new and modified technology, across all its development
cost-benefit analysis.
stages, also referred to as Technology Readiness Levels (TRL); needs to be incorporated into HTA.
The plans for the next year will be feed the model with more robust data and expand it accordingly, thereby ultimately to il-
The final goal of the planned impact assessment is to estimate
lustrate how the transformation of bio-computational modelling
a high-level generic benefit-cost scenario for exploring the
and VPH technologies into a future patient flow will supplement
clinical and socio-economic impact of MD-Paedigree applica-
and improve the current management the specific diseases
tions. In order to render the scenarios to be developed useful
targeted by MD-Paedigree. The goal behind this clinical and
and reasonably realistic, it will be necessary to provide at least
socio-economic assessment perspective is to facilitate the
rough estimates of their potential socio-economic impact, and
testing of clinical application scenarios for bio-computational
in particular their clinical benefits. Clinical impact and health-re-
models and deliver support tools as well as empirical evidence
lated outcomes may refer to factors and variables such as: pri-
for health system actors and decision makers, exploitation
mary and secondary endpoints of medical and clinical trials, for
planning and business modelling.
example, changes in mortality (death rate) or morbidity (disease rate), length of stay in hospital, visits to physicians/outpatient
14
MD-PAEDIGREE - Newsletter
MODELLING Haemodynamic Modelling of the Heart
A
n important objective of MD-Paedigree
In our context a reduced order model that works
is to create patient-specific multiscale
with volume-averaged variables would be called a
and multiphysics models of the whole
0D model, one that works with area-averaged vari-
heart, both healthy and affected by
ables would be called a 1D model, and
cardiomyopathies,
so on. In MD-Paedigree we built a num-
the ic
anatomical
by and
representation
with
integrating haemodynambiomechani-
cal and electrophysiological models.
Mihalef Viorel
ber of such haemodynamic models, with
PhD. Imaging and Computer Vision, Siemens AG
A multiscale/multi-physics virtual heart
increasing complexity: a 0D standalone model of the cardiac and adjacent vessel flow, a 0D model of the cardiac and
model would allow integration of various medical data, so that,
adjacent vessel flow fully coupled with the 3D biomechanical
as more data is accumulated over time, the model evolves
model of the heart, and a 3D+time computational fluid dynam-
from an “averaged” to a “detailed”, individualized model. Such
ics (CFD) model that is driven by boundary conditions pro-
patient-specific models are expected to augment existing clini-
vided by the biomechanical heart model.
cal tools by enabling the assessment of post-treatment outcomes even prior to the intervention, and therefore help clini-
We are now close to the final modeling goal, which seeks to
cal decision making.
maximize the flexibility of our model by fully coupling the blood flow and biomechanics at the 3D level. Exciting achievements
Blood flow and cardiac wall and valves motion are tightly con-
along our path are the successful tests of the robustness of
nected, through so-called momentum exchange. Blood flow
our models, and of their capability to model cardiac patholo-
can be impacted by, but also trigger and influence various
gies like valvular regurgitation or stenosis, as well as their ca-
cardiac pathologies, like for instance paediatric cardiomyopa-
pacity to match medical imaging data.
thy. For this reason, blood flow can reveal important details associated to the severity of disease. Such haemodynamic information can be captured by complete (or “full order”) three-dimensional (3D) models that quantify the blood velocity and pressure variables everywhere inside the cardiovascular region of interest, or by so-called “reduced order” models,
F i g u r e 1 I m a g i n g d a t a i s i n t e g r a t e d f ro m m u l t i p l e n o n - i n v a s i v e s o u rc e s t o b u i l d t h e c o m p l e t e c a rd i a c a n a t o m y. F ro m l e f t t o r i g h t : T T E d a t a a n d e x t r a c t e d v a l v e s , C i n e - M R I d a t a a n d e x t r a c t e d c a rd i a c w a l l s , c o m p l e t e c a rd i a c m o d e l i n t e g r a t i n g v a l v e s a n d walls over time.
which work with averages of those haemodynamic variables.
MD-PAEDIGREE - Newsletter
15
Modelling
Model personalization
are just two examples of the process through which medical
Models are governed by a set of parameters, often related to
imaging data informs our haemodynamics models.
a biophysical property of the organ (e.g. stiffness). Standard parameters are available in the literature, derived from ex-vivo
Model testing and validation
or animal experimental studies. They thus represent a generic
Initial verification of the reduced order haemodynamics cou-
organ, and have limited impact at the time of treating a patient.
pled with the biomechanical model of the heart (Figure 2) show
Day-to-day clinical applications require personalized models,
its ability to capture both mitral and aortic stenosis and regur-
whose parameters are fitted such that the output of the model
gitation (Figure 2).
matches the current clinical observations. Clinical data used for personalization can come from multi-
F i g u r e 3 F l o w s t re a m l i n e s i n s i d e t h e s e g m e n t e d c a rd i a c re g i o n , b a s e d o n t h e P C - M R I d a t a .
ple, complementary sources: standard clinical measurements like heart beat or cuff blood pressure, Trans-Thoracic (TTE) or trans-esophageal (TEE) echocardiography images for valve information, Cine MRI images for ventricular wall information and tissue substrate, 3D+time Phase-Contrast MRI (PC-MRI) images for blood velocity information. Personalization of the anatomy is done by building a geometric model of the heart which integrates the TTE-based dynamic valve model and the Cine MRI-based dynamic ventricle model (Figure 1). The reconstructed moving anatomy is subsequently used by the full order model to constrain the 3D haemodynamics, with the endocardial walls pushing or pulling the blood through the openings created by the valves. On the other hand, the same moving anatomy provides the temporal variation of the ventricular volume over time, which is the information perfectly fitted to personalize the reduced order 0D blood flow model. These The 3D CFD model has been used to perform the first valiFigure 2 The left image shows a schematic of our coupled biomechanical model and whole body c i rc u l a t i o n . T h e r i g h t i m a g e s s h o w t h e re s p o n s e o f our system to inducing valvular pathologies. The h e a l t h y - v a l v e s P V l o o p i s d i s p l a y e d i n g r a y, a n d t h e p a t h o l o g i c a l c o n d i t i o n s P V l o o p s a re o v e r l a y e d i n pink.
dations versus 3D+time PC-MRI data measured by clinicians from Ospedale Pediatrico Bambino Gesu. The cardiac segmentation was used to mask the 3D PC-MRI velocity vectors (Figure 3), which were then compared with the CFD computed vectors. For the comparison we defined a “flow-aware” regional map, which is a 7-segment intra-cardiac map designed to better capture the “slingshot” dynamics of the cardiac blood
16
MD-PAEDIGREE - Newsletter
Modelling
flow. Initial validation results show a promising match of the 3D PC-MRI and 3D CFD region-averaged velocities, especially during systole (Figure 4). Conclusion We have provided a small picture of how integration of multi-modality medical imaging data allows the generation of a holistic heart model. This model in turn helps estimate cardiac haemodynamics even when only limited or no such imaging data is available. As future work, a comprehensive evaluation of haemodynamics-based features will determine their usefulness to facilitate further patient stratification and inform future treatment. If successful, this technology will also impact the clinical decisions for dilated cardiac myopathies, resulting in better diagnosis and therapy for the
F i g u r e 4 L e f t s i d e , t o p : " f l o w - a w a re " 7 - s e g m e n t re g i o n a l m a p o n t o w h i c h f l o w i s a v e r a g e d a n d c o m p a re d . L e f t , b o t t o m : Ve l o c i t y v e c t o r a v e r a g e s f ro m C F D ( re d a r ro w s ) a n d P C - M R I ( w h i t e a r ro w s ) a t p e a k s y s t o l e . R i g h t t o p : m a g n i t u d e c u r v e s o f t h e re g i o n - a v e r a g e d C F D a n d P C - M R I v e c t o r s . R i g h t b o t t o m : m a g n i t u d e ( i n d e g re e s ) o f t h e a n g l e b e t w e e n t h e re g i o n - a v e r a g e d v e c t o r s . T h e a b s c i s s a re p re s e n t s t h e 7 segments.
young patients
Development of a scaling method and Adaptation of the existing musculoskeletal model in motion analysis Musculo-skeletal
To obtain reliable outcomes in the paediatric population, one
modelling
an
of the most important parameters which need to be specified
often-used tool in
or made patient-specific, is the length of the model segments,
motion analysis to
in other words to scale the model to the patient’s length.
study the contri-
Motekforce Link’s Human Body Model (HBM), which is used in
bution of different
MD-Paedigree, is based on a generic model, using measures
muscles to move-
obtained from cadaver studies of adults. Based on theoretical
ment patterns in
and practical influences of the different model parameters on
clinical rehabilita-
the model output a novel scaling method was developed to
tion. Using these models in clinical gait analysis offers methods
make the Human Body Model more accurate and applicable
to diagnose, plan and evaluate various interventions in neuro-
for the pathological paediatric population.
Frans Steenbrink
P T, P h . D , A p p l i e d R e s e a rc h m a n a g e r, M o t e k f o rc e L i n k
is
logical and neuromuscular diseases. Enhancement of musculo-skeletal models to improve clinical services is an on-going
In wp11 Siemens, Sheffield, TU Delft and Motek Medical have
topic of research and has continuously led to more accurate
collaborated to create patient-specific HBM models from MRI
and realistic models.
data sets. Segmented MRI data was processed into modelling parameters which can be used to create patient-specific
To better match the (pathological) morphology of a specific pa-
OpenSIM models. These OpenSIM models can subsequently
tient or patient group, model parameter specification is intro-
be processed using a motion-file to generate a patient-specific
duced. This means that a certain model parameter is adapted
HBM model. The pipeline to develop patient-specific models
according to a patient specific recording, which would offer
was agreed upon between the various partners and tested us-
better insight in a patient’s pathological gait to improve treat-
ing data sets of typically developing children and children with
ment strategies.
a neurological or neuromuscular disease. MD-PAEDIGREE - Newsletter
17
Modelling
F i g u r e 1 M o d e l i n g p i p e - l i n e t o c re a t e p a t i e n t s p e c i f i c m u s c u l o - s k e l e t a l m o d e l f ro m M R I a n d clinical gait analysis data
The application of computational anatomy to fat quantification and biomarker extraction Fraunhofer is represented in two workpackes within
more robust and generic algorithmic solutions. The
the MD-Paedigree project. In WP 9, which concerns
first step was to automate the process of analyzing
the modelling of cardiovascular risk in the obese
fat in the liver. Approaches based on learned shape
child and adolescent, the automated
and/or appearance information are very
quantification of body fat distributions is researched. The body mass index (BMI) is still the primary measure to rate the degree of obesity for clinical diagnostics and studies. However, a drawback of this
successful for segmentation in general and for liver segmentation in CT datasets
Klaus Dreschler,
D r- I n g H e a d o f D e p a r t m e n t , Fraunhofer IGD
Anqi Wang,
M.Sc., Fraunhofer IGD
in particular. Such a method has been developed for liver segmentation in MR datasets, which is now successfully used
simple measure is that it only estimates
to quantify liver fat.
the general adiposity of a subject, but not the indi-
Subcutaneous adipose tissue (SAT) consists of fat
vidual fat distribution within the body. It has been
deposits beneath the skin and is clearly visible in
shown that the visceral adipose tissue (VAT) highly
the fat images that we got from our clinical partners.
correlates with cardio vascular diseases (CVD). But
However, simple thresholding would also include
also subjects with normal BMI might suffer CVD due
visceral fat and other tissues within the threshold
to fat accumulation in other parts of the body, for example in
range. We obtained promising results using a ray-based meth-
organs. Manual delineation of different anatomical structures
od that scans the preprocessed whole-body fat-fraction MR
in 3D data is very time consuming, especially for large amount
image from different directions to detect the inside of the body.
of data. Therefore, it is highly desirable to automate this task.
