Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.

ABSTRACT:

Background

Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI).

Methods

This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the

SUBMITTER: Han Z 

PROVIDER: S-EPMC11097892 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature