摘要：SummaryImmunotherapy shows durable response but only in a subset of patients, and test for predictive biomarkers requires procedures in addition to routine workflow. We proposed a confounder-aware representation learning-based system, genopathomic biomarker for immunotherapy response (PITER), that uses only diagnosis-acquired hematoxylin-eosin (H&E)-stained pathological slides by leveraging histopathological and genetic characteristics to identify candidates for immunotherapy. PITER was generated and tested with three datasets containing 1944 slides of 1239 patients. PITER was found to be a useful biomarker to identify patients of lung adenocarcinoma with both favorable progression-free and overall survival in the immunotherapy cohort (p Graphical abstractDisplay OmittedHighlights•Genetic profile could be extracted by deep learning from digital histologic slides•Extracted pathomic signature could identify patients’ response to immunotherapy•The extracted pathomic signature was involved in an active immune microenvironmentImmunology; Cancer; Artificial intelligence.
