Knowledge Engineering in Oncology
Andre DekkerDepartment of Radiation Oncology (MAASTRO)GROW - Maastricht University Medical Centre +Maastricht, The Netherlands
SLIDES AVAILABLE ON SLIDESHARE (slideshare.net/AndreDekker)
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Disclosures• Research collaborations incl. funding and speaker honoraria
– Varian (VATE, SAGE, ROO, chinaCAT, euroCAT), Siemens (euroCAT), Sohard (SeDI, CloudAtlas), Mirada Medical (CloudAtlas), Philips (EURECA, TraIT, SWIFT-RT, BIONIC), Xerox (EURECA), De Praktijkindex (DLRA), ptTheragnostic (DART, Strategy), CZ (My Best Treatment)
• Public research funding– Radiomics (USA-NIH/U01CA143062), euroCAT(EU-Interreg), duCAT&Strategy
(NL-STW), EURECA (EU-FP7), SeDI & CloudAtlas & DART (EU-EUROSTARS), TraIT (NL-CTMM), DLRA (NL-NVRO), BIONIC (NWO)
• Spin-offs and commercial ventures– MAASTRO Innovations B.V. (CSO)– Various patents on medical machine learning
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Seminar structureBig Data in Radiation Oncology
• Part 1: Rationale • Part 2: Data• Part 3: Modelling• Part 4: Change Practice
Knowledge Engineering in OncologyPart 1: Rationale
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Can we predict a tulip’s color by looking at the bulb?
http://www.amystewart.com
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Predicting the color of a tulip - AUC
1.00AUC
0.72
0.50
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Predicting the survival of NSCLC patients
AUC1.00
AUC0.50
AUC0.72
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Prediction by MDs?
NSCLC2 year survival30 patients8 MDsRetrospectiveAUC: 0.57
NSCLC2 year survival158 patients5 MDsProspectiveAUC: 0.56
Oberije et al. Kruger et al. 1999
Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence leads to inflated self-assessments. J Pers Soc Psych
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The problem of Big Data – 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) J Clin Oncol 2010;28:4268
JMI 2012 Friedman, RigbyBMJ Clinical Evidence
We cannot predict outcomes of individual treatments
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The potential of Big Data - Rapid Learning Health Care
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: DLRA, NROR, CAT (www.eurocat.info) ASCO’s CancerLinQ
Knowledge Engineering in OncologyPart 2: Data
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Cancer Data?
Oncology2005-2015140M patients0.1-10GB per patient14-1400PB80% unstructured100k hospitals
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Barriers to sharing data[..] the problem is not really technical […]. Rather, the problems are ethical, political, and administrative. Lancet Oncol 2011;12:933
1. Administrative (I don’t have the resources)2. Political (I don’t want to)3. Ethical (I am not allowed to)
4. Technical (I can’t)
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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 research• You have to bring the research to the data
• Challenges– The research application has to be distributed (trains & track)– The data has to be understandable by an application (i.e. not a human) ->
FAIR data stations
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CORAL: Community in Oncology for RApid Learning
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meerCATLung - DyspneaU MichiganMAASTROThe Christie
Map © Copyright Showeet.com
canCATLung SBRT - ControlPrincess MargaretMAASTRO
BIONICRadiomicsMAASTROTata Memorial
duCATLung - DysphagiaMAASTRORadboudNKI
euroCATLung - SurvivalUK AachenLOC HasseltCatharinaMAASTROCHU Liege
Interest to joinErasmus (Breast)BCCA (Breast)Bloemfontein (Cervix)Odense (HN, Lung)Aalst (Lung)McGill (Brain)
ozCATHead&Neck - Survival LiverpoolIllawarra NewcastleWestmeadMAASTRORTOG/NRG
worldCATRectum - Local ControlFudanRome/EURTOG/NRG
Knowledge Engineering in OncologyPart 3: Modelling
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Modelling
Lambin et al doi:10.1038/nrclinonc.2012.196
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TRIPOD
https://www.tripod-statement.org/TRIPOD
Knowledge Engineering in OncologyPart 4: Changing practice
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Lung cancer -> DESERT trial
PalliativeRT/Chemo
Radical RT
SequentialChemo-RT
ConcurrentChemo-RT
EscalatedChemo-RT
100%
50%50%
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Model based approach• Proton therapy introduction in the Netherlands• Expensive and only 1800 slots• ALARA -> protons for reduced toxicity• RCT -> protons for better survival/control• Evidence-based (e.g. paediatric) and model-based
indications (HN, GBM, Lung, Breast, Prostate)
Widder et al. http://dx.doi.org/10.1016/j.ijrobp.2015.10.004
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Model based approach
Widder et al. http://dx.doi.org/10.1016/j.ijrobp.2015.10.004
Cheng et al. http://dx.doi.org/10.1016/j.radonc.2015.12.029
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There is an app for that
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Next iteration -> Personal Health Train: Get citizens in control• https://www.youtube.com/watch?v=mktAtHmy-FM
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Acknowledgements• Fudan Cancer Center, Shanghai,
China• Varian, Palo Alto, CA, USA• Siemens, Malvern, PA, USA• RTOG, Philadelphia, PA, USA• MAASTRO, Maastricht, Netherlands• Policlinico Gemelli, Roma, Italy• UH Ghent, Belgium• UZ Leuven, Belgium• Radboud, Nijmegen, Netherlands• University of Sydney, Australia• University of Michigan, Ann Arbor,
USA
• Liverpool and Macarthur CC, Australia
• CHU Liege, Belgium• Uniklinikum Aachen, Germany• LOC Genk/Hasselt, Belgium• Princess Margaret CC, Canada• The Christie, Manchester, UK• UH Leuven, Belgium• State Hospital, Rovigo, Italy• Illawarra Shoalhaven CC, Australia • Catharina Zkh Eindhoven,
Netherlands• Philips, Eindhoven, NetherlandsMore info on: www.predictcancer.org www.cancerdata.org
www.eurocat.info www.mistir.info
Thank you for your attention
Andre DekkerDepartment of Radiation Oncology (MAASTRO)GROW - Maastricht University Medical Centre +Maastricht, The Netherlands