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Andre Dekker, PhDMedical PhysicistMAASTRO 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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Big data in Oncology
Oncology2005-2015140M patients100k hospitals1-10GB per patient140-1400PB80% unstructured
Source: Cancer Research UK
Source: Institute for Health Technology Transformation
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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 dayHalf-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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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
Source: J Clin Oncol 2010;28:4268
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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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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)
Oncology2005-2015140M patients100k hospitals1-10GB per patient140-1400PB80% unstructured
[..] the problem is not really technical […]. Rather, the problems are ethical, political, and administrative. Lancet Oncol 2011;12:933
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Proposed solutions to sharing Big Data in CancerExamples Wide
(#patients)Deep
(#features)High
QualityUnbiased Diverse Avail
TechSingle institute / hospital network
PartnersPMHVA
-- ++ + -- -- ++
Open data GuidelinesPublicationsPublic datasetsWatsoncancerdata.org
- - ++ - + +
Centralized Google / FlatironRegistries/SEERCancerLinQCancer CommonsSage Bionetworks
+ - + + + -
Distributed euroCAT ++ + -- ++ ++ --
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euroCAT, duCAT, chinaCAT, ozCAT, VATE, ukCAT, dkCAT, worldCAT, BIONIC Network
Industry Partners
Active or funded CAT partners (19)
Prospective centers
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Map from cgadvertising.com
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Clinical / Academic Partners
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Specific challenges for Radiation Oncology
ClinicalGenomic (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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Radiomics (www.radiomics.org)
Source: Nature Communic. 5:4006 (2014)
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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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Summary
Big databases and outcome research: Opportunities and challenges for radiation oncology
• Rapid Learning Health Care / Precision MedicinePredict outcomes better
• Main challenge is access to enough data Distributed across the globe and across data holders
• Solutions to getting access to dataNow centralized, future distributed learning
• Specific challenge for Radiation OncologyNomenclature/Ontology & Volume of imaging data
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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.orgwww.eurocat.info www.mistir.info
Andre Dekker, PhDMedical PhysicistMAASTRO Clinic
Thank you for your attention
More info on: www.eurocat.info
www.predictcancer.orgwww.cancerdata.org
www.mistir.infowww.maastro.nl