However, this is current work in progress and we are excited to
The basis for fat quantification is the acquisition of fat & water
finish with this part of WP 9. Figure 1 shows preliminary seg-
separated MR images, where two different protocols are used
mentation results (red: Subcutaneous fat, Green: Internal fat,
by the clinical partners: T2* IDEAL and a Dixon sequence. Al-
Yellow: Liver).
though two different protocols will require more work on the image processing side, it gives us the opportunity to develop 18
MD-PAEDIGREE - Newsletter
Modelling
F i g u re 1 : S e g m e n t a t i o n re s u l t s w h i c h a re u s e d f o r fat quantification.
In order to stage the disease progression of the enrolled JIA patients, within WP 10 “Modelling and simulation for JIA�, we aim at extracting relevant biomarkers such as the volume of
to differentiate between inflamed tissue and imaging artifacts
inflamed synovial tissue. A processing pipeline consisting of
we use a knowledge-based approach. Since synovial mem-
two main steps has been constructed to automatically detect
brane is the soft tissue found between the joint capsule and the
inflamed synovial volume. During the first step, all foot bones
joint cavity, the inflammatory tissue has to be in the proximity
are segmented in a dataset. The second processing step con-
of the detected joints. The remaining regions are morphologi-
sists of several sub-procedures. The binary bone segmentation
cally processed and the voxels are grouped into components
images are converted to 3D meshes. Using these meshes, the
based on pixel connectivity. Each of the connected regions is
MRI dataset of an ankle is then classified into regions of inter-
checked for size and proximity to one of the previously detect-
est. In the images acquired after the injection of the contrast
ed joints. Regions with a size of less than a certain threshold
medium (postCM, Figure 1 left), inflamed regions are clearly
are discarded as scatter, and so are regions which do not in-
visible due to their high intensity values. By detecting regions
tersect with one of the joints. The segmentation result is shown
with extraordinarily high intensity values, potential candidates
in Figure 1 right. In past research, evidence has shown that the
for inflamed synovial tissue are obtained. These are detected
volume of synovial inflammation may be used as a predictor of
using an adapted version of multilevel thresholding (Figure 1
erosive damage and a relevant marker of disease activity.
middle). The next task is then to filter out those that represent
With the aforementioned modelling activities, Fraunhofer seeks
other structures with similar intensities; these may be tubular
to push the boundaries of current state of the art methods and
structures like vessels or tendons, or just imaging artifacts. The
aims to provide new and innovative solutions to assist clinicians
tubular structures are detected using an approach based on
in their daily practice.
the eigenvalues of the Hessian matrix of the image. In order
Fig. 1: Original postCM image (left), intermediate re s u l t ( m i d d l e ) , s e g m e n t a t i o n re s u l t ( r i g h t )
MD-PAEDIGREE - Newsletter
19
INFOSTRUCTURE Similarity Search Functionalities of ATHENA’s DCV tool Identification
of
similarities
Several well-established Machine Learn-
•
ing (ML) techniques and algorithms have
among samples, where we want to
been implemented on top of ATHENA’s
find similar patients based on specific
EXAREME (ex ADP/madIS) data flow
characteristics.
processing system and integrated with the Data Curation and Validation (DCV) tool following the same architecture.
• Orfeas Aidonopoulos
R & D e n g i n e e r, A t h e n a R C
Some of these methods can be used
Identification
among
features,
of
similarities
where
we
infer
similarities and correlations among the predictor variables.
for the identification of similar patterns/ samples/patients/trials in order to gain information about their
Identification of similarities among samples
structural relationships.
Cluster analysis is used to explore datasets for hidden patterns or similar groups of data. Clustering algorithms form groupings
Particularly, extending DCV’s functionalities with a knowledge
or clusters in such a way that data within a cluster have a higher
discovery tab the user can now address two kinds of problems:
measure of similarity than data in any other cluster.
F i g u re 1 : D B S C A N o n i r i s d a t a s e t . T h i s a l g o r i t h m v i e w s c l u s t e r s a s a re a s o f h i g h d e n s i t y s e p a r a t e d b y a re a s o f l o w d e n s i t y. H e re i n , c l u s t e r i n g b a s e d o n s e p a l f e a t u re s a n d s a m p l e s a s s i g n e d i n t o 3 c l u s t e r s . C o re s a m p l e s a re d e p i c t e d b y b i g g e r c i rc l e s , w h i l e o u t l i e r s a re g ro u p e d i n c l u s t e r - 1 .
20
MD-PAEDIGREE - Newsletter
Infostructure
The measure of similarity on which the clusters are modeled
ours indicate cluster membership, bigger circles the core sam-
can be defined by metrics like Euclidean distance. In Figure 1
ples in each cluster (samples that belong in areas of high den-
one can observe the interface of DCV’s clustering tab (example
sity), and misgrouped samples (outliers) belong to ‘cluster: -1’.
on Iris data set). The user selects an algorithm (in this exam-
Currently, four clustering algorithms have been implemented
ple, DBSCAN has been selected), and the corresponding pa-
in the ‘extended-DCV’ tool: k-means, DBSCAN, Affinity Propa-
rameters and features (columns) based on which the algorithm
gation and Mean Shift. The following comparison table shows
groups the samples together. DBSCAN identified three clusters
which parameters the user can change, the most appropriate
and some samples that cannot be grouped in any cluster: col-
use-case, and the metric used by each method.
METHOD
PARAMETERS
APPROPRIATE USE-CASE
METRIC USED
K-means
number of clusters
General purpose, not too many clusters, flat (Euclidean) geometry
distances between points
DBSCAN
number of core samples, threshold distance
Very large number of samples, uneven cluster size, non-flat geometry
distances between points
Mean Shift
bandwidth of data
Many clusters, uneven cluster size, non-flat geometry
distances between points
Affinity Propagation
damping factor, sample preference
Many clusters, uneven cluster size, non-flat geometry
graph distance
Identification of similarities among features The reduction of the input space infers similarities and correla-
The current version of DCV doesn’t provide correlation scores
tions among the predictor variables, something with high sig-
among features or any other results, but these have been al-
nificance in clinical applications. Thus, dimensionality reduction
ready scheduled for a future version. However, as illustrated
techniques are usually used to identify correlated or very similar
bellow, these techniques tend to reduce the number of dimen-
features and visualize them in a new space. With DCV’s exten-
sions and simultaneously cluster samples in groups. We tested
sions, the user can project her/his data into a two-dimensional
DCV on the Iris flower data set and, as one can easily see,
space and group similar variables together, avoiding problems
PCA and SVD separates samples in two groups; a few outliers
like overfitting at the same time. We implemented on DCV three
can also be detected. t-SNE tends to separate them in three
techniques for dimensionality reduction: Principal Component
clusters, something very informative as this corresponds to the
Analysis (PCA) and Singular Value Decomposition (SVD), which
three kinds of flowers found in the Iris data.
are two very similar methods, and t-SNE.
F i g u re 2 : P r i n c i p a l C o m p o n e n t A n a l y s i s . O r t h o g o n a l p ro j e c t i o n o f t h e I r i s d a t a o n a 2 - d i m e n s i o n a l s p a c e .
MD-PAEDIGREE - Newsletter
21
Infostructure
High Performance Computing in Scientific Applications Employing massively parallel processors for speeding-up: computer intensive algorithms
Parallel computing is driven by one of
in the region of interest is marked as be-
the three following goals: solve a given
ing either inside (fluid) or outside (solid) of
problem in less time, solve more com-
the domain, a procedure which is called
plex problems within a given amount of
voxelixation. In FSI computations, since
time, and achieve better solutions for a given problem and a given amount of
the geometry (arterial wall, ventricular Lucian Mihai Itu,
time. Thus, parallel computing is pri-
R e s e a rc h e r a t Tr a n s i l v a n i a University
marily motivated by increased speed!
Cosmin Ioan Nita
Graphic Processing Units (GPUs) are dedicated processors, designed originally as graphic accelerators. Since CUDA (Compute Unified Device Architecture) was introduced in 2006 by NVIDIA as a graphic application pro-
the type of a location (fluid/solid) may also change in time, the voxelization needs to
P h D s t u d e n t a t t h e Tr a n s i l v a n i a U n i v e r s i t y o f B r a s o v.
Anamaria Vizitiu,
R e s e a rc h a n d D e v e l o p m e n t Engineer – Siemens, UTBV
Constantin Suciu,
wall, etc.) is changing in time, and hence
be run repeatedly. In case a CPU-based implementation is used, the voxelization leads to an increase of the execution time by 50%, when compared to a rigid-wall hemodynamic computation.
Head of Department Corporate Te c h n o l o g y, S i e m e n s , U T B V
Two GPU-based implementations of the voxelization algorithm have been consid-
gramming interface (API), the GPU has
ered. The first one is the classic method
been used increasingly in various areas
which was initially implemented on the
of scientific computations due to its su-
CPU and which is based on bounding
perior parallel performance and energy
boxes. The disadvantage of its GPU based
efficiency. When a GPU is programmed
implementation is that multiple threads
through CUDA, it is viewed as a com-
access the same memory location. This
pute device, which is able to run thousands of threads in par-
requires a synchronization operation, which in turn reduces
allel by launching a kernel (a function, written in C language,
parallelism and performance. Furthermore, since each thread
which is executed by the threads on the GPU). GPU based
processes a different number of locations, some threads have
applications are run in a CPU-GPU tandem manner, whereas
to wait for others to finish their operations. The second im-
the CPU, usually called host, launches the main application,
plementation employs a method based on separating planes,
and allocates and initializes the data.
which identifies apriori the locations processed by each thread, and elliminates the need for synchronization.
Then, the buffers are transferred to the global memory of the GPU and the CPU calls the kernel which performs the compu-
For a typical ventricular geometry the classic method lead to an
tations on the GPU. Finally, results are copied back to the CPU,
execution time of 23.5 seconds on the CPU and of 0.234 sec-
which performs post-processing tasks.
onds on the GPU. With the separating planes, the GPU based
Due to its very efficient performance-cost ratio, and its widespread availability, the GPU is currently the most used massively parallel processor. In the following we will focus on two specific applications for which the GPU-based implementation has allowed to significantly reduce the execution time. The first application is used in the cardiomyopathies disease group and has allowed to significantly speed-up fluid-structure interaction (FSI) hemodynamic computations. Each location 22
MD-PAEDIGREE - Newsletter
Infostructure
implementation required only 0.0214 seconds, thus leading to
the GPU-based implementation varies between 170x and 190x
an overall speed-up of more than 1000x! With this approach
compared to the single-core implementation and between 24x
FSI computations can be performed almost as fast as rigid-
and 28x compared to the multi-core implementation. This ap-
wall computations, i.e. the overhead of the voxelization step
plication was developed in collaboration with Siemens AG.
becomes negligible. This application was developed in collaboration with Siemens AG and Siemens Corporation.
