Andre Dekker, PhDMedical PhysicistMAASTRO Clinic

Big databases and outcome research: Opportunities and challenges for radiation oncology

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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

2

5

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