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Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics

Dercle, Laurent, Fronheiser, Matthew, Lu, Lin, Du, Shuyan, Hayes, Wendy, Leung, David K., Roy, Amit, Wilkerson, Julia +4 more·2020·Clinical Cancer Research
DOI 10.1158/1078-0432.ccr-19-2942PMC PMC9239371

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Abstract

Purpose: Using standard-of-care CT images obtained from patients with a diagnosis of non–small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib. Experimental Design: Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity. Results: The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55–1.00); docetaxel, 0.67 (0.37–0.96); and gefitinib, 0.82 (0.53–0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival. Conclusions: Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.

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  • Neural network models have been used to forecast postoperative outcomes in NSCLC, demonstrating strong performance in predicting cardio-respiratory toxicity and postoperative complications.

    “Neural network (NN) models have also been used to forecast postoperative outcomes in NSCLC, demonstrating strong performance in predicting cardio-respiratory toxicity and postoperative complications.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [43]

    The Discussion describes machine-learning analysis of CT images to predict sensitivity to systemic therapies in NSCLC, not postoperative outcomes. Searches for neural networks, cardio-respiratory toxicity, and postoperative complications found no passage addressing those predictions in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Dercle, L., Fronheiser, M., Lu, L., Du, S., Hayes, W., Leung, D. K., Roy, A., Wilkerson, J., Guo, P., Fojo, A. T., Schwartz, L. H., & Zhao, B.
Affiliations
  • 1Department of Radiology, Columbia University Medical Center/New York Presbyterian Hospital, New York, New York.
  • 3Translational Medicine, Bristol-Myers Squibb, Princeton, New Jersey.
  • 4Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Princeton, New Jersey.
  • 5The National Cancer Institute, NIH, Bethesda, Maryland.
  • 6Columbia University/New York Presbyterian Hospital and James J. Peters VA Medical Center, New York, New York.
Venue
Clinical Cancer Research
Publisher
American Association for Cancer Research (AACR)
Published
2020-05-01
Volume
26
Issue
9
Pages
2151-2162
ISSN
  • 1078-0432
  • 1557-3265
DOI
10.1158/1078-0432.ccr-19-2942
PMC
PMC9239371
Cited by
173
References
3
Language
en
Funders
NIH (U01 CA225431)

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