Other GPU-based applications which have been developed in collaboration with various project partners are: optimized com-
The second application is used in the modelling of the cardio-
putation of stencil based algorithms, single-GPU and multi-
vascular risk in obese children and adolescents: a GPU-based
GPU solution of very large systems of sparse linear equations
implementation of a random forest classifier has been devel-
using the preconditioned conjugate gradient method and the
oped. Random forest (RF) is an ensemble classifier consisting
geometric multigrid method, information retrieval based on
of decision trees that combines two sources of randomness
Bloom filters, latent semantic analysis, and texture analysis us-
to generate base decision trees: bootstrapping instances for
ing steerable Riesz wavelets.
each tree and considering a random subset of features at each node. Similar to other machine learning techniques, it is composed of a training and a testing phase. Since the training is performed offline, its execution time is not critical. Hence, we have focused on the acceleration of the testing phase: in most of the cases this phase is performed online and the execution time is critical. An interesting aspect of the GPU-based implementation of the RF classifier is the storage format of the decision trees: the data structures describing the RF are mapped to a 2-D texture array which is stored in the GPU texture memory. These texture arrays are read-only, and, since they are cached, the performance of reading operations is improved. Both single-core and multi-core implementations have been considered for the CPU and the speed-up provided by
Fluid node becomes solid node when geometry is updated
To p : m e s h re p re s e n t i n g l e f t v e n t r i c l e a n d a n d l e f t ventricle outflow tract Bottom: voxelized left ventricle and left ventricle outflow tract
MD-PAEDIGREE - Newsletter
23
Infostructure
Case Reasoner Big data and machine learning are cur-
rithms that underpin these elements is
rently seen as the most disruptive technol-
crucial for delivering an effective sys-
ogies for the years to come. Indeed, our
tem. In the context of MD-Paedigree,
world generates yearly a massive amount
data characterizing patients is acquired
of data that potentially contains highly val-
and/or extracted from multiple sources
uable information. This is especially true in the domain of healthcare, where by mining huge collections of patient data, new correlations, factors, biomarkers or drugs can
Olivier Pauly
P h . D . , R e s e a rc h s c i e n t i s t a t S i e m e n s H e a l t h c a re , M e d i c a l I m a g i n g Te c h n o l o g i e s , Erlangen, Germany
be discovered to enhance the diagnostic
of very different types of information such as MR imaging, haemodynamic measurements, and clinical and family history. Modelling similarity of cases using such disparate data types is very
tools and treatments of tomorrow. To unlock information buried
challenging for a number of reasons, including wide differences
in huge collections of patient data, the team at Siemens devel-
in data dimensionality, differences in predictive information con-
ops a case-based reasoning solution called CaseReasoner. In
tent in the various sources, and different scales or structures
a nutshell, CaseReasoner is a flexible data analytics and infer-
for different data types. To address this challenge, the team at
ence web tool that relies on a search engine. Given a query pa-
Siemens proposes an approach based on deep learning, which
tient, it permits to retrieve the most similar cases from a patient
can be seen as a form of non-linear dimensionality reduction.
collection and to perform inference based on their attached
Indeed, the original multi-modal patient data is encoded into a
information. For instance, one can predict for a new incom-
compact signature that has better properties in terms of scale,
ing patient the outcome of a particular therapy by aggregating
distribution and sparseness than the raw dataset for analysis.
therapy outcomes observed on the most similar patients.
Once plugged into the search engine of CaseReasoner, the compact nature of these signatures allows us to perform much
Search engines such as the one at the heart of CaseReasoner
faster and more reliable retrieval than when using original raw
consist of two main elements that define how the system rep-
data. CaseReasoner has the potential to support early iden-
resents data content of each case and how it derives similarity
tification and therapy selection in the different disease areas
measures between those cases. Careful design of the algo-
addressed by the MD-Paedigree consortium.
C a s e R e a s o n e r S c re e n s h o t : " P a t i e n t s l i k e m i n e "
24
MD-PAEDIGREE - Newsletter
GENETICS AND METAGENOMICS FOCUS MD-Paedigree’s Genetic and Metagenomic Analysis Study D-Paedigree is looking into three major areas
A critical point of this effort is the ability to compute and share
of interest in genetic research. The first is the
results from hundreds of thousands of data points in a clinically
highly heterogeneous and multifactorial (i.e.
meaningful way. Our research has succeeded in solving the
mendelian) disease cluster of Cardiomyopa-
information complexity challenge thanks to the computational
thies through the use of next generation sequencing. Fiftysix
tool developed by Md-Paedigree which made possible to dis-
genes are being tested to find classes of patients based on
cover complex relationships and hidden trends in the raw data.
genotype/phenotype correlations.
This is an important acheivement thanks to which we hope to
M
find more interesting correlations in the following months of the The second area focuses on obese children, in particular to
study.
those with cardiovascular risk. Currently, tens of gene variations are being investigated which are known to be related in some way with obesity and with its complications. The final objective here is to find useful markers to predict risk of cardiovascular disease in obese patients.
The The line of research is potentially the most original as it deals with the emergent field of gut microbiota analysis.
Gut microbiota consists of all the microbes colonising human intestine which we carry starting few hours after birth and for the rest of our lives. About 1.3 kg of bacteria populate our intestine, serving very important functions in immune response and gene expression modulation. Data have been collected from a large cohort of patients and subsequent analysis has confirmed previous findings from other studies of genetic differences in the composition of phyla species of bacteria in target patients. More interestingly this study has drawn attention to results coming from the gut microbiota analysis in children affected by juvenile idiopathic arthritis. There is no recorded study of this disease at the gut microbiota level and what was found is that there are some major changes of the composition of bacteria from the baseline to the remission phase of the disease. MD-PAEDIGREE - Newsletter
25
Microbiota and Microbiome: an introduction
T
he human body contains over 10 times more
that the gut microbiome performs numerous important bio-
microbial cells than human cells and each hu-
chemical functions for the host, and disorders of the micro-
man body along with intestinal and other mi-
biome are associated with many and diverse human disease
crobiota forms a complex ecosystem the parts
processes.
of which interactively performs a variety of biological processes. The human gut is a lush microbial ecosystem containing
Synergic meta-omics or "systems biology"-based approaches
about 100 trillion microorganisms, whose collective genome,
are now able to describe the gut microbiome at a detailed ge-
the microbiome, contains 100-fold more genes than the entire
netic and functional level, providing new insights into its im-
human genome. This has led some researchers to regard the
portance in human health by mapping microbiome variability
human body as a ‘superorganism’ in regard to its indigenous
among individuals and populations. This has established the
microbes, and refer to the composite genome as the human
importance of the gut microbiome in the disease pathogenesis
‘metagenome’. In this perspective sequencing the microbiome
of obesity and cardiovascular diseases, and in intestinal condi-
can be viewed as a logical albeit ambitious expansion of the
tions, such as inflammatory bowel disease. Thus, understand-
human genome project.
ing microbiome activity is essential to the development of future personalized strategies of healthcare, as well as potentially
The colonic microbiome is among the most densely populated microbial habitats on Earth. Recent studies have suggested
26
MD-PAEDIGREE - Newsletter
providing new targets for drug development.
GUT Health
M
icrobiotas live in all over
preliminary analyses, we then developed
and inside our body, the
several technological tools, driven by re-
major component resid-
quirements from clinicians, integrated in
ing in the intestinal canal.
the overall platform following a ‘big data’
This ecosystem plays a crucial role in our immunitary response to environmental stimuli, modulated by multiple factors (e.g., diet, exposomes) and with a di-
Lorenza Putignani
Head of Parasitology and Metagenomcis Units Ospedale Pediatrico Bambino Gesù
rect and indirect effect on our health. At
paradigm for the description of genomes and metabolites. This approach allowed to
translate complex data arrays into
microbiota maps that integrate clinical, genomic and metabolome data.
birth, neonates start evolving the gut microbiota (also called “programming phase”) which, depending on type of feeding
These maps are being used to assist in the interpretation of
(mother vs artificial), defines the future health status of the baby
how the microbiota modulates clinical features. By interacting
and in the following few months brings his gut ecosystem close
closely with clinicians we have been able to accurately identify
to the one he will carry as an adult. Factors affecting the initial
key targets in the modulation process. For instance, we have
composition are many, and all very important as each con-
proven the possibility to improve microbiota’s compositions in
cur to the final profile which varies from the physiological (i.e.,
diseases using prebiotics or tailored diets, in such a way that
healthy microbiota) to the diseased microbiota.
allows to revert altered microbiota into physiological ones. Having defined some of these altered microbiota (a.k.a dysbiosis)
During the MD-Paedigree Project we have focused on different
we have designed and tested a variety of tools that are able to
kinds of diseases such as obesity and juvenile idiopathic arthri-
restitute the microbiota to an eubiosis state.
tis in paediatric subjects. We started with bench-work to map all the bacterial genomes in healthy phenotypes.
This approach is becoming strategic in the care and manage-
All these “meta-genomes” depict the whole microbiotic space
ment of multiple diseases, providing new lab-based diagnostic
clustered in ‘profiles’ of the microbiota. This made possible to
instruments and alternative clinical pathways under the frame-
observe different profiles in relation to healthy phenotypes and
work of ’System’s Medicine’, which is poised to become the
see what differences marked various diseases. Based on these
future paradigm of patient care.
MD-PAEDIGREE - Newsletter
27
Press Release (OPBG):
Microbiota: scientists discover bacteria that cause obesity and fatty liver disease
A
study conducted at the Bambino Gesù Hospital, recently published on Hepatology, describes for the first time a model of microbiota associated to these pathologies openinig new
clinical pathways to personalised probiotics. A new discovery has been made in the study of gut microbiota, the community of commensal microorganisms (a few billion, mostly composed by bacteria) who live in our digestive system and which were once known as Gut flora. The mechanisms which regulate their interaction with the human DNA and the surrounding environment are the root cause of various simple and complex diseases. A study of the Bmabino Gesù Hospital has described for the
The study made possible to trace In further detail two mod-
first time in international litterature a model of the mi-
els of microbiota corresponding to just as much profiles: the
crobiota associated to fatty liver and obesity. Clinicians
first, associated to onset fatty liver, is characterised by reduc-
and researchers at the hospital have discovered that within the
tion of the Oscillospira microbial genre and to the increase of a
gut of children suffering from obesity and and fatty liver there
specific molecule (2-butanone); the second instead is associ-
are some families of bacteria that are too abundant (Rumi-
ated to a more advanced stage of the disease (steatohepati-
nococcus and Dorea) when compared to those that inhabit a
tis), which is identifiable from the elevated number of microbial
healthy subject, while others are too scarce (Oscillospira). The
genres Ruminococcus e Dorea. The study was made possible
association between the alterations of the gut flora and obese
thanks to an original approach developed from the research-
subjects opens an entirely new way to create and prescribe
ers of the human microbiome unit together with those from the
personalised probiotics, with all the annexed advantages
hepatic research unit. The method consisted of integrating a
both in tems of health and in terms of social costs. The study
very large amount of data produced by second generation se-
has been recently published on the scientific journal Hepatol-
quencing platforms (i.e., Next Generation Sequencing) and the
ogy.
mass spectrometry platforms of the Bambino Gesù Hospital’s labs. From the integration of these “big data” it was possible to
THE PICTURE OF THE MICROBIOTA IN OBESE CHIL-
produce the first model of functional microbiota associated to
DREN WITH FAT LIVER
paediatric cases suffering from fatty liver and obesity.
In healthy subjects, the bacteria poulation that forms the gut microbiota show a very diversified environment. On the con-
PERSONALISED PROBIOTICS
trary, in all obese patients with fatty liver, some bacterial spe-
The study conducted by the Bambino Gesù Hospital shows
cies result particularly abundant (Ruminococcus and Dorea),
that the complete characterisation of the microbiota associated
while others (Oscillospira) are eccessively scarce if compared
to fatty liver will allow to select ad hoc probiotics for obese chil-
to a healthy subject. Hence, different imbalances are present
dren with steatosis, thus enabling the rebalancing of the organ
in it’s composition.
and a recovery to healthy weight. The choice and the associa-
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Science mag’s Special Issue: Gut Microbiota at Work On April 29, 2016 Science mag delivered a new issue with a special focus on Microbiota Analysis (just as us!). The special issue is available online and for free download (personal, non-commercial use only) at the magazines
www.sciencemag.org
tion of the active bacteria selected to fight such pathologies cannot disregard the personalised study of the gut microbiota profiles. Recent data on health expenditure show infact an increase in the number of prescriptions of the so-called “integrators”. To date, in Italy, those prescriptions count up to 6,6% of the total prescriptions given by specialists. At the first place are probiotics (12,6%) followed by products for the joints (8,9%) and ophtalmics (8,2%). Within the prescribers of probiotics, first of all are the paediatricians (73%), followed by gastroenterologists (9,8%)*. A calculation of the economic turnover correlated to probiotics estimated that in 2016 $42 billion will be spent globally.**
mental in the growing child, when the foundations to “prepare” the healthy state during all the following phases of life, from
“Studies on gut microbiota are fundamental in paediatrics to
childhood to adulthood and senescence, are being laid.”
highlight the correlation between microbial and metabolic profiles and the main pathoogies correlated to gastrointestinal al-
“From the study two highly relevant facts have emerged, espe-
terations – explains Lorenza Putignani, responsible for the
cially for paediatricians – added Valerio Nobili, respinsible for
Parasitology Unit of the Bambino Gesù Hospital – this opens a
the hepatic-metabolic diseases Unit of the Hospital – first of all
new path for systems medicine, enabling to highlight the role of
to have a healthy liver one must have a healthy gut, hence pop-
the microbial communities at the onset and in the progression
ulated by “friendly” bacteria. The second patency is that there
of the pathologies, an aspect which is considered to be funda-
is a necessity for a more scientific and “updated” prescription of probiotics for children. Infact from this work, stemming from the experience of the highly advanced technologies implemented at the Bambino Gesù Hospital, the strategic relevance of the role of probiotics in obesity and in the fatty liver has been confermed, but also and foremost the necessity to discover new and associations and formulations of bacteria to fight such pathologies. In parallel, the uselessness of the promiscuous existent formualtions has also been confirmed.”
