Radiomics for 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), 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

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LectureLearning 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 minsPart 2: Radiomics workflow & challenges – 25 minsPart 3: New directions in Radiomics – 15 mins

Part 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

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Cancer Data?

Oncology2005-2015140M patients0.1-10GB per patient14-1400PB80% unstructured100k hospitals

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Images are not picture, they are data

Gillies et al., Radiology 2016;278(2). Larue, et al., Br J Radiol 2017

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Nature selects for phenotype

Lambin et al., Eur J Cancer. 2012 Mar;48(4):441-6

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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?)

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

Part 2 - Radiomics workflow & challenges

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Radiomics WorkflowLambin, Walsh et al., Nat Rev Clin Oncol (in-press)

Larue, et al., Br J Radiol 2017

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Guide

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Feature Extraction – Imaging Protocols

Oliver et al. , Translational Oncology (2015) 8, 524–534

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Guide

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Feature Extraction – Robust Segmentation

Parmar et al., PLoS One. 2014; 9(7): e102107.

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Feature Extraction – Robust Segmentation

Parmar et al., PLoS One. 2014; 9(7): e102107.

Approaches1. Perform semi-automatic segmentation2. Remove features which are too sensitive to the exact

segmentation

Larue, et al., Br J Radiol 2017

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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

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Guide

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Feature Extraction - Software

Non-texture-based features: Histogram, GeometryTexture-based features: GLCM, GLRLM

Sample capacity: 31 51 33

Correlation Coefficients Distribution

correlation coefficient range

Fudan University Cancer Hospital (unpublished)

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Feature Extraction – Phantom / Ontology

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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)

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Rectum clinical vs. Lung coffee-break

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Guide

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Combining with clinical

Aerts, JAMA Oncol 2016

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Dimensionality reduction - Archetypes

Gillies et al., Radiology 2016;278(2).

219 features in 235 patients

Aerts et al., Nature Communications 5, 4006

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Guide

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Our modelling approach

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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)

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Source: Jason Brownlee (2013)

Machine Learning

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Considerations for machine learning• Discrimination (AUC)• Calibration (Brier)

• Interpretability (black box vs. transparent)

• Can it handle low data quality (of training and validation)?

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Choose alreadySimple and quick, but need complete data• Logistic regression• Support Vector Machines

Intuitive and can handle missing data• Bayesian Networks

Review pending

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TRIPOD

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

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So, Radiomics needs a lot of training data….

Aerts et al., Nature Communications 5, 4006

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…. and a lot of validation data

Aerts et al., Nature Communications 5, 4006

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis

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Radiomics – End result

Part 3: New directions

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Radiomics – Preclinical

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Radiomics – PET

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Radiomics - MRI• Rectal cancer - Chemoradiation• Pathological response• Training n=173, Validation n=25• AUC 0.79 (validation)

1) MR GTV delineation

2) GTV ROI extraction

3) LoG filter application according different s

0.3 0.5 1.0 2.0 3.0 4.0

4) Data analysis

|||cT

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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

TRG1Probability

0 1 2

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

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Radiomics outside of oncology

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Radiomics – Delta Radiomics

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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

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Kaplan-Meier curves Correction with slope of linear regression

p = 0.0054 (pCT) and p = 0.00099 (CBCT-FX1)

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Radiogenomics – Virtual Biopsy

Wu et al., Front Oncol 2016

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Distributed Radiomics

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Rapid Learning Health Care

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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

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Pubmed RadiomicsRadiomics

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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 and Women’s Hospital, Harvard Medical

School, Boston– Hugo Aerts, Emmanuel Rios Velazquez, …

• Radboud University Medical Center, Nijmegen, The Netherlands• VU University Medical Center, Amsterdam, The Netherlands

More info on: www.radiomics.org

Thank you for your attention

Andre DekkerDepartment of Radiation Oncology (MAASTRO)GROW - Maastricht University Medical Centre +Maastricht, The Netherlands