Published on September 27, 2015
1. Andre Dekker, PhD Medical Physicist MAASTRO Clinic Big databases and outcome research: Opportunities and challenges for radiation oncology
2. 2 © 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
3. 3 © MAASTRO 2015 Big data in Oncology Source: Cancer Research UK Source: Institute for Health Technology Transformation
4. 4 © 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
5. 5 © 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
6. 6 © 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)
7. 7 © 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
8. 8 © 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 ++ + -- ++ ++ --
9. 9 © 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
10. 10 © 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)
11. 11 © MAASTRO 2015 Radiomics (www.radiomics.org) Source: Nature Communic. 5:4006 (2014)
12. 12 © 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.
13. 13 © 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
14. 14 © 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
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