*source: IMS medical audit 2014 **source: Marketsandmarkets MD-PAEDIGREE - Newsletter
29
Hereditary Cardiomyopathies: Why Genetic Testing matters?
Ilena Limura
H e a d o f C a rd i o m y o p a t h i e s Service – BMR Genomics
Barbara Sioniati
Biologist, co-founder and CEO of BMR Genomics
G
enetic testing helps clinicians to better identify
tations in different genes and effective analysis of all disease-
the disease etiology and allows appropriate
genes by traditional approaches is not feasible in a diagnostic
mutation(s) analysis for proband’s family mem-
setting. By the simultaneous analysis of 56 genes, CardioGen-
bers.
ica service represents a valuable tool for mutation screening in patients affected with hereditary cardiomyopathies, with re-
CardioGenica is a service developed by BMR Genomics within
duced time and costs. The MD-Paedigree project has allowed
the European project FP7- MD-Paedigree and in collaboration
to validate the protocol and the data analysis pipeline underly-
with Alessandra Rampazzo, associate professor of human ge-
ing the CardioGenica service for application in the molecular
netics at the University of Padua. By the use of next generation
diagnosis of inherited cardiomyopathies.
DNA sequencing (NGS) technologies, CardioGenica allows to analyse at the same time 56 genes involved in the main forms of hereditary cardiomyopathies, in order to identify disease-
CardioGenica
causing mutations. For patient who have a clinical diagnosis or suspicion of hereditary cardiomyopathy, CardioGenica provides the genetic data analysed and interpreted by an expert geneticist, starting from a saliva sample. Hereditary cardiomyopathies are a frequent cause of sudden cardiac death, often as first manifestation of the disease, in particular in young people and athletes under 35 years of age. Sudden death may be prevented by timely detection and intervention, however early diagnosis of these disorders is often very difficult due to the absence of clinical signs and symptoms in the early stages of the disorder and the high variability in the disease expression. When properly inter-
!
BMR Genomics
CardioGenica
Why to perform the genetic test? Genetic testing helps clinicians to better identify the disease etiology and allows appropriate mutation(s) analysis for proband’s family members. The specific field in genetic studies of heart muscle (cardiomyopathy) and conduction system abnormalities is highly heterogenous. In symptomatic individuals, genetic analysis aims to:
• Confirm a clinical suspicion; • Establish the diagnosis at an early stage of the disease, espec ially in cases where the phenotype can not meet all diagnostic criteria;
preted, genetic testing helps clinicians to better characterize
• Perform a differential diagnosis in patients with borderline phenotype;
the disease and allows diagnosis confirmation, early identifica-
• Optimise preventive strategies and therapeutic approaches.
tion of asymptomatic carriers, and interpretation of borderline clinical phenotypes. Genetic testing can provide a definitive answer, if the symptoms
!
In asymptomatic individuals at high risk of sudden death, such as athletes with positive family history of cardiomyopathy, genetic testing can be adopted as a preventive measure, contributing to early identify carriers of pathogenic mutations.
!
CardioGenica service, developed by BMR Genomics within the FP-7 MD-Paedigree project, aims to analyse 56 genes involved in the main types of hereditary cardiomyopathies, by the use of next generation sequencing (NGS) technologies. In terms of diagnostic performance, the high genetic heterogeneity and overlap of hereditary cardiomyopathies, along with double, triple or compound heterozygosity, makes the genetic analysis of such disorders with traditional methods a time-consuming, expensive and inconclusive process. NGS technologies have revolutionised the genetic testing approach, enhancing its potential. BMR Genomics offers a complete range of services, from production to interpretation of the genetic data, allowing the access to a wider genetic profiling, with reduced time and costs.
!
Fields of application Our panel allows the analysis of 56 genes involved in a wide spectrum of phenotypes of heart muscle and conduction system abnormalities: • Dilated cardiomyopathy (DCM) • Hypertrophic cardiomyopathy (HCM) • Arrhythmogenic right ventricular Cardiomyopathy (ARVC) • Brugada Syndrome (BgS) • Long QT Syndrome (LQT) • Short QT Syndrome (SQT) • Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT)
• Left Ventricular Non Compaction (LVNC)
!
What do we offer
are not enough for a diagnosis and determine the cause of the
The CardioGenica service includes:
cardiomyopathy. It can also be useful when making decisions
• Genomic DNA extraction;
for clinical care. If the genetic cause of a patient is known, it
• Bioinformatic analysis;
is possible to test relatives in order to identify those at risk of
• Validation of the clinically relevant variants, by Sanger sequencing
• Genetic counseling; • NGS DNA Sequencing; • Variant annotation and filtering, using the proprietary software 'SNP-shot';
!
• Genetic report with characterisation of the validated variants
developing the disease. Only family members with a mutation should undergo appropriate clinical monitoring or alter their lifestyle. Hereditary cardiomyopathies can be explained by mu30
MD-PAEDIGREE - Newsletter
CardioGenica
BMR Genomics
Book Review:
Gut: The inside story of our body’s most under-rated organ
Giulia Enders
A
Scribe Publications
recent publishing sensation “Gut” the inside story of our body's most underrated organ by Giulia Enders makes a good case showing that
Some (mainly paediatric) bibliographical references
our gastrointestinal tract is not only our body's most under-appreciated organ, but also "the brain's most im-
Gut microbiota: next frontier in understanding
portant adviser". A 25-year-old doctoral student at Goethe Uni-
human health and development of
versity in Frankfurt, Giulia Enders was first noticed in 2012 by
biotherapeutics Prakash
winning the first prize at the Science Slam with a 10 minute lec-
S, Rodes L, Coussa-Charley M, Tomaro-Duchesneau
ture that went viral on YouTube, prompting a publisher to com-
C. Biologics 2011; 5: 71-86.
mission her. Appropriately called Darm Mit Charme ("Intestine with Charm") in it’s original German title, the book has now sold
Prebiotics and probiotics; modifying and
more than 1.3 million copies worldwide and is distributed in over
mining the microbiota
18 countries. Enders thinks that beyond our brain and our heart,
Eamonn M.M. Quigley, Pharmacological Research
we should equally be grateful to the complex achievements of
61, 3, March 2010, Pages 213-218 Nutraceuticals and
our intestine, without discarding it as little more than a tube that
Functional Foods
produces "small brown heaps and farting noises". The Good Gut: Taking Control of Your Weight, The author argues that if only we can thought differently about
Your Mood, and Your Long Term Health
our gut by understanding its role beyond basic digestion, we
April 21, 2015, Justin Sonnenburg (Author), Erica
might be much more appreciative and kinder to it. Thanks to the
Sonnenburg (Author), Andrew Weil (Foreword). The
author’s gift for metaphor and her vivacious explanations of our
Sonnenburg Lab, Stanford Press.
digestive system, the reader is driven deeper and deeper into the human gastrointestinal tract. Starting from the mouth, the
The microbiome and obesity: Is obesity linked
book brings us on a tour de force of our gastrintestinal system,
to our gut flora?
by breaking a series of taboos along the way and ending on how
Franklin Tsai and Walter J. Coyle (2009) CURRENT
things work ‘at the very bottom’.
GASTROENTEROLOGY REPORTS 11, 4, 307-313
Darm mit Charme can be seen as a practical guide on our gut
Gut microbiota: Changes throughout the
physiology to improve our lives, how the nervous system of the
lifespan from infancy to elderly
gut affects our mood, issuing in the meanwhile valuable lifestyle
O'Toole PW, Claesso MJ. Int Dairy J 2010; 20:281-91
and nutritional tips on how to apply the physiology of our intestine to our daily lives: the best position to defecate when sitting on our “marble thrones”, getting complete protein intake on a vegetarian diet, strategies for reducing unneccessary attacks
While making us giggle this book also instills in the reader a pro-
of vomiting, constipation, bad breath and finally a medical field
found sense of appreciation for the everyday miracles that our
guide to our faeces, accompanied all along by quirky illustrations
body performs silently to protect us, helping us to live a little
of her sister (graphic designer Jill Enders).
healthier and happier lifes. MD-PAEDIGREE - Newsletter
Enhanced Consent: a vision for Patient Data Protection and Data Management Discussion paper drafted by Edwin Morley-Fletcher, Lynkeus - ICT2015 Networking Session
→ Semantically advanced digital repositories → Stratifying patient cohorts: Patient like mine
→ Empowering citizens through Health Data Cooperatives
→ Social media for “Patients exactly like us”
→ Data donation and data inheritance
→ Who are the data owners?
→ Informed, dynamic and enhanced consent
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MD-PAEDIGREE - Newsletter
→ Patient stratification
→ Secure Multiparty Computation
→ The challenges of scalability and anonymization
→ Homomorphic Encryption
→ HIPAA and FedEHR
→ Securing universal verified access
→ Differential privacy
→ The block-chain system
MD-PAEDIGREE - Newsletter
33
Enhanced Consent: a vision for Patient Data Protection and Data Management
Discussion paper drafted by Edwin Morley-Fletcher, Lynkeus (ICT 2015 Netwroking Session) Big Data Healthcare at ICT’13
which has meanwhile unrolled, and of some key technological developments which are now available.
Two years ago, at ICT’13 in Vilnius, we introduced a successful networking session on Big Data Healthcare after having, beforehand, circulated a “discussion paper” (“Big Data Healthcare” 1, followed in 2014 by a subsequent article on “Healthy Data?” 2) where some issues had been raised.
We had stated, then, that three political, academic and business issues were – and in fact still are – at the core of the debate: the need to ensure that EU citizens’ data are adequately protected, the need for open access to data for research purposes, and the need for Europe to develop a vibrant data analytics industry, capable of investing a growing amount of resources into the break-through innovations in healthcare that the appropriate utilisation of Big Data promises to deliver.
The speedy adoption of an updated legal and ethical framework that we had then advocated is still pending in the trilogue3 between the European Commission, the European Parliament and the European Council. We have deemed that this is, therefore, the right time for coming back on some issues, taking stock of the discussion
Semantically advanced digital repositories
1
The discussion paper proposed in Vilnius can be found on http://ec.europa.eu/digital-agenda/en/news/bigdata-what-it-and-why-it-important. 2 The paper was published on the Horizon 2020 Projects portal and on the Digital agenda, see: www.horizon2020projects.com and http://ec.europa.eu/digitalagenda/en/news/discussion-big-data-and-healthcarenew-knowledge-era-world-healthcare. 3 The basis for the trilogue has been the Commission's Data Protection Regulation proposal of January 2012, the Parliament legislative resolution of 12 March 2014 and the General Approach of the Council adopted on 15 June 2015.
In order to reap this growing value – we had added – there will be the need not only for clinicians and researchers to acquire Big Data analytics skills and services, but also to develop data repositories which adhere to international standards for the preservation of data, set common storage protocols and metadata, protect the integrity of data, establish rules for different levels of access and define common rules that facilitate the aggregation of datasets and improve interoperability.
futureconsent@lynkeus.eu
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MD-PAEDIGREE - Newsletter
some of its initial features. This early success has encouraged us to go further and to try now to better explicit the vision, which lies behind the MD-Paedigree repository that we are committed to develop, and which constitutes also the basic Infostructure used by another EU-funded project, Cardioproof.
between researchers and clinicians to allow for data intensive pathophysiological diagnoses.
