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Cancer Discovery
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Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade

Kathryn C. Arbour, Anh Tuan Luu, Jia Luo, Hira Rizvi, Andrew J. Plodkowski, Mustafa Sakhi, Kevin B. Huang, Subba R. Digumarthy, Michelle S. Ginsberg, Jeffrey Girshman, Mark G. Kris, Gregory J. Riely, Adam Yala, Justin F. Gainor, Regina Barzilay and Matthew D. Hellmann
Kathryn C. Arbour
1Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York.
2Department of Medicine, Weill Cornell Medical Center, New York, New York.
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  • ORCID record for Kathryn C. Arbour
Anh Tuan Luu
3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute for Technology, Cambridge, Massachusetts.
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Jia Luo
1Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York.
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Hira Rizvi
1Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York.
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Andrew J. Plodkowski
4Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
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Mustafa Sakhi
5Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts.
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Kevin B. Huang
5Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts.
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Subba R. Digumarthy
6Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
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Michelle S. Ginsberg
4Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
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Jeffrey Girshman
4Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
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Mark G. Kris
1Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York.
2Department of Medicine, Weill Cornell Medical Center, New York, New York.
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Gregory J. Riely
1Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York.
2Department of Medicine, Weill Cornell Medical Center, New York, New York.
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Adam Yala
3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute for Technology, Cambridge, Massachusetts.
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Justin F. Gainor
4Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
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Regina Barzilay
3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute for Technology, Cambridge, Massachusetts.
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  • For correspondence: hellmanm@mskcc.org regina@csail.mit.edu
Matthew D. Hellmann
1Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York.
2Department of Medicine, Weill Cornell Medical Center, New York, New York.
7Parker Institute for Cancer Immunotherapy at Memorial Sloan Kettering Cancer Center, New York, New York.
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  • For correspondence: hellmanm@mskcc.org regina@csail.mit.edu
DOI: 10.1158/2159-8290.CD-20-0419 Published January 2021
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Abstract

Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine-learning techniques to integrate and interrogate large and otherwise underutilized datasets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep-learning model could be trained to use radiology text reports to estimate gold-standard RECIST-defined outcomes. Using text reports from patients with non–small cell lung cancer treated with PD-1 blockade in a training cohort and two test cohorts, we developed a deep-learning model to accurately estimate best overall response and progression-free survival. Our model may be a tool to determine outcomes at scale, enabling analyses of large clinical databases.

Significance: We developed and validated a deep-learning model trained on radiology text reports to estimate gold-standard objective response categories used in clinical trial assessments. This tool may facilitate analysis of large real-world oncology datasets using objective outcome metrics determined more reliably and at greater scale than currently possible.

This article is highlighted in the In This Issue feature, p. 1

Footnotes

  • Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/).

  • Cancer Discov 2021;11:59–67

  • Received April 3, 2020.
  • Revision received July 10, 2020.
  • Accepted September 16, 2020.
  • Published first September 21, 2020.
  • ©2020 American Association for Cancer Research.
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Cancer Discovery: 11 (1)
January 2021
Volume 11, Issue 1
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Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade
Kathryn C. Arbour, Anh Tuan Luu, Jia Luo, Hira Rizvi, Andrew J. Plodkowski, Mustafa Sakhi, Kevin B. Huang, Subba R. Digumarthy, Michelle S. Ginsberg, Jeffrey Girshman, Mark G. Kris, Gregory J. Riely, Adam Yala, Justin F. Gainor, Regina Barzilay and Matthew D. Hellmann
Cancer Discov January 1 2021 (11) (1) 59-67; DOI: 10.1158/2159-8290.CD-20-0419

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Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade
Kathryn C. Arbour, Anh Tuan Luu, Jia Luo, Hira Rizvi, Andrew J. Plodkowski, Mustafa Sakhi, Kevin B. Huang, Subba R. Digumarthy, Michelle S. Ginsberg, Jeffrey Girshman, Mark G. Kris, Gregory J. Riely, Adam Yala, Justin F. Gainor, Regina Barzilay and Matthew D. Hellmann
Cancer Discov January 1 2021 (11) (1) 59-67; DOI: 10.1158/2159-8290.CD-20-0419
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