Andre Dekker, PhDMedical PhysicistMAASTRO Clinic

MaastrichtThe Netherlands

Informatics and Clinical Decision Support in Precision Medicine

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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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Review the feasibility of engaging stakeholders on an international scale

from academia, government, and industry

to develop an open source interoperable informatics research infrastructure

for the evaluation of clinical support systems and the technical requirements to meet this goal.

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Data > Decision Support

1. Modeling“Learn a model from data”

2. Validation“Estimate model performance”

3. Decision Support“Impact of the model on clinical practice”

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Dehing-Oberije (MAASTRO), IJROBP 2009;74:355

A model learnt from data – Early Example

Training cohort– 322 patients (MAASTRO)

Clinical variablesSupport Vector MachinesNomogram

Validated in external hospitals from Belgium

AUC 0.75 for 2 year survival

Cary Oberije et al.

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Radiomics (www.radiomics.org)

Source: Nature Communic. 5:4006 (2014)

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Clinical Decision Support / 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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Scaling up to all cancer patients

Oncology2005-2015140M patients100k hospitals1-10GB per patient140-1400PB80% unstructured

Source: Cancer Research UK

Source: Institute for Health Technology Transformation

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Informatics Need

• 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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The ‘standard’ approach

• Sharing standardized, highly curated data from clinical research programs

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TraIT – Translational Research IT Platform

Oct 2015: 1750 users – 250 centers – 215 studies – 32 partnersUniversity Medical Centers, several other public institutions, charities, and companies2011-now: ~15-20M€

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The ‘standard’ approach

• Sharing standardized, highly curated data from clinical research programs

• Very useful, but only 3% of patients (if that)• Worries about privacy, loss of control, limited amount of

features, limited reusability, a lot of work

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A different approach

If sharing is the problem: Don’t share the data

If you can’t bring the data to the learning applicationYou have to bring the learning application to the data

Consequences• The learning application has to be distributed • The data has to be readable by an application (i.e. not a human)

• Solution: Sharing standardized highly curated research data• Solution: Not-sharing non-standardized non-curated clinical data

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Source: Varian Medical Systems

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In-hospital infra & de-identification

Deidentification:• Removal of obvious patient identifiers (name, MRN, social security number, email etc.)• Assign a persistent token pseudonym• Change (data banding) of obvious but required patient identifiers (everyone born and died on the

15th of the month, part of the postal code)• No individual patient data leaves the hospital

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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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Summary

Review the feasibility of engaging stakeholders on an international scale from academia, government and industry It is very feasible to do this on an international scaleto develop an open source interoperable informatics research infrastructure We choose both a centralized approach AND a Semantic Web / Linked Data approach with 80% open sourcefor the evaluation of clinical support systems For us these are systems which predict outcomes using clinical dataand the technical requirements to meet this goal.Future: Don’t share the data, keep them where they are now

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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

MaastrichtThe Netherlands

Thank you for your attention

More info on: www.eurocat.info

www.predictcancer.orgwww.cancerdata.org

www.mistir.infowww.maastro.nl