Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning…

• CC
  Conquer Cancer Foundation
• DR
  Damon Runyon Cancer Research Foundation

  Award: CI-98-18
• MS
  Memorial Sloan-Kettering Cancer Center

  Awards: T32-CA009207, CA008748, P30-CA008748, UL1TR00457, K30-UL1TR00457
• CF
  Cycle for Survival

  Award: P30-CA008748
• NI
  National Institutes of Health

  Awards: K30-UL1TR00457, T32GM007739, CA009207, UL1TR00457, T32-CA009207, P30-CA008748
• WC
  Weill Cornell Medical College

  Award: T32GM007739
• NC
  National Cancer Institute

  Awards: CA008748, T32-CA009207, T32GM007739, P30CA008748, P30-CA008748, F30CA257414
• NI
  National Institute of General Medical Sciences

  Award: T32GM007739