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Dekker trog - big data for radiation oncology - 2017
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
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
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)
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.
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%)
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?
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