Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.

ABSTRACT:

Background

Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive. This study aims to develop machine learning models using deep learning features from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict basal and luminal subtypes in BLCA.

Methods

RNA sequencing data and clinical outcomes were downloaded from seven public BLCA databases, including TCGA, GEO datasets, and the IMvigor210C cohort, to assess the prognostic value of BLCA molecular subtypes. WSIs from TCGA were used to construct and validate the machine learning models, while WSIs from Shanghai Tenth People's Hos

SUBMITTER: Zheng Z 

PROVIDER: S-EPMC11844054 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature