Advertisement
Journal of Thoracic Oncology
IASLC
Advanced searchSave search
Original Article|Articles in Press

Baseline radiomic signature to estimate overall survival in patients with non-small cell lung cancer

Published:January 12, 2023DOI:https://doi.org/10.1016/j.jtho.2022.12.019
Baseline radiomic signature to estimate overall survival in patients with non-small cell lung cancer
Previous ArticleCamrelizumab Plus Carboplatin and Pemetrexed as First-line Treatment for Advanced Non-squamous NSCLC: Extended Follow-up of CameL Phase 3 Trial
Next ArticleImpact of Duration of Diagnostic Workup on Prognosis for Early Lung Cancer
      This paper is only available as a PDF. To read, Please Download here.

      Abstract:

      Introduction

      We aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed-tomography (CT) images obtained from patients with non-small cell lung cancer (NSCLC) treated with nivolumab or chemotherapy.

      Methods

      The radiomics signature was developed in patients with NSCLC treated with nivolumab in CheckMate 017, 026, and 063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semi-automatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomics signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies.

      Results

      A baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy.

      Conclusions

      The radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.

      Key words

      Abbreviation list:

      AI (artificial intelligence), AUC (area under the curve), CI (confidence interval), CT (computed tomography), DWT (discrete wavelet transform), GLCM (gray-level co-occurrence matrix), HR (hazard ratio), ICI (immune checkpoint inhibitor), LN (lymph node), NA (not applicable), NSCLC (non-small cell lung cancer), OS (overall survival), PD-1 (programmed death-1), PD-L1 (programmed death ligand 1), ROC (receiver operating characteristic), SCC (squamous cell carcinoma)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Thoracic Oncology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Register: Create an account
      Institutional Access: Sign in to ScienceDirect

      Article info

      Publication history

      Accepted: December 20, 2022
      Received in revised form: November 22, 2022
      Received: March 22, 2022

      Publication stage

      In Press Journal Pre-Proof

      Footnotes

      Funding

      This work was supported by the National Institutes of Health (grant number U01 CA225431) and Bristol Myers Squibb. The content is solely the responsibility of the authors and does not necessarily represent the funding sources.

      Competing interests

      M.F. is an employee of Bristol Myers Squibb. S.M. is an employee of Bristol Myers Squibb. W.H. was an employee of Bristol Myers Squibb at the time the study was conducted and holds ownership interest (including patents) in Bristol Myers Squibb. D.K.L. is an employee of Bristol Myers Squibb. L.H.S. is a paid advisory board member for Roche and Novartis and reports receiving commercial research grants from Merck and Boehringer Ingelheim. M.D.H. reports grants from Bristol Myers Squibb, nonfinancial support from AstraZeneca and Bristol Myers Squibb, personal fees from Achilles, Arcus, AstraZeneca, Blueprint, Bristol Myers Squibb, Genentech/Roche, Immunai, Instil Bio, Janssen, Merck, Mirati, Natera, Nektar, Pact Pharma, Regeneron, Shattuck Labs, and Syndax, and equity options from Immunai, Shattuck Labs, and Arcus. A patent filed by Memorial Sloan Kettering Cancer Center related to the use of tumor mutational burden to predict response to immunotherapy (PCT/US2015/062208) is pending and licensed by PGDx. No potential conflicts of interest were disclosed by the other authors.

      Author contributions

      Laurent Dercle: Conceptualization, Formal analysis, Software, Roles/Writing - original draft, Writing - review & editing

      Matthew Fronheiser: Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Naiyer A. Rizvi: Data curation, Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Matthew D. Hellmann: Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Sabine Maier: Conceptualization, Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Wendy Hayes: Conceptualization, Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Hao Yang: Software, Roles/Writing - original draft, Writing - review & editing

      Pingzhen Guo: Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Tito Fojo: Conceptualization, Formal analysis, Software, Roles/Writing - original draft, Writing - review & editing

      Lawrence H. Schwartz: Conceptualization, Formal analysis, Roles/Writing - original draft, Writing - review & editing

      Binsheng Zhao: Conceptualization, Formal analysis, Software, Roles/Writing - original draft, Writing - review & editing

      David K. Leung: Conceptualization, Data curation, Formal analysis, Software, Roles/Writing - original draft, Writing - review & editing

      Identification

      DOI: https://doi.org/10.1016/j.jtho.2022.12.019

      Copyright

      © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

      ScienceDirect

      Access this article on ScienceDirect
      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties. The content on this site is intended for healthcare professionals.


      RELX