Development and validation of a deep learning-based pathomics signature for prognosis and chemotherapy benefits in colorectal cancer: a retrospective multicenter cohort study.

ABSTRACT:

Introduction

The conventional tumor-node-metastasis (TNM) classification system remains limited in accurately forecasting prognosis and guiding adjuvant chemotherapy decisions for patients with colorectal cancer (CRC). To address this gap, we introduced and validated a novel pathomics signature (PS_CRC) derived from hematoxylin and eosin-stained whole slide images, leveraging a deep learning framework.

Methods

This retrospective study analyzed 883 slides from two independent cohorts. An interpretable multi-instance learning model was developed to construct PS_CRC, with SHapley Additive exPlanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) for the improvement of model interpretability and the identification of critical histopatholog

SUBMITTER: Lou S 

PROVIDER: S-EPMC12280904 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature