Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction.

ABSTRACT: Immuno-oncology and the advent of immunotherapies, in particular immune checkpoint inhibitors (ICIs), have fundamentally altered the way we treat cancer. Yet only a small subset of patients actually responds to ICIs, and many face significant adverse effects, making the accurate selection of patients for ICIs essential to the work of immuno-oncology. Immune biomarkers, such as programmed death-ligand 1, microsatellite instability/defective mismatch repair, and tumor mutational burden have been developed for patient selection and stratification for ICIs, though their predictive abilities remain limited. This is due to several challenges: lack of adequate tissue sampling, the time-consuming and subjective nature of manual visual-based quantification techniques, and the growing recognition of

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12323524 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature