Graduate course precision radiation medicine

1. Radiomics for 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), OncoRadiomics
• 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
Lecture
Learning objectives, after this lecture you should be able to
• Formulate what the rationale of Radiomics is and how it might contribute to
personalized medicine
• Name the major workflow steps to use Radiomics to get from image data to
decision support
• Appraise papers that describe Radiomics research incl. how the authors handle
the many Radiomics challenges
• Name a few future directions for Radiomics
Part 1: Rationale (Predictions, Big Data, Radiomics) – 15 mins
Part 2: Radiomics workflow & challenges – 25 mins
Part 3: New directions in Radiomics – 15 mins

4. 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. 11
Cancer Data?

12. 12
Images are not picture, they are data
Gillies et al., Radiology 2016;278(2).Larue, et al., Br J Radiol 2017

13. 13
Nature selects for phenotype
Lambin et al., Eur J Cancer. 2012 Mar;48(4):441-6

14. 14
Radiomics vs Radiongenomics
• Radiomics
– High throughput quantitative analysis of standard of care imaging to characterize tumours and
normal tissues to improve cancer diagnosis, prognosis, prediction and response to therapy.
• Radiogenomics
– The link between Radiomics and Genomics (i.e. how the imaging phenotype and genotype are
related)
– The interaction between Radiotherapy and Genomics (genetic risk factors for radiation
toxicities?)

15. 15
Animation
• https://www.youtube.com/watch?v=Vf0F7q8vaS4

16. Part 2 - Radiomics workflow & challenges

17. 17
RadiomicsWorkflow
Lambin,Walsh et al., Nat Rev Clin Oncol (in-press)
Larue, et al., Br J Radiol 2017

18. 18
Guide

19. 19
Feature Extraction – Imaging Protocols
Oliver et al. ,TranslationalOncology (2015) 8, 524–534

20. 20
Guide

21. 21
Feature Extraction – Robust Segmentation
Parmar et al., PLoS One. 2014; 9(7): e102107.

22. 22
Feature Extraction – Robust Segmentation
Parmar et al., PLoS One. 2014; 9(7): e102107.
Approaches
1. Perform semi-automatic segmentation
2. Remove features which are too sensitive to the exact segmentation
Larue, et al., Br J Radiol 2017

23. 23
Key points until now
• They key to Radiomics is not to be perfect but to be consistent and
adhere to (other people’s) standards
• Radiomics on the state of the art imaging does not makes sense, focus
on clinical standard of care
• Radiomics until now works (much) better in Radiotherapy than in
Radiology

24. 24
Guide

25. 25
Feature Extraction - Software
Non-texture-based features:
Histogram, Geometry
Texture-based features: GLCM,
GLRLM
Sample capacity:
31
51
33
Correlation Coefficients Distribution
correlation coefficient range
Fudan University Cancer Hospital (unpublished)

26. 26
Feature Extraction – Phantom / Ontology

27. 27
Test-retest feature stability
• Rectal cancer clinical test-retest data from Fudan (Shanghai)
– n=40, different scanners, tube currents, recon parameters
– Time between scans 5-19 days (median 8)
• Lung cancer coffee-break test-retest from NCI (RIDER)
– N=35, same scanner, same recon
– Time between scans 10 minutes
• Hypotheses
– Similar features are reproducible in the clinical scenario as in the “coffee-
break” scenario
– Features found to be robust in one tumor site are also robust in another tumor
site.
• Compare ICC between Lung (RIDER, coffee-break) & Rectum (Fudan,
clinical)

28. 28
Rectum clinical vs. Lung coffee-break

29. 29
Guide

30. 30
Combining with clinical
Aerts, JAMA Oncol 2016

31. 31
Dimensionality reduction - Archetypes
Gillies et al., Radiology 2016;278(2).
219 features in 235 patients
Aerts et al., NatureCommunications 5, 4006

32. 32
Guide

33. 33
Our modelling approach

34. 34
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
• Less features is generally better Source: vitalflux.com (2017)

35. 35
Source: Jason Brownlee (2013)
Machine Learning

36. 36
Considerations for machine learning
• Discrimination (AUC)
• Calibration (Brier)
• Interpretability (black box vs. transparent)
• Can it handle low data quality (of training and validation)?

37. 37
Choose already
Simple and quick, but need complete data
• Logistic regression
• SupportVector Machines
Intuitive and can handle missing data
• Bayesian Networks
Review pending

38. 38
TRIPOD
https://www.tripod-statement.org/

39. 39
So, Radiomics needs a lot of training data….
Aerts et al., NatureCommunications 5, 4006

40. 40
…. and a lot of validation data
Aerts et al., NatureCommunications 5, 4006
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis

41. 41
Radiomics – End result

42. Part 3: New directions

43. 43
Radiomics – Preclinical

44. 44
Radiomics – PET

45. 45
Radiomics - MRI
• Rectal cancer - Chemoradiation
• Pathological response
• Training n=173,Validation n=25
• AUC 0.79 (validation)
1) MRGTVdelineation
2) GTVROI extraction
3) LoGfilter application
accordingdifferents
0.3 0.5 1.0 2.0 3.0 4.0
4) Dataanalysis
|||cT
234
Points
| | |cN
||||||||||||||SKE0485
−0.6−0.4−0.200.20.40.6
| | | | | | | | | |ENT0344
1.6 1.8 2 2.2 2.4
| | | | | | | || | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |Total Points
320 330 340 350 360 370 380 390
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
TRG1
Probability
0 1 2
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

46. 46
Radiomics outside of oncology

47. 47
Radiomics – Delta Radiomics

48. 48
CBCT and CT interchangeable?
• 132 patients with stage I-IV non-small cell lung cancer (NSCLC) treated
with curative intent
• Total of 543 radiomic features

49. 49
Kaplan-Meier curves
Correction with slope of linear regression
p = 0.0054 (pCT) and p = 0.00099 (CBCT-FX1)

50. 50
Radiogenomics –Virtual Biopsy
Wu et al., Front Oncol 2016

51. 51
Distributed Radiomics

52. 52
Rapid Learning Health Care

53. 53
Conclusion
• We are still in the very early phase
• A lot of underpowered, exploratory
papers out there
• A lot of dials to control (medical physics
needs to get involved)
• Prospective validation as a decision
support system is needed
• We all can help by collection of highly
standardized images in our clinics
• But the promise is HUGE
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6
Pubmed Radiomics
Radiomics

54. 54
Acknowledgements
• MAASTRO Clinic, Maastricht,The Netherlands
– Philippe Lambin, Ralph Leijenaar,….
• Moffitt Cancer Center,Tampa, FL, USA
– Bob Gillies, Bob Gatenby,…
• Dana-Farber Cancer Institute, Brigham andWomen’s Hospital, Harvard Medical School, Boston
– Hugo Aerts, Emmanuel RiosVelazquez, …
• Radboud University Medical Center, Nijmegen,The Netherlands
• VU University Medical Center, Amsterdam,The Netherlands
More info on: www.radiomics.org

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