TROG plenary

1. Knowledge Engineering in Oncology
Andre Dekker
Department of Radiation Oncology (MAASTRO)
GROW - Maastricht University Medical Centre +
Maastricht,The Netherlands
SLIDES AVAILABLE ON SLIDESHARE
(slideshare.net/AndreDekker)

2. 2
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
BestTreatment)
• 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

3. 3
Seminar structure
Big Data in Radiation Oncology
• Part 1: Rationale
• Part 2: Data
• Part 3: Modelling
• Part 4: Change Practice

4. Knowledge Engineering in Oncology
Part 1: Rationale

5. 5
Can we predict a tulip’s color by looking at the bulb?
http://www.amystewart.com

6. 6
Predicting the color of a tulip - AUC
1.00
AUC
0.72
0.50

7. 7
Predicting the survival of NSCLC patients
AUC
1.00
AUC
0.50
AUC
0.72

8. 8
Prediction by MDs?
NSCLC
2 year survival
30 patients
8 MDs
Retrospective
AUC: 0.57
NSCLC
2 year survival
158 patients
5 MDs
Prospective
AUC: 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

9. 9
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 day
Half-life of knowledge estimated at 7 years (in young students)
J Clin Oncol 2010;28:4268
JMI 2012 Friedman, Rigby
BMJ Clinical Evidence
We cannot predict
outcomes of individual treatments

10. 10
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’sCancerLinQ

11. Knowledge Engineering in Oncology
Part 2: Data

12. 12
Cancer Data?

13. 13
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)

14. 14
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

15. 15
CORAL: Community in Oncology for RApid Learning
7
4
meerCAT
Lung - Dyspnea
U Michigan
MAASTRO
The Christie
Map © Copyright Showeet.com
canCAT
Lung SBRT - Control
Princess Margaret
MAASTRO
BIONIC
Radiomics
MAASTRO
Tata Memorial
duCAT
Lung - Dysphagia
MAASTRO
Radboud
NKI
euroCAT
Lung - Survival
UK Aachen
LOC Hasselt
Catharina
MAASTRO
CHU Liege
Interest to join
Erasmus (Breast)
BCCA (Breast)
Bloemfontein (Cervix)
Odense (HN, Lung)
Aalst (Lung)
McGill (Brain)
ozCAT
Head&Neck - Survival
Liverpool
Illawarra
Newcastle
Westmead
MAASTRO
RTOG/NRG
worldCAT
Rectum - Local Control
Fudan
Rome/EU
RTOG/NRG

16. Knowledge Engineering in Oncology
Part 3: Modelling

17. 17
Modelling
Lambin et al doi:10.1038/nrclinonc.2012.196

18. 18
TRIPOD
https://www.tripod-statement.org/TRIPOD

19. Knowledge Engineering in Oncology
Part 4: Changing practice

20. 20
Lung cancer -> DESERT trial

21. 21
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

22. 22
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

23. 23
There is an app for that

24. 24
Next iteration -> Personal HealthTrain: Get citizens in control
• https://www.youtube.com/watch?v=mktAtHmy-FM

25. 25
Acknowledgements
• Fudan Cancer Center, Shanghai,China
• Varian, PaloAlto, CA, USA
• Siemens, Malvern, PA, USA
• RTOG, Philadelphia, PA, USA
• MAASTRO, Maastricht, Netherlands
• PoliclinicoGemelli, Roma, Italy
• UH Ghent, Belgium
• UZ Leuven, Belgium
• Radboud, Nijmegen, Netherlands
• University of Sydney, Australia
• University of Michigan,Ann Arbor, USA
• Liverpool and MacarthurCC, Australia
• CHU Liege, Belgium
• UniklinikumAachen, Germany
• LOC Genk/Hasselt, Belgium
• Princess Margaret CC, Canada
• The Christie, Manchester, UK
• UH Leuven, Belgium
• State Hospital, Rovigo, Italy
• Illawarra ShoalhavenCC, Australia
• CatharinaZkh Eindhoven, Netherlands
• Philips, Eindhoven, Netherlands
More info on: www.predictcancer.org www.cancerdata.org
www.eurocat.info www.mistir.info

26. Thank you for your attention
Andre Dekker
Department of Radiation Oncology (MAASTRO)
GROW - Maastricht University Medical Centre +
Maastricht,The Netherlands