Predicting ustekinumab treatment response in Crohn's disease using pre-treatment biopsy images.

ABSTRACT:

Motivation

Crohn's disease (CD) exhibits substantial variability in response to biological therapies such as ustekinumab (UST), a monoclonal antibody targeting interleukin-12/23. However, predicting individual treatment responses remains difficult due to the lack of reliable histopathological biomarkers and the morphological complexity of tissue. While recent deep learning methods have leveraged whole-slide images (WSIs), most lack effective mechanisms for selecting relevant regions and integrating patch-level evidence into robust patient-level predictions. Therefore, a framework that captures local histological cues and global tissue context is needed to improve prediction performance.

Results

We propose a novel clustering-enhanced weakly supervised learning framework to pr

SUBMITTER: Cai C 

PROVIDER: S-EPMC12133262 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature