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Radiomics in Clinical Trials Laurence Court

Departments of Radiation Oncology and Imaging Physics

University of Texas MD Anderson Cancer Center

Conflicts of Interest

• Radiomics projects funded by the NCI

• Other projects funded by the NCI, CPRIT, Varian, Elekta

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Significant growth in radiomics

Figure from Philippe Lambin, MAASTRO

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Goals of radiomics studies

Buckler, et al., A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging, Radiology 258:906-914, 2011

Based slides from Xenia Fave and Ed Jackson

General Radiomics Hypothesis: Quantitative image features are related to underlying gene expression and phenotype

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Classifing Tumors

• Benign v. Malignant, Wang 2010

• SCC v. ACC, Basu 2011

Predicting Outcomes

• Aerts 2014 • Fried 2015

Links to Genomics

• K-ras mutant, Weiss 2014

• MAPK pathway, Miles 2016

Monitoring Response

• Fave 2017

Imaging biomarker roadmap

Figure from O’Connor et al., Nat Rev Clin Oncol 14(3), 169-186, 2017

Radiomics for prospective trials • Radiomics takes advantage of imaging that is happening anyway!

• Sufficiently advanced that we should be planning radiomics studies for all prospective trials • Embed in protocol (e.g. as secondary endpoint)

• Additional QA for radiomics aspects of the trial

Treatment delivery accreditation by IROC-Houston ACR CT phantom

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Radiomics workflow

Figure adapted from Aerts et al, Nature Communications 2015

Imaging Pre-processing Feature extraction Analysis Segmentation

Includes: • Imaging protocol • Motion

management

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Including feature selection

Imaging protocols should be harmonized • Texture phantom

• Acquired 17 scans from GE, Philips, Siemens and Toshiba scanners scattered throughout the Texas Medical Center

Dennis Mackin et al, Investigative Radiology 50(11), 757-765, 2015 Data from Dennis Mackin

Harmonization of PET imaging

Harmonize protocols

Figures from Robert Jeraj, University of Wisconsin. Also see Lin et al, JNM 2016

• Two scanners: Discovery (GE) and Gemini (Philips) • Harmonize GE scanner to the Philips scanner

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Minimize inter-scanner variability

• Minimize the number of scanners (to one…..) • Aerts et al, Defining a Radiomics Response

Phenotype: A Pilot Study using targeted therapy in NSCLC, Scientific Reports 6, 33860, 2016 • 47 patients • Radiomics data could predict EGFR-mutation status

and associated gefitinib response

• Minimize the number of scanners (to a few) • Fried et al, Prognostic Value and Reproducibility

of Pretreatment CT texture features in stage III non-small cell lung cancer, IJROBP 90(4), 834-842, 2014

But care is still needed…….. • 110 NSCLC patients with advanced stage disease

• Randomized to treatment with IMRT or PSPT to 66 or 74 Gy

• Received concurrent chemotherapy

• Imaged with a 4DCT weekly during treatment (5-9 4DCTs per patient)

• Most on one of two CT scanners (both GE, one was widebore, one was PET-CT)

• Texture signature was a function of CT scanner

Data from Xenia Fave, UT MD Anderson Cancer Center (published in Scientific Reports 7, 588, 2017 11

CT Model Dependence can be mitigated by image preprocessing • Scanner independence, p-value of Wilcoxon rank sum test

• No features were ALWAYS significantly associated with CT model

• Suggests that image preprocessing can be used to remove differences between CT scanners

• 24 features were ALWAYS independent of CT model

• Included features from all 4 categories

• For 31 features that were sometimes dependent,

• 2 features were fixed with bit depth resampling

• 28 features with smoothing

• 27 with smoothing and bit depth resampling

• 6 no preprocessing

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+

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Radiomics workflow

Figure adapted from Aerts et al, Nature Communications 2015

Imaging Pre-processing Feature extraction Analysis Segmentation

Includes: • Imaging protocol • Motion

management

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Including feature selection

Segmentation

Based on Hunter et al, Med Phys 40, 121916, 2013 14

Increasing pixel removal

• PET texture – tracer uptake heterogeneity: • Metabolism • Hypoxia • Cellular proliferation • Vascularization • Necrosis

• CT texture - tissue density heterogeneity: • Vascularization • Necrosis • Relative fat, air, water content

• But - we don’t understand the meaning of the radiomics texture features in terms of pathophysiology

• Reproducibility/robustness is important • If using pre-drawn contours, consider the original task

Hatt et al, Eur J Nucl Mol Imaging, 2016

• Benchmark standards (standard images etc) • Open source algorithms • Standardization

• Phantoms • Best (consensus) practices • Accurate reporting • Data sharing 15

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Resources • Court et al, Computational resources for radiomics, Translational Cancer Research 5(4), 340-348, 2016

• Larue et al, Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures, Brit. J. Radiol. 90, 20160665, 2017

• www.Radiomics.world – Radiomics Quality Score

www.radiomics.world

Final thoughts • Radiomics:

• Is correlated to biology

• Can improve accuracy of prediction

• Can calculate probability of certain diagnosis

• Can be used in clinical trials

• (and is low cost and non-invasive)

• Re-analyse your image data whenever possible

• Plan radiomics studies in all your prospective trials

• Embed radiomics studies into the protocol (e.g. as a secondary endpoint or hypothesis generative approach monitoring treatment response)

• Careful consideration of the imaging protocols

• Full transparency on everything else

• Imaging

• Feature extraction (details)

• Analysis

Partially based on slides by Philippe Lambin, MAASTRO Fried et al, IJROBP 94, 368-376, 2015

Research group and collaborators

Our group (past and present)

• Joy Zhang

• Jinzhong Yang

• Dennis Mackin

• Luke Hunter

• David Fried

• Xenia Fave

• Joonsang Lee

• Rachel Ger

Physics

• Osama Mawlawi

• Peter Balter

Radiation Oncology and Radiology

• Zhongxing Liao

• Steven Lin

• Daniel Gomez

• Chaan Ng

• Joe Chang

• Dave Fuller

Statistics

• Shouhao Zhou

• Susan Tucker

• Francesco Stingo

• Arvind Rao

• Center for Radiation Oncology Research 18