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Poster Display session 3

2534 - Radiomic Signatures for Identification of Tumors Sensitive to Nivolumab or Docetaxel in Squamous Non-Small Cell Lung Cancer (sqNSCLC)


30 Sep 2019


Poster Display session 3


Translational Research

Tumour Site

Non-Small Cell Lung Cancer


Laurent Dercle


Annals of Oncology (2019) 30 (suppl_5): v760-v796. 10.1093/annonc/mdz268


L. Dercle1, M. Fronheiser2, L. Lu1, S. Du2, W. Hayes2, D.K. Leung2, A. Roy3, L.H. Schwartz1, B. Zhao1

Author affiliations

  • 1 Department Of Radiology, Columbia University Medical Center/New York Presbyterian Hospital, 10032 - New York/US
  • 2 Translational Medicine, Bristol-Myers Squibb, 08648 - Princeton/US
  • 3 Clinical Pharmacology & Pharmacometrics, Bristol-Myers Squibb, 08648 - Princeton/US


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Abstract 2534


Radiomic signatures offer the potential to enhance clinical decision-making as on-treatment markers of efficacy to assess which patients (pts) should continue treatment. Using treatment-related radiomic signatures via quantitative, artificial intelligence (AI)-based analysis of computed tomography (CT) images, we evaluated early tumor changes in pts with sqNSCLC treated in 2 treatment groups: nivolumab (group A) or docetaxel (group B).


Data from pts with sqNSCLC were collected prospectively and analyzed retrospectively across 2 multicenter clinical trials (A, n = 92 CheckMate 017 [NCT01642004], CheckMate 063 [NCT01721759]; B, n = 50 CheckMate 017). For the current study, pts with a measurable lung lesion and baseline and on-treatment assessments (8 weeks) were randomized to training (T) or validation (V) datasets (A: 72T, 20V; B: 32T, 18V;). For each pt, the largest measurable lung tumor was segmented to extract 1,749 radiomic features. Pts were classified as treatment-sensitive or -resistant using median progression-free survival (PFS) calculated from pts included in this study (A, B). Using AI-based methodologies, up to 4 features were selected and combined to develop a signature score (range, 0-1) in the T datasets and applied to each pt in the V datasets to classify sensitivity to treatment.


The radiomics features associated with treatment sensitivity in the T datasets were a decrease in tumor volume (A, B), infiltration of tumor boundaries (A), or tumor spatial heterogeneity (A). The radiomic signatures predicted treatment sensitivity in the V dataset of each study group (AUC [95% CI]: A, 0.77 [0.55-1.00]; B, 0.67 [0.37-0.96]).


AI-based CT imaging detected early changes in radiomic features from baseline to first on-treatment tumor assessment—decrease in tumor volume, tumor heterogeneity, and tumor infiltrativeness along boundaries—that were associated with sensitivity to treatment in pts with sqNSCLC, offering an approach that could guide clinical decision-making to continue or modify systemic therapies.

Clinical trial identification

CheckMate 017 [NCT01642004] July 17, 2012 (first posted date) CheckMate 063 [NCT01721759] November 6, 2012 (first posted date).

Editorial acknowledgement

Legal entity responsible for the study

Bristol-Myers Squibb and Columbia University Medical Center.


Bristol-Myers Squibb.


M. Fronheiser: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. S. Du: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. W. Hayes: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. D.K. Leung: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. A. Roy: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. L.H. Schwartz: Research grant / Funding (self), Member DSMB: Merck; Research grant / Funding (self), Member DSMB: Novartis; Research grant / Funding (self), Consultant endpoint analysis: Boehringer Ingelheim. All other authors have declared no conflicts of interest.

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