TROG FRO workshop

1. Big Data in Radiation 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
TROG 2017 talks
• Learning outcome prediction models from cancer data
– Technical ResearchWorkshop, Monday 840-910, followed by
Panel Discussion
• Big Data in Radiation Oncology
– Statistical Methods, Evidence Appraisal and Research for
Trainees, Monday 1450-1520
• Knowledge Engineering in Oncology
– TROG Plenary,Tuesday, 925-1000
• Radiomics for Oncology
– TROG Plenary,Thursday, 1150-1220
Some
Overlap
No
Overlap

4. 4
Learning objectives
After the lecture, attendees should be able to
• Name the major sources of cancer data and their absolute and relative size
• Itemize steps in the methodology to go from data to models
• Appraise papers that describe models incl. usingTRIPOD
• Grasp challenges and opportunities to use models to improve care

5. The Data Part

6. 6
Cancer Data?

7. 7
Data landscape
• Clinical research
• 3% of patients
• 100% of features
• 5% missing
• 285 data points
• Clinical registries
• 100% of patients
• 3% of features
• 20% missing
• 240 data points
• Clinical routine
• 100% of patients
• 100% of features
• 80% missing
• 2000 data points
Data elements
Patients

8. 8
Our approach
• euroCAT: https://www.youtube.com/watch?v=ZDJFOxpwqEA

9. The Modelling Part

10. 10
Our modelling approach
• Hypothesis driven!!

11. 11
How much data do you need?
• Rule of thumb. Min. 10 events per input feature
• 200 NSCLC patients
• 25% survival at two years
• 50 events
• 10 input features
• Simpler models are better Source: vitalflux.com (2017)

12. 12
TRIPOD
https://www.tripod-statement.org/

13. 13
Dehing-Oberije (MAASTRO), IJROBP 2009;74:355
Learn a model from data
• Training cohort
– 322 patients (MAASTRO)
• Clinical variables
• SupportVector Machines
• Nomogram
Cary Oberije et al.

14. 14
Estimate model performance
• Validation cohort
• 36 patients (Leuven)
• 65 patients (Ghent)
• Discrimination,
Calibration
• AUC 0.75
Dehing-Oberije (MAASTRO), IJROBP 2009;74:355
Cary Oberije et al.

15. 15
Decision Support
Stage IIIA 10 (14%)
Stage IIIB 13 (19%)
T4 12 (17%)
Cary Oberije et al.

16. Better Care?

17. 17
There is an app for that

18. 18
Validation Results (AUC 0.69)
DSS works, but only to discriminate between good and medium/poor
Better than TNM stage
0 1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
Survival
Years from the start of radiotherapy
69%
27%
30% p<0.001
Good prognosis (n=67, 30%)
Medium prognosis (n=132, 59%)
Poor prognosis (n=26, 12%)

19. 19
Guideline vs. the real world in lung cancer

20. 20
What did Liverpool learn?
routine data, realistic quality, good evidence?
0 1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
Survival
Years from the start of radiotherapy
69%
27%
30% p<0.001
Good prognosis (n=67, 30%)
Medium prognosis (n=132, 59%)
Poor prognosis (n=26, 12%)
0 1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
Survival
Years from the start of radiotherapy
18%
16%
16%
Good prognosis (n=41, 17%)
Medium prognosis (n=112, 47%)
Poor prognosis (n=84, 35%)
• Rethink palliative treatments in good prognosis patients
• Rethink curative treatments in poor prognosis patients
radicalRTtreatments
palliativeRTtreatments
Rapid learning: Expected survival
gain with curative dose from 18 to
~60% in good prognosis patients
Rapid learning: No survival gain
with curative dose in poor
prognosis patients
What did MAASTRO learn?

21. 21
There is an app for that

22. 22
Challenges
• Trust in models vs. own expertise
• Continuous changing models,
commissioning
• Evidence level and methodology
(equipoise, randomisation, contamination)
• Endpoint
– Survival,Toxicity, QoL,Cost Effectiveness
– Patient satisfaction
• Bad news, over-optimism
• There is nothing new, lot of “real trial”
competition
• Timing: Multidisciplinary team vs. shared
decisions
• Time pressure, limit on patient cognition
• Radiation oncology in 3rd line, e.g. a change
from concurrent to sequential in NSCLC
• Deviations from guidelines, bad quality
indicators

23. 23
Learning objectives
After the lecture, attendees should be able to
• Name the major sources of cancer data and their absolute and relative size
• Itemize steps in the methodology to go from data to models
• Appraise papers that describe models incl. usingTRIPOD
• Grasp challenges and opportunities to use models to improve care

24. 24
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

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