We are persuaded that repositories such as MDPaedigree’s can play a special role in “testing” the prior knowledge of clinicians, who identify the data features deemed to be key for specifying a patient’s treatment, versus the correlations that big data crunching may highlight, possibly leading to further knowledge discovery. Indeed – we had said in Vilnius – by statistically and semantically reasoning on the data, existing pathophysiological patterns may be revealed and inputted as a first step in a fractional factorial and model driven research process supporting physicians in their iterative and interactive quest to discovering new knowledge. Stratifying patient cohorts: “patient like mine” There were two main goals that we identified, and that have been the drivers of our activity. A first goal has been to provide model-driven patientspecific predictions and simulations, and consequent optimised personalised clinical workflows, to allow for advanced similarity search among patients, such that clinicians can find “the patient like mine”, and to get support through risk stratification and outcome analysis. Eventually – we had said – it is hoped that specific pathophysiological patterns (“disease signatures”) can be detected, refined and made available to other clinicians and researchers in the form of pattern libraries. These pattern libraries, identifying homogenous groupings among patients and model similarities, shall be shared
Social media for “patients exactly like us” A second, allied, goal had been identified in the potential to revolutionise health communications by making it possible, on the basis of our semantically advanced repository, to use social media among patients aware of sharing highly similar conditions (“patients exactly like us”), empowering them to bridge the gap with the clinicians, especially in the case of paediatric patients and their parents. We are now in the position of being able to provide a more in depth understanding of what are the legal, regulatory, and technical preconditions which can best facilitate attaining such goals. Who are the data owners? The issue of the ownership of health data (the rights of patients to their data, or “the data subject's legitimate interests”), to avoid others use these data in a supposedly anonymised form, usually without the patients’ knowledge or consent, to earn profits without letting patients participate in the wealth generated from adding their respective data to large repositories, had already been highlighted, and attention had been drawn to Anne Philips’ Whose Bodies, Whose property? book (2013), which advocated introducing “a levy on the proceeds of research to be returned, either as assistance to the specific group suffering from the disease, or to the wider community”. We had thought, in fact, that the right of data subjects “to be forgotten” would imply, de facto, some sort of ownership of data relating to them, and therefore the right to eventually donate or
futureconsent@lynkeus.eu
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35
sell them. Even though we had been aware that customarily, when it comes to data relating to individual subjects, the preferred model for the advancement of scientific research has not been to allow intellectual property (IP) rights deriving from raw datasets, so that IP rights seemed more likely to be attached to the analytic work performed on the data, in the same way as current IP law covers arrangement of facts, but not the facts themselves. Possibly, as implied by the remark from the European Data Protection Supervisor, that “data have value only if accompanied by a means of extracting knowledge”4. The debate which has ensued in the last two years has, however, highlighted a growing concern “not only to find a shared home for personal health data but also to give individuals the right to own them”5. According to Kish and Topol, citizens should in fact safely store and manage all their health data (medical, m-health, genome etc.) in some sort of “individual accounts”, which we will further return to in this paper. Kish and Topol think that the time is ripe for starting to talk “about creating a health data resource in a much broader and more universal context, controlled by the individuals who supply the data”6. To this end they have introduced the term UnPatient7 to characterise a new model of data ownership. This term is meant to have a double meaning: on the one hand, the customary condition of “the patient subjected to medical paternalism and information asymmetries”8; the other hand, the idea that “it has taken far too long to become free to use our 4
Preliminary Opinion of the European Data Protection Supervisor, Privacy and competitiveness in the age of big data: The interplay between data protection, competition law and consumer protection in the Digital Economy, March 2014. 5 Leonard Kish and Eric Topol, Unpatients—why patients should own their medical data, “Nature Biotechnology”, 33, September 2015, p. 921. 6 Cit, p. 924. 7 http://unpatient.org. 8 Ibid.
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MD-PAEDIGREE - Newsletter
medical data as we see fit and to own it”9. Without connecting to their medical data – add Kish and Topol – “people are unnecessarily being hurt and dying”10. Accordingly, they urgently seek “to promote ownership of one’s medical data as a civil right and as a pivotal strategy to further digitize medicine, providing a new resource to potentially help every individual who willingly participates”11. Previously, some type of data ownership suggestion had come already from the World Innovation Summit for Health (WISH) report, where it had been posited that patients “should form coalitions aimed at having their data handled by common trustee organisations”12: such “data management co-operatives” would take care of safely storing and managing all health data “in individual accounts”13. The citizens adhering to these cooperatives would not only have access to them from anywhere anytime, but they would also be able to share subsets of their data, or all of them, with doctors, friends or biomedical and pharmaceutical research. Empowering the citizens through Health Data Cooperatives In an analogous vein, in 2015 the Strategic Research and Innovation Agenda (SRIA), titled Shaping Europe’s Vision for Personalised Medicine, completed by the PerMed consortium for the European Commission, has suggested starting Health Data Cooperatives (HDCs) to
9
Ibid. Ibid. 11 Ibid. 12 A. Pentland, T.G. Reid, and T. Heibeck (eds.), Revolutionizing medicine and Public Health, Report of the Big Data and Health Working Group, The World Innovation Summit for Health (WISH), Qatar Foundation for Education, Science and Community Development (QF), 2013. 13 Ibid. 10
futureconsent@lynkeus.eu
facilitate “a growing involvement of patient and citizen interests”, as well as an “increased role of patient advocacy and support groups”, and a “rise in health literacy of patients and citizens”14. These trends – they posited – are likely to change the way that Healthcare clients and providers interact in the future, requiring the “definition of new responsibilities and financial models”.15 According to PerMed, “the construction of national or regional citizen-owned and citizencontrolled health data cooperatives, in which citizens and patients can securely store, manage and share their data, will […] not only make these data more readily available, it will force data out of the incompatible data silos of national healthcare systems and so improve interoperability. Such a cooperative system represents a possible way for citizens to obtain the true value from the secondary use of their data for their own health and that of society. The cooperatives would compete in the personal data market to maximise the scientific and economic value of the data that citizens have agreed to share for the cooperative’s members. Personal and economic benefits for cooperative members by the control of their personal data could be the new driver for the implementation of a more effective data-driven personalised healthcare system”16. Data donation and data inheritance The awareness of the personal and social significance of anonymised individual patient and personal data for preventative and predictive purposes in healthcare, and of the implied data ownership issue, had prompted us to also take into account what we had called in Vilnius a 14
PerMed, Shaping Europe’s Vision for Personalised Medicine, Strategic Research and Innovation Agenda funded by the European Commission (http://www.permed2020.eu), June 2015, p. 9. 15 Ibid. 16 PerMed., cit., p. 12.
“tocquevillian approach”, namely the remark, by Alexis de Tocqueville, that the laws on inheritance “ought to be placed at the head of all political institutions, for they exercise an incredible influence”17. The data ownership principle could in fact also lead to promoting “data donation” policies, and “data inheritance” mechanisms. The latter could also be conceived of as being automatically applied after a certain period would elapse from the data subject’s time of death, unless they would have explicitly opted-out prior to death. This suggestion had come in Vilnius with the specification that a different treatment should, however, be considered for the parts of these data whose public availability could have detrimental consequences for the relatives of the deceased, such as genetic information. New technological developments, and enhanced privacy security mechanism, may now overcome such proviso, as we shall soon see. With regard more generally to “digital legacy”, an ICT 2015 Discussion Group has very recently remarked that “digital assets […] are part of legacy of the deceased and should be available and transferable to heirs or other persons authorised by the deceased”. In consideration of the fact that “transferability of digital assets in case of death has not been completely regulated by law yet and there is no existing unified legal system within the European Union”, one participant in the Discussion Group, Paweł Szulewski, has advocated that the EU “should prepare a European framework and guidelines in the area of transferability of digital assets”18.
17
A. de Tocqueville, De la démocratie en Amerique, Vol. I, ch. 3, 1833. 18 http://ec.europa.eu/digitalagenda/en/content/digital-legacy-it-possible-transferdigital-assets-case-death. See also: P. Szulewski, Transferability of Digital Assets in Case of Death,
futureconsent@lynkeus.eu
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With regard more specifically to data donation in healthcare, in a position paper issued in 2014, the European oncology community has suggested that “patients should have the right to ‘donate’ their data and tissues to health research, as well as to retain access to the tissue and data donated, hence ensuring their ability to obtain relevant information related to his/her condition; they should also have the right to deny their consent and withdraw it at any time”19. Regulation – they added – should “avoid the notion of a ‘specific’ consent, which would result in researchers needing to obtain continuous patient re-consent every time new research is carried out”. The process should be a ‘one-time’ consent, which means that “patients will be informed that their data/tissues will be used for future research, and they will be informed about the conditions under which their data and tissues will be stored, making the protection safeguards a part of their consent”. The same position paper also highlights that in the fields of public health and epidemiological research, based on population disease registers, “a derogation from the obligation of any form of consent is essential”20.
According to the proposed EU regulatory framework, data usage always implies a preliminary personal consent, and “the data subject’s consent means any freely given specific, informed and explicit indication of his or her wishes by which the data subject, either by a statement or by a clear affirmative action,
Tagungsband IRIS 2015 (LexisNexis Best Paper Award at the International Legal Informatics Symposium 2015 in Salzburg). 19 Risks of the new EU Data protection regulation: an ESMO position paper endorsed by the European oncology community, “Annals of Oncology”, 25, 2014, pp. 1458–1461. 20 Ibid.
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However, the forthcoming European Data Protection Regulation (EDPR) also specifies that “personal data may be stored for longer periods insofar as the data will be processed solely for historical, statistical or scientific research purposes in accordance with the rules and conditions of Article 83 and if a periodic review is carried out to assess the necessity to continue the storage”22. We consider this statement as particularly important, since it is the basis for allowing the availability of data for long-term research, and for Open Science, as necessary under the assumption that knowledge discovery is “an iterative, longterm process”23, and not a one-off endeavour. As the European Data Protection Supervisor (EDPS) has stated in his recent Opinion 3/2015, “researchers and archivists should be able to store data for as long as needed”24, subject to safeguards “preventing personal information being used against the interest of the individual, paying particular attention to the rules governing sensitive information concerning health”25. Dynamic consent
Informed consent
signifies agreement to personal data relating to them being processed”21.
In a publication following the work developed within the UK-funded Ensuring Consent and
21
European Commission's Data Protection Regulation proposal, Ch. I: General Provisions, Art. 4: Definitions. 22 EDPR proposal, cit. Ch. II: Principles, Art. 5: Principles relating to personal data processing, letter (c). 23 J. Kaye, E.A. Whitley, D. Lund, M. Morrison, H. Teare and K. Melham, Dynamic consent: a patient interface for twenty-first century research networks, “European Journal of Human Genetics”, 2014, pp. 1–6. 24 G. Buttarelli, Europe’s big opportunity: EDPS recommendations on the EU’s options for data protection reform, Opinion 3/2015, Brussels, 28 July 2015. 25 Ibid.
futureconsent@lynkeus.eu
Revocation (EnCoRe) project26, some authors have, however, expressed their fear that the socalled “broad consent” may not remain a lawful option for research. Furthermore, deeming that “collection of one-off consent for research can often occur at a stressful time for the person concerned, such as before treatment or surgery”27, they have looked for ways to remove this pressure and allow participants “to return to their decisions and review their consent preferences in their own time”28. These stimulating authors fear, in fact, that “many of the traditional protections used in research such as anonymisation, coding, and pseudonymisation, are increasingly tested or rendered ineffectual by advanced data collection”29, and that, consequently, “the potential that individuals can be identified either directly or indirectly from the data”30 may eventually end up by reasserting the need to “invoke the requirement for explicit consent under data protection and privacy law”31. They seem, in reality, to have anticipated a statement by the EDPS: “Individuals should be able to exercise more effectively their rights with regard to any information which is able to identify or single them out, even if the information is considered ‘pseudonymised’32. These concerns have led Jane Kaye and her coauthors to propose a “dynamic consent” 26 The EnCoRe project was funded by the Technology
Strategy Board, the Engineering and Physical Sciences Research Council, and the Economic and Social Research Council. It unrolled from June 2008 to April 2012, and was developed in the context of three biobanks: the Oxford Radcliffe Biobank, the Oxford Musculoskeletal Biobank, and the Oxford Biobank. 27 J. Kaye et al, Dynamic consent, cit., p. 5. 28 Ibid. 29 Cit., p. 2. 30 Ibid. 31 Ibid. 32 G. Buttarelli, Europe’s big opportunity, cit.
