Presentation at European Cancer Conference 2015 in Vienna

1. Andre Dekker, PhD
Medical Physicist
MAASTRO Clinic
Big databases and outcome research:
Opportunities and challenges for radiation oncology

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© MAASTRO 2015
Disclosures
Research collaborations incl. funding / honoraria etc.
– Varian (VATE, chinaCAT, euroCAT), Siemens (euroCAT), Sohard (SeDI,
CloudAtlas), Mirada Medical (CloudAtlas), Philips (EURECA, TraIT, SWIFT-RT),
Xerox (EURECA), De Praktijkindex (DLRA)
Public research funding
– Radiomics (USA-NIH/U01CA143062), euroCAT(EU-Interreg), duCAT (NL-
STW), EURECA (EU-FP7), SeDI & CloudAtlas (EU-EUREKA), TraIT (NL-
CTMM), DLRA (NL-NVRO)
Spin-offs and commercial ventures
– MAASTRO Innovations B.V. (CSO)
– Various patents on medical machine learning

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© MAASTRO 2015
Big data in Oncology
Source: Cancer Research UK
Source: Institute for Health Technology Transformation

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© MAASTRO 2015
The doctor is drowning
• Explosion of data
• Explosion of decisions
• Explosion of ‘evidence’*
• 3 % in trials, bias
• Sharp knife
*2010: 1574 & 1354 articles on lung cancer & radiotherapy = 7.5 per day
Half-life of knowledge estimated at 7 years (in young students)
Source: J Clin Oncol 2010;28:4268
Source: JMI 2012 Friedman, Rigby

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© MAASTRO 2015
Main Opportunity of Big Data Driven Medicine : Rapid Learning
Health Care / Precision Medicine / Predict outcome in an individual
In [..] rapid-learning [..] data routinely
generated through patient care and
clinical research feed into an ever-
growing [..] set of coordinated
databases.
J Clin Oncol 2010;28:4268
[..] rapid learning [..] where we can
learn from each patient to guide
practice, is [..] crucial to guide rational
health policy and to contain costs [..].
Lancet Oncol 2011;12:933
Examples:
Radiotherapy CAT (www.eurocat.info)
ASCO’s CancerLinQ

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© MAASTRO 2015
Why would we want to predict outcome in an individual patient?
If you can’t predict outcomes
Doctor/Patient perspective
• you can’t inform and involve your patient properly
• you might not make the right decision of treatment
A over treatment B
Quality perspective
• you can’t know if your treatments are given the
predicted outcome
Innovation perspective
• you can’t determine which patient (group) we need
to innovate in
Source: www.predictcancer.org (MAASTRO)
Source: www.lifemath.net (MGH)

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© MAASTRO 2015
Main challenge of using Big Data and Outcomes
Research in Oncology
• You need to learn from other
patients to predict the outcome of
a new patient
• These data are spread out over
100k hospitals
• So we need to share…,
challenges:
• Administrative (I don’t have the
time)
• Political (I don’t want to )
• Ethical (I am not allowed)
• Technical (I can’t) [..] the problem is not really technical […]. Rather, the problems are
ethical, political, and administrative.
Lancet Oncol 2011;12:933

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© MAASTRO 2015
Proposed solutions to sharing Big Data in Cancer
Examples Wide
(#patients)
Deep
(#features)
High
Quality
Unbiased Diverse Avail
Tech
Single
institute /
hospital
network
Partners
PMH
VA
-- ++ + -- -- ++
Open data Guidelines
Publications
Public datasets
Watson
cancerdata.org
- - ++ - + +
Centralized Google / Flatiron
Registries/SEER
CancerLinQ
Cancer Commons
Sage Bionetworks
+ - + + + -
Distributed euroCAT ++ + -- ++ ++ --

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© MAASTRO 2015
euroCAT, duCAT, chinaCAT, ozCAT, VATE, ukCAT, dkCAT, worldCAT, BIONIC Network
Industry Partners
Active or funded CAT partners (19)
Prospective centers
2
5
Map from cgadvertising.com
5
Clinical / Academic
Partners

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© MAASTRO 2015
Specific challenges for Radiation Oncology
Clinical
Genomic (RGC)
Socio-economic
Imaging (diag, follow-up, IGRT)
Treatment (TPS, DGRT, R&V)
MAASTRO: 33 fraction IGRT/DGRT Lung Cancer patient: 14GB
(Exome sequencing: 6GB)

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© MAASTRO 2015
Radiomics (www.radiomics.org)
Source: Nature Communic. 5:4006 (2014)

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© MAASTRO 2015
Ontologies – Speaking the same language
• Radiation Oncology Ontology
• AAPM TG 263 Standardizing Nomenclature for Radiation Therapy
– Structure names across imaging and treatment planning system platforms.
– Ontologies for structures identified in nomenclature to minimize variations in
how structures are segmented.
– Nomenclature for elements of the dose volume histogram curve and related
data.
– Developing templates for clinical trial groups and users of specific software
platforms.

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© MAASTRO 2015
Summary
Big databases and outcome research:
Opportunities and challenges for radiation oncology
• Rapid Learning Health Care / Precision Medicine
Predict outcomes better
• Main challenge is access to enough data
Distributed across the globe and across data holders
• Solutions to getting access to data
Now centralized, future distributed learning
• Specific challenge for Radiation Oncology
Nomenclature/Ontology & Volume of imaging data

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© MAASTRO 2015
Acknowledgements
• Varian, Palo Alto, CA, USA
• Siemens, Malvern, PA, USA
• RTOG, Philadelphia, PA, USA
• MAASTRO, Maastricht, Netherlands
• Policlinico Gemelli, Roma, Italy
• UH Ghent, Belgium
• Catherina Zkh Eindhoven, Netherlands
• UZ Leuven, Belgium
• Radboud, Nijmegen, Netherlands
• University of Sydney, Australia
• Liverpool and Macarthur CC, Australia
• CHU Liege, Belgium
• Uniklinikum Aachen, Germany
• LOC Genk/Hasselt, Belgium
• Princess Margaret Hospital, Canada
• The Christie, Manchester, UK
• UH Leuven, Belgium
• State Hospital, Rovigo, Italy
• Illawarra Shoalhaven CC, Australia
• Fudan Cancer Center, Shanghai, China
More info on: www.predictcancer.org www.cancerdata.org
www.eurocat.info www.mistir.info

15. Andre Dekker, PhD
Medical Physicist
MAASTRO Clinic
Thank you for your attention
More info on:
www.eurocat.info
www.predictcancer.org
www.cancerdata.org
www.mistir.info
www.maastro.nl