Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics

Clin Cancer Res. 2020 May 1;26(9):2151-2162. doi: 10.1158/1078-0432.CCR-19-2942. Epub 2020 Mar 20.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use*
  • Carcinoma, Non-Small-Cell Lung / diagnostic imaging*
  • Carcinoma, Non-Small-Cell Lung / drug therapy*
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Clinical Trials, Phase II as Topic
  • Clinical Trials, Phase III as Topic
  • Disease Progression
  • Docetaxel / administration & dosage
  • Female
  • Gefitinib / administration & dosage
  • Humans
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / drug therapy*
  • Lung Neoplasms / pathology
  • Machine Learning*
  • Male
  • Nivolumab / administration & dosage
  • Prognosis
  • Randomized Controlled Trials as Topic
  • Retrospective Studies
  • Survival Rate
  • Tomography, X-Ray Computed / methods*


  • Docetaxel
  • Nivolumab
  • Gefitinib

Associated data