approach, whereby, “rather than being restricted to the opportunity only to give broad consent to the use of their samples and data, individuals could provide different types of consent depending upon the kind of study”33. These consent preferences would “travel securely with their samples or data so that third parties know the scope of the consent that applies”, and a secure consent interface, tailored to individuals’ needs, would “allow participants to change their consent preferences reliably”34. Dynamic consent would meet “the highest international ethical and legal standards for consent in a world where data protection laws are in flux”35. It would not be meant, therefore, to be “a replacement for existing models such as broad consent, but rather a facilitation tool to improve how that consent is obtained, understood and acted upon”36. Given an ‘opt in’ and ‘opt out’ approach to choice, a participant would still be enabled to “choose to give a broad consent and not receive updates and so on, but if at some future point they [would] wish to become more engaged they [would] have the option to do so”37. Enhanced Consent In 2013, our Vilnius “discussion paper” had proposed the inclusion of a concept of enhanced consent, whereby the data subject would have been specifically able to exclude certain data usage whilst allowing data utilisation for the benefit of, for example, healthcare research, alongside maintaining and ensuring that consent can be withdrawn and data completely deleted. In the same paper we also posited that Big Data allow for “long-tail medicine” drugs with enhanced personalised information content,
33
Ibid. Ibid. 35 Ibid. 36 Cit. p. 5. 37 Cit. p. 3. 34
futureconsent@lynkeus.eu
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based on customized algorithms tackling the individual disease conditions. The complex nature of any disease – we had added – is arrived at by a multitude of pathways, influenced by genome, metagenome and environmental exposures: thus, no single intervention or set of interventions applied in a blanket fashion to the population would be able to adequately tackle it. Whereas, only personalised interventions, resulting from a big data focus on the disparate underlying genotypic and phenotypic drivers of the disease would offer a possible solution and provide appropriate decision support to the clinicians. It has been pursuing such a goal that MDPaedigree and Cardioproof have progressed, sharing the belief that a way to enhance consent is to make it understood by the data subject that he/she will be enabled to get the best preventative information on his/her condition over the course of time. Patient stratification This approach is being tackled, specifically within MD-Paedigree, by the Siemens CaseReasoner, which is a tool for applying similarity search machine-learning on all the data in the repository, eventually stratifying all included data subjects into ever more personalised clusters or cohorts of patients, with specially similar characteristics. CaseReasoner is complemented by HES-SO’s BiTeM case-based retrieval system38, which supports clinicians in identifying similar patients on the basis of automated text analytics, applied in the first place to hospital discharge summaries. MD-Paedigree’s and Cardioproof’s common Infostructure has the ambition of providing an innovative marketplace where big health data, patient-specific modelling, stratification, and further associated services, will be able to converge, bringing together data subjects, clinical 38
http://bitem.hesge.ch.
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MD-PAEDIGREE - Newsletter
centres, biomedical research and industry, making personalised medicine get closer to reality. The incentive of a personal return A way to make this succeed depends, in our view, also on the capacity of guaranteeing some secure and practical personal return for all those who will be willing to contribute with their data, for instance by: 1. making it easier for patients to access further clinical opinions (if this is what they need) having at hand all their data; 2. ensuring that, by regularly checking the information accrued on their personal stratification or “disease signature”, they will be able to follow any future knowledge discovery regarding their condition; 3. empowering them with specially designed social media for appropriately getting in touch, if so they wish, with similar data subjects, equally willing to be contacted.
The challenges of scalability and anonymization The necessary preconditions are, however, the assurance that the model-driven digital repository under construction will prove to be an easily accessible, collaborative, secure, and scalable platform, and that there will be the guarantee of providing the best possible level of data subjects’ privacy. Which is not at all a trivial task, given the triple requirement of: a) making use of all type of highly sensitive data, for providing the most advanced stratification and patient-specific modelling, b) allowing the data to be recurrently available for further update and potential future knowledge discovery, c) combining full anonymization with the possibility for the data subject to access his/her data for second opinions, as well
futureconsent@lynkeus.eu
as for stratification cluster awareness, and for optional interaction with very similar patients, not to mention exerting the “right to be forgotten”. Already in Vilnius we had been aware that the demands for informed consent and deidentification implied the implementation of appropriate counteracting measures to prevent deductive disclosure, i.e. the ability to re-identify data based on some inferences either by aggregating more data or by querying the available dataset. A simple and effective approach – we had remarked then – could consist of setting a minimal threshold on query engines so that queries returning less than a minimum number of cases would not inform the user on the real count (K-anonymity). An additional approach consisted of devising an algorithm and an interface allowing to inject random noise into datasets in order to prevent the possibility of re-identification whilst still producing accurate answers to the research queries. These are all areas where significant progress has been attained, and further levels of even stronger anonymization can now be applied, having recourse to a variety of alternative solutions. HIPAA and FedEHR Such developments allow to go significantly beyond the American Health Insurance Portability and Accountability Act of 1996 (HIPAA), according to which de-identified health information is not Personal Health Information (PHI), and thus is not protected by the Privacy Rule.
employers, or household members have been removed, and “the covered entity [has] no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information”40. To reach a comparable level of prior deidentification for making data queries available on the cloud, MD-Paedigree makes use of ad hoc tools especially developed within the Infostructure. Data protection procedures for privacy, security, and storage, are provided by the gNὑbila’s FedEHR product41, made up from 10 software modules, each of which addresses functional requirements in the field of data protection and confidentiality. On top of this, MD-Paedigree also provides an automated preliminary Data Curation tool for outlier detection, developed by Athena RC42. Differential privacy As already mentioned, a further level of “privacyby-design” de-identification can be based on the injection of random noise, with the effect of eventually producing “synthetic data sets” which are sufficiently scrambled and muddied to make them no more identifiable. This approach is also called “differential privacy,” meaning that a query on a database is “differentially private if the contribution of an individual in the database can only marginally influence the query result”43. More precisely, the contribution of each single
40
Ibid. www.fedehr.com. 42 http://www.imis.athena-innovation.gr. 43 F. Eigner, A. Kate, M. Maffei, F. Pampaloni, and I. Pryvalov, Achieving Optimal Utility for Distributed Differential Privacy Using Secure Multiparty Computation, in: P. Laud and L. Kamm (eds.), Applications of Secure Multiparty Computation, IOS Press, 2015, p. 82. 41
According to HIPAA, data are de-identified when all 18 elements39 that could be used to identify the individual or the individual's relatives, 39
These elements are enumerated in the HIPAA Privacy Rule: http://privacyruleandresearch.nih.gov/pr_08.asp.
futureconsent@lynkeus.eu
MD-PAEDIGREE - Newsletter
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entry to the query result is bounded by a small constant factor, even if all remaining entries are known. “A deterministic query can be made differentially private by perturbing the result with a certain amount of noise. The amount of noise can depend on the query itself, and a variety of perturbation algorithms have been proposed for different queries and datatypes (e.g. numerical and non-numerical data, buckets, histograms, graphs)”44. Secure Multiparty Computation A different approach can be based on further developments of a methodology, already invented in the late ‘70s, and called “secret sharing”, based on the principle of breaking down each value to several pieces. This has evolved into the Secure Multiparty Computation (SMC) approach, where parties provide their inputs to a cryptographic protocol that is used to compute a pre-agreed function in such a manner that nothing that a party sees during the protocol can be deduced from the party’s inputs and outputs. This way sensitive data can be safely collected, stored, and analysed, and their aggregated result can be published, without compromising the privacy of the data owner. Input data never leave the hands of the owner, only the final results of the computation are shared with the partners. An EU-funded Future and Emerging Technology project, led by the Estonian company Cybernetica, has aimed at employing Usable and Efficient Secure Multiparty Computation (UaESMC)45, and one important subsequent outcome has been a remarkable book, just published in 201546. As practical exploitation, Cybernetica now offers a commercial tool, Sharemind47, which operates as 44
Ibid. http://www.usable-security.eu. 46 P. Laud and L. Kamm (eds.), Applications of Secure Multiparty Computation, cit. 47 http://sharemind.cyber.ee. 45
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MD-PAEDIGREE - Newsletter
a virtual machine for privacy-preserving data processing, relying on share computing techniques48. Homomorphic Encryption An, as yet, ultimate level of de-identification can be based on Homomorhic Encryption, i.e. the methodology which allows computations on encrypted data without decrypting them, which can be, of course, extremely interesting for otherwise always identifiable data, such as, for instance, all genetic and metagenomic data. Also Jane Kaye and the other co-authors advocating “dynamic consent” have referred to this approach, remarking that “homomorphic encryption provides new, privacy-enhancing, technical capabilities allowing the processing of sensitive information”49, never decrypting “identifiable information but still [being] able to generate analytical results from the encrypted data”50. While the statement by the Cloud Security Alliance, that “strong encryption with key management is one of the core mechanisms that Cloud Computing systems should use to protect data”51, had become a common place, fully homomorphic encryption52 long appeared as a mirage, since it was until recently making it too
48
D. Bogdanov, L. Kamm, S. Laur, P. PruulmannVengerfeldt, R. Talviste, J. Willemson, Privacypreserving statistical data analysis on federated databases, APF 2014, Proceedings of the 2nd Annual Privacy Forum 2014, Springer 2014, pp. 30-55. 49 J. Kaye et al., Dynamic Consent, cit., p. 3. 50 Ibid. 51 CSA, Security Guidance for Critical Areas of Focus in Cloud Computing, V. 2.1, December 2009, p. 60. 52 C. Gentry, A fully homomorphic encryption scheme, PhD thesis, Stanford University, 2009; Id., Fully homomorphic encryption using ideal lattices, in: M. Mitzenmacher (ed.), , 41st Annual ACM Symposium on Theory of Computing, Bethesda, Maryland, May 1-June 2, 2009, ACM Press, pp. 169–178. Craig Gentry.
futureconsent@lynkeus.eu
slow by supporting all functions within a single scheme. Systems like CryptDB53, followed by HELib54, have been showing, however, that, by using a variety of specialised algorithms for different encryption schemes, it becomes possible to perform a series of different computations with the encrypted results in real time. This approach can have powerful consequences for MD-Paedigree’s similarity search and stratification tools like CaseReasoner, allowing to make secure and anonymised use of all type of sensitive data. Further developments like Mylar55 enable users of a cloud application to also share data with one another, probably fraying the way for what MDPaedigree has called the “patients exactly like us” functionality, to run in parallel with the “patients like mine” functionality, referring to clinicians interested in getting decision support based on outcomes analyses. In fact, Mylar is a system allowing the server to perform keyword search over encrypted documents, even when the documents are encrypted with different keys, and the users to share keys and data securely, also in the presence of an active adversary. Finally, Mylar ensures that the client-side application code is authentic, even if the server is malicious56.
53
R.A. Popa, C.M.S. Redfield, N. Zeldovich, and H. Balakrishnan, CryptDB: Protecting Confidentiality with Encrypted Query Processing, Proceedings of the 23rd ACM Symposium on Operating Systems Principles (SOSP), Cascais, Portugal, October 2011; R.A. Popa, N. Zeldovich, and H. Balakrishnan, Guidelines for Using the CryptDB System Securely, Cryptology ePrint Archive, Report 2015/979. 54 HELib 55 https://css.csail.mit.edu/mylar. 56 R.A. Popa, E. Stark, J. Helfer, S. Valdez, N. Zeldovich, M.F. Kaashoek, and H. Balakrishnan, Building web th applications on top of encrypted data using Mylar, 11
Securing universal verified access These latest cryptographic developments show, on one hand, that the kind of technology that seems to be most needed would be a protocol that does not rely on anyone trusting anyone else, and yet guarantees appropriate information accountability, determining through computational monitoring how the data are used and whether a specific use of data complies with laws and regulations. Analogously, it would be necessary to complement homomorphic encryption by a functional certification service, providing a kind of mediator having the task of accepting or rejecting queries from users. The Infostructure would need to check a digital certificate to ensure that the user has the right to issue a particular query before carrying it out on the database. On the other hand, Kish and Topol advocate, as ideal digital ownership system, a data network, which “would foster better trust in the accuracy of data; connect people to facilitate enhanced sharing, anonymity and security; create a single system of exchange, standard methods of exchange and better metadata to assess the value of a piece of information; and finally enable ways for all involved to benefit from sharing so as to maximize sharing and value”57. Ultimately – they say – “once the infrastructure is built with clients and nodes for such a data network, transferring secure health data could be as easy as sending an e-mail is today”58. The block-chain system The block-chain system, i.e. Bitcoin’s underlying technology, seems to Kish and Topol as a way for solving such an apparent contradiction, and enabling digital property on a global platform. USENIX Symposium on Networked Systems Design and Implementation, 2014. 57 L.J. Kish and E.J. Topol, Unpatients, cit., p. 923. 58 Ibid.
futureconsent@lynkeus.eu
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Bitcoins– they specify - are “bits of digital property (‘coins’ are a ledger of transactions, a shared database of who owns what at a given point in time). Ownership is enabled by network consensus […] Its digital ownership model creates a shared, agreed-upon record of data. Using and repurposing of the block-chain, wallets and ‘proof of work’ components are already being adapted for multiple forms of data that are beginning to look like a global, distributed data ownership store. A worldwide health data graph enabled by health data ownership may not be far behind”59. They envisage the creation of a new global infrastructure for data, “accessible through Bitcoin wallet addresses, or something like them”60, which would have “the potential to provide a universal patient identification mechanism”61, separating personal information from health data “as each data element can exist and be trusted independently”62.
requires, for the first two Ps, that the citizens be enabled to know to what cluster (“disease signature”) do they belong to, as well as what degree of knowledge has been acquired on that specific condition, and to be informed when further knowledge discovery will be attained. The third P requires that clinicians and patients can both request or purchase patient-specific modelling for personalised simulation and prediction. The fourth P requires that patients can get to know each other (if so they wish) and learn from each other’s experience, while also jointly exerting a stakeholder role in fostering further targeted research by providing they data and letting them be available for a long term for Open Science discoveries.
The banking system example Probably, simpler systems may already start to be applied, even before reaching high levels of integral encryption of all health data. The banking system may serve as concrete example. Universal online access to home-banking functionalities is currently based on personal identification numbers (PIN) and on one-time passwords (OTP). If this system is sufficient for safely transferring money, something similar should be enough for moving appropriately anonymised health data, even while a fully encrypted solution is still under way. The famous 4p-medicine (personalised, preventative, predictive, and participatory) 59
Ibid. Ibid. 61 L.J. Kish and E.J. Topol, Unpatients, cit., p. 924. 62 Ibid. 60
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futureconsent@lynkeus.eu
Informed consent
Data donation patient like mine
Data Protection
Social media Data inheritance patients like us
Dynamic consent
Who owns data?
Patient stratification
anonymization
MD-PAEDIGREE - Newsletter
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Other Dissemination Activities TYPE OF ACTIVITY (workshop, conference, publication, etc.)
TITLE OF THE EVENT / PUBLICATION
DATE
LOCATION / DISSEMINATION CHANNEL
TYPE OF AUDIENCE
Conference
(JA Hauser, V Muthurangu, A Taylor, JA Steeden, A Jones. Redistribution of organ specific blood flow in response to food ingestion measured by RR-interval averaged golden-angle spiral phase contrast MRI. Journal of Cardiovascular Magnetic Resonance 18 (S1), 1-2)
February 4-7th, 2015
Society for Cardiovascular Magnetic Resonance (SCMR) / EuroCMR Meeting, Nice, France.
Scientific
Meeting
Health promotion in young age. Practical prevention of cardiometabolic abnormalities. Lesson from the Origin study to the MD Paedigree project.
March 20 th-21st 2015
Semmelweis University, 2nd Dept. of Paediatrics, Budapest. Hungary.
Medical students, physicians and in particular pediatricians
Conference (MIE 2015)
Applying machine learning to gait analysis data for disease identification
May 27th-29 th, 2015
Madrid (ES)
Conference
Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study. (R Molléro, D Neumann, MM Rohé, M Datar, H Lombaert, N Ayache, D Comaniciu, O Ecabert, M Chinali, G Rinelli, X Pennec, M Sermesant, and T Mansi.)
June 25th-27th, 2015
Functional Imaging and Modeling of the Heart, LNCS., 8th International Conference, FIMH 2015, Maastricht, The Netherlands
Scientific
Conference abstract
Iain Hannah, Erica Montefiori, Luca Modenese, Marco Viceconti and Claudia Mazzà. Repeatability of operator dependent input in a patient-specific musculoskeletal model of the ankle. Congress of the European Society of Biomechanics (ESB2016), 2016.
July 10 th -13th, 2016
Lyon
Researchers and clinical professionals with interest in Biomechanics.
Conference
Luca Modenese, Daniele Ascani, Claudia Mazzà, Marco Viceconti. A reproducible procedure for creating subject specific models of the lower limb. Congress of the European Society of Biomechanics (ESB2016), 2016.
July 10 th -13th, 2016
Lyon
Researchers and clinical professionals with interest in Biomechanics.
Conference abstract
Joe A. I. Prinold, Claudia Mazzà, Stefan Wesarg, Roberto Di Marco, Pieter van Dijkhuizen, Laura Tanturri De Horatio, Clara Malattia, Marco Viceconti and MDPAEDIGREE Consortium. A patient-specific musculoskeletal modelling pipeline applied to phalangeal loading conditions in gait. 25th Congress of the International Society of Biomechanics, 2015.
July 12th -16 th, 2015
Glasgow
Researchers and clinical professionals with interest in Biomechanics.
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MD-PAEDIGREE - Newsletter
Glasgow
Researchers and clinical professionals with interest in Biomechanics
July 12th -16 th, 2015
Glasgow
Researchers and clinical professionals with interest in Biomechanics.
Conference
Descriptive and Intuitive PopulationBased Cardiac Motion Analysis via Sparsity Constrained Tensor Decomposition. (K Mcleod, M Sermesant, P Beerbaum, and X Pennec)
October 2015
MICCAI 2015 Conference, Munich, Germany
Scientific
Workshop
Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct. (MM Rohé, N Duchateau, M Sermesant, and X Pennec)
October 9 th 2015
STACOM, Conference MICCAI 2015, Munich Germany
Scientific
Workshop
A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?. (JL Bruse, K Mcleod, G Biglino, HN Ntsinjana, C Capelli, TY Hsia, M Sermesant, X Pennec, A Taylor, and S Schievano)
October 9 th 2015
STACOM, Conference MICCAI 2015, Munich Germany
Scientific
Conference
ICT 2015 Innovate, Connect, Transform
October 20 th-22nd, 2015
Lisbon (PT)
Scientific
Conference
J Hauser, A Taylor, A Jones. Obesity is Associated with an Exaggerated Postprandial Pro-atherosclerotic Response to High-energy Food in Children. Circulation 132 (Suppl 3), A19647-A19647
November, 2015
American Heart Association Scientific Sessions, Orlando, Florida
Scientific
Workshop
Challenges of Big Data
November 10 th-11th 2015
Roma (IT)
Scientific
Conference
J Hauser, A Taylor, A Jones. Höherer Blutdruck ist bei Jugendlichen mit einem Anstieg vaskulär-inflammatorischer Marker im Blut nach Aufnahme kalorienreicher Nahrung assoziiert. The Thoracic and Cardiovascular Surgeon. 64 (S 02): OP177.
February, 2016
Deutsche Gesellschaft für Pädiatrische Kardiologie Conference in Leipzig
Scientific
Conference (PDP 2016)
Accelerated Texture Analysis Using Steerable Riesz Wavelets
February 17th-19 th, 2016
Heraklion (GR)
Scientific
Conference Keynote
Claudia Mazzà, Keynote Lecture – Measurement technologies. 25th Congress of the International Society of Biomechanics, 2015
July 12th -16 th, 2015
Conference abstract
Roberto Di Marco, Stefano Rossi, Vitomir Racic, Paolo Cappa and Claudia Mazzà. A comparison between four foot model protocols: the effect of walking on a treadmill. 25th Congress of the International Society of Biomechanics, 2015.
MD-PAEDIGREE - Newsletter
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NEXT INITIATIVES AEPC 2016: Cardioproof’s Final Conference treatment blood flows, providing the cardiologists with key information for
clinical
Furthermore,
decision-making.
CARDIOPROOF
will
provide a trial in order to explore and evaluate the impact of the application Since
1963,
AEPC
(Association
software technologies and human-
of in silico models in the clinical
la
Cardiologie
computer interaction techniques into
decision-making process. During the
been
organising
decision support and treatment planning
trial, through a computerized random-
annual meetings and creating a network
systems in congenital heart diseases.
sample
Européenne
pour
Pédiatrique)
has
function,
interventional
of specialists who are committed to the
cardiologists are randomly allocated
practice and advancement of Congenital
into two separate groups. Each group
Cardiology. On June 2016, the 50th
is then provided with one set of
Annual Meeting of the AEPC will
imaging data, either the limited dataset
cover the topics of Cardiology, Health,
(including
Paediatrics, Paediatric Cardiology as
CARDIOPROOF
well as Congenital Cardiology. Nearly
workshop
1110
and
attendees,
CARDIOPROOF
among the others, will join the event.
parameters
a
currently recommended by clinical
at
introducing
practice guidelines) or the image-
disseminating
an
image-based
based modelling dataset (strengthening
tool,
organise
imaging
aimed
simulation
will
the
which
models
by additional simulation modelling
blood flow profiles in patients with
parameters.). Thus, it is possible to
CARDIOPROOF is an EU funded proof-
coarctation
Using
check whether the two groups come
of-concept project focused on the
individual patient data, this tool models
with different treatment decisions, due
frontiers of VPH and it aims to achieve
pre-treatment
to the information made available by
the development and integration of
the impact that stenting has on post-
of
the
hemodynamics
MD-Paedigree’s Final Conference The final conference of MD-Paedigree will be held in February 2017 in Rome. Partners will provide complete presentations of the technical and clinical achievements within the project, focusing particularly on the validation and clinical usability of project’s outcomes. A large number of associations, external experts, clinicians and EC representatives will be invited, providing the occasion for a public presentation of MD-Paedigree’s results and to showcase demo’s of it’s mature and validated technologies.
48
MD-PAEDIGREE - Newsletter
aorta.
and
the image-based modelling dataset.
Scientific publications Following is a list of scientific publications by Md-Paedigree partners which have been approved for publishing:
Noninvasive hemodynamic assessment, treatment out-
Reviewer acknowledgement 2015. Martini A, Spencer C.
come prediction and follow-up of aortic coarctation
Pediatr Rheumatol Online J. 2016 Feb 1
from MR imaging. Ralovich K, Itu L, Vitanovski D, Sharma P, Ionasec R, Mihalef V, Krawtschuk W, Zheng Y, Everett A, Pon-
Gut Microbiota Dysbiosis as Risk and Premorbid Factors
giglione G, Leonardi B, Ringel R, Navab N, Heimann T, Coman-
of IBD and IBS Along the Childhood-Adulthood Transi-
iciu D. Med Phys. 2015 May
tion. Putignani L1, Del Chierico F, Vernocchi P, Cicala M, Cucchiara S, Dallapiccola B; Dysbiotrack Study Group, Inflamm
Applying machine learning to gait analysis data for dis-
Bowel Dis. 2016 Feb;22
ease identification. Joyseeree R, Abou Sabha R, Mueller H. Stud Health Technol Inform. 2015.
Relations between muscle endurance and subjectively reported fatigue, walking capacity, and participation in
A Patient-Specific Foot Model for the Estimate of Ankle
mildly affected adolescents with cerebral palsy. Eken
Joint Forces in Patients with Juvenile Idiopathic Arthri-
MM, Houdijk H, Doorenbosch CA, Kiezebrink FE, van Benne-
tis. Prinold JA, Mazzà C, Di Marco R, Hannah I, Malattia C,
kom CA, Harlaar J, Dallmeijer AJ. Dev Med Child Neurol. 2016
Magni-Manzoni S, Petrarca M, Ronchetti AB, Tanturri de Ho-
Feb 24.
ratio L, van Dijkhuizen EH, Wesarg S, Viceconti M; MD-PAEDIGREE Consortium. Ann Biomed Eng. 2016 Jan
Energy Balance-Related Behaviors, Perinatal, Sociodemographic, and Parental Risk Factors Associated with
Comprehensive Assessment of the Global and Regional
Obesity in Italian Preschoolers. Shashaj B, Graziani MP,
Vascular Responses to Food Ingestion in Humans Using
Contoli B, Ciuffo C, Cives C, Facciolini S, Rigoni ML, Spaterna
Novel Rapid MRI. JA Hauser, V Muthurangu, JA Steeden, AM
S, Taucci M, Raponi M, Manco M. J Am Coll Nutr. 2016 Mar 2
Taylor, A Jones. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology. Jan 2016 10.1152/
Autoimmune disease-associated gene expression is re-
ajpregu.00454.2015
duced by BET-inhibition. Peeters JG, Vervoort SJ, Mijnheer G, de Roock S, Vastert SJ, Nieuwenhuis EE, van Wijk F, Pr-
Delineating the application of ultrasound in detecting
akken BJ, Mokry M, van Loosdregt Genom Data. 2015 Nov
synovial abnormalities of subtalar joint in juvenile idi-
7;7:14-7. doi: 10.1016/j.gdata.2015.11.004. eCollection 2016
opathic arthritis. Lanni S, Bovis F, Ravelli A, Viola S, Mag-
Mar.
naguagno F, Pistorio A, Magnano GM, Martini A, Malattia C.
Disease activity accounts for long-term efficacy of IL-1
Arthritis Care Res (Hoboken). 2016 Jan 27
blockers in pyogenic sterile arthritis pyoderma gangrenosum and severe acne syndrome. Omenetti A, Carta
Expert consensus on dynamics of laboratory tests for
S, Caorsi R, Finetti M, Marotto D, Lattanzi B, Jorini M, Delfino
diagnosis of macrophage activation syndrome compli-
L, Penco F, Picco P, Buoncompagni A, Martini A, Rubartelli A,
cating systemic juvenile idiopathic arthritis. A.Martini et
Gattorno M. Rheumatology (Oxford). 2016 Mar 17
al. RMD Open. 2016 Jan 19;2(1):e000161. doi: 10.1136/rmdopen-2015-000161. eCollection 2016.
2016 Classification Criteria for Macrophage Activation Syndrome Complicating Systemic Juvenile Idiopathic
Gut Microbiota Dysbiosis as Risk and Premorbid Factors
Arthritis: A European League Against Rheumatism/
of IBD and IBS Along the Childhood-Adulthood Transi-
American College of Rheumatology/Paediatric Rheu-
tion. Putignani L , Del Chierico F, Vernocchi P, Cicala M, Cuc-
matology International Trials Organisation Collaborative
chiara S, Dallapiccola B; Dysbiotrack Study Group, Inflamm
Initiative. A. Martini et al. Ann Rheum Dis. 2016 Mar
1
Bowel Dis. 2016 Feb;22
MD-PAEDIGREE - Newsletter
49
Gut microbiota profiling of pediatric NAFLD and obese
2015, Madrid, Spain, 2015.
patients unveiled by an integrated meta-omics based approach. Del Chierico F, Nobili V, Vernocchi P, Russo A, De
Combination of Polyaffine Transformations and Super-
Stefanis C, Gnani D, Furlanello C, Zandonà A, Paci P, Capuani
vised Learning for the Automatic Diagnosis of LV Infarct,
G, Dallapiccola B, Miccheli A, Alisi A, Putignani L. Hepatology.
Marc-Michel Rohé , Nicolas Duchateau, Maxime Sermesant,
2016 Mar 29
Xavier Pennec. Proc of STACOM 2015 (Statistical Atlases and Computational Models of the Heart. Imaging and Modelling
A Patient-Specific Foot Model for the Estimate of Ankle
Challenges), LNCS Vol 9534, pp. 190-198, January 2016.
Joint Forces in Patients with Juvenile Idiopathic Arthritis. Prinold, J. I., Mazzà, C., Di Marco, R., Hannah, I., Malattia,
A Non-parametric Statistical Shape Model for Assess-
C., Magni-Manzoni, S., Petrarca, M., Ronchetti, A., Tanturri de
ment of the Surgically Repaired Aortic Arch in Coarcta-
Horatio, L., van Dijkhuizen, E. H. P., Wesarg, S. and Viceconti,
tion of the Aorta: How Normal is Abnormal? JL Bruse, K
M., 2016. Annals of biomedical engineering 44, 247-257.
Mcleod, G Biglino, HN Ntsinjana, C Capelli, TY Hsia, M Sermesant, X Pennec, A Taylor, and S Schievano. Proc of STACOM
Accelerated Visual Concept Detection Using Steerable
2015 (Statistical Atlases and Computational Models of the
Riesz Wavelets, Anamaria Vizitiu, Lucian Itu, Ranveer Joy-
Heart. Imaging and Modelling Challenges), LNCS Vol 9534, pp.
seeree, Adrien Depeursinge, Henning Müller and Constantin
21-29, January 2016
Suciu, GPU-, Conference on Parallel, Distributed, and Network-Based Processing, Heraklion, Greece, 2016.
Descriptive and Intuitive Population-Based Cardiac Motion Analysis via Sparsity Constrained Tensor Decompo-
A Demo of Multimodal Medical Retrieval, Ranveer Joy-
sition. K Mcleod, M Sermesant, P Beerbaum, and X Pennec.
seeree, Roger Schaer, Henning Müller, Workshop on Content-
Proc of MICCAI 2015 (Medical Image Computing and Comput-
based Multimedia Indexing (CBMI), Bucharest, Romania, 2016.
er-Assisted Intervention), LNCS Vol. 9351, pp 419-426, 2015.
Locating seed points for multi-organ automatic seg-
Propagation of Myocardial Fibre Architecture Uncertain-
mentation using non-rigid registration and organ anno-
ty on Electromechanical Model Parameter Estimation: A
tations, Ranveer Joyseeree, Henning Müller,, SPIE Medical
Case Study. R Molléro, D Neumann, MM Rohé, M Datar, H
Imaging, 2015.
Lombaert, N Ayache, D Comaniciu, O Ecabert, M Chinali, G Rinelli, X Pennec, M Sermesant, and T Mansi. Proc of FIMH
Identifying the pathology class of patients using only
2015 (Functional Imaging and Modeling of the Heart), LNCS
their gait analysis data with the aid of machine learning,
Vol. 9126, pp 448-456, 2015
Ranveer Joyseeree, Rami Abou Sabha, Henning Müller, MIE
Upcoming Scientific publications Following is a list of scientific publications by Md-Paedigree partners which have recently been approved for publishing:
·· KU Leuven/UZ Leuven and OPBG, “The effect of muscle weakness on gait in children with DMD”, Paper is expected to finished at the end of 2016/beginning of 2017.
·· KU Leuven/UZ Leuven, “Level of co-contraction does not influence net joint torques during gait or during MVIC's in children with CP and DMD”. Paper is expected to finished half-way/at the end of 2016.
·· KU Leuven/UZ Leuven and the University of Washington, “The differences in synergy complexity and structure during gait in CP and DMD children and its relationship with weakness”. Data collection is ongoing. First results in CP children expected half-way 2016. Paper expected to be finalized at the end of 2016.
·· KU Leuven/UZ Leuven, “The effect of changes in muscle morpholgy on the outcomes of a MVIC and gait kinetics in children with CP and DMD.”Data collection is ongoing. First results in CP children expected half-way 2016. Paper expected to be finalized by the end of 2016.
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International Conferences & Events A list of international meetings to which MD-Paedigree partners are willing to attend:
EVENT
WHEN
WHERE
ISMRM: 24th Annual Meeting of International Society for Magnetic Resonance in Medicine
7th-13th May 2016
Singapore
7th Edition of Health 2.0 Europe
10th-12th May, 2016
Barcelona, Spain
AEPC 2016 : 50th Annual Meeting of the Association for European Paediatric and Congenital Cardiology
1st-4th June, 2016
Rome, Italy
International Joint Conference on Cerebral Palsy and other Childhood-onset Disabilities: EACD 2016, ICPC, IAACD
1st -4th June, 2016
Stockholm, Sweden
ICIMTH 2016 – International Conference on Informatics, Management and Technology in Healthcare
1-3 July, 2016
Athens, Greece
EDF 2016: European Data Forum
29th -30th June, 2016
HEC/MIE 2016 : Medical Informatics Europe (MIE) & Health exploring complexity (HEC)
28thAugust-2ndSeptember, 2016
Eindhoven, the Netherlands
Munich, Germany
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ECAI 2016 : Biannual European Conference on Artificial Intelligence
29thAugust - 2ndSeptember, 2016
The Hague, the Netherlands
EG VCBM 2016 : the 6th Eurographics Workshop on Visual Computing for Biology and Medicine
7th-9th September, 2016
Bergen, Norway
ESMAC: 25th Annual Meeting of the European Society for Movement Analysis in Adults and Children
26th September - 1st October, 2016
Sevilla, Andalucia, Spain
EHFG 2016 : 19th European Health Forum Gastein
28th - 30th September, 2016
Gastein, Austria
MICCAI 2016 : Conference of Medical Image Computing and Computer Assisted Intervention
17th-21st October, 2016
Istanbul, Turkey
European Congress on Ecardiology & Ehealth
26th-28th October 2016
Berlin, Germany
HIMSS Europe World of Health IT (WoHIT) Conference & Exhibition
21st –22nd November, 2016
Barcelona, Spain
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NEWSLETTER INFO Editorial Board + Almerico Bartoli - Lynkeus + Mirko De Maldè - Lynkeus + Davide Zaccagnini - Lynkeus
Guest Authors Orfeas Aidonopoulos - Athena Research Centre Bruno Dallapiccola - Ospedale Pediatrico Bambino Gesù Klaus Dreschler - Fraunhofer IGD Lucian Mihai Itu – Transilvania University Ilena Limura – BMR Genomics Cosmin Nita - Transilvania University Olivier Pauly – Siemens Healthcare Lorenza Putignani – Ospedale Pediatrico Bambin Gesù Barbara Simoniati – BMR Genomics Frans Steenbrink – MotekForce Link Costantin Suciu - Transilvania University Rainer Thiel – Empirica Mihalef Viorel – Siemens Healthcare Anamaria Vizitiu - Transilvania University
Subscription The MD-Paedigree newsletter is published twice a year by the MD-Paedigree consortium and is distributed free of charge. All issues of the newsletter will be available on our website. For subscription to the newsletter please go to md-paedigree.eu
Disclaimer The MD-Paedigree newsletter is funded by the European Commission under the Seventh Framework Programme. The content of this newsletter cannot be considered as the European Commission’s official position and neither the European Commission nor any person acting on behalf of the European Commission is responsible for the use which might be made of it; its content os the sole responsibility of the MD-Paedigree project partners. Although the MD-Paedigree consortium endeavours to deliver high-quality, no guarantee can be given regarding the correctness and completeness of the content of this newsletter due to its general information character.
Contacts: Email: info@md-paedgree.eu Twitter: @mdpaedigree Web: www.md-padigree.eu MD-PAEDIGREE - Newsletter
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MD-PAEDIGREE PARTNERS MD-Paedigree is an international collaboration between 22 partners across Europe from industry, academia and healthcare.
Project Coordinator:
Belgium
France
Germany
Italy
Netherlands
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Greece
Romania
USA
Switzerland
UK
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