Histopathology images-based deep learning prediction of prognosis in primary mucinous ovarian carcinoma.

ABSTRACT:

Background

Accurately predicting the prognosis of primary mucinous ovarian carcinoma (PMOC) remains a significant challenge in gynecologic oncology. This study aimed to develop and validate a deep learning model using histopathological images for precise prognostic prediction and risk stratification in PMOC.

Methods

Histopathological slides of PMOC patients were retrospectively collected and digitized into whole-slide images (WSIs). A graph-based deep learning survival model was established by integrating histological feature extraction, spatial graph construction, and survival prediction through graph neural networks (GNN) combined with Cox proportional hazards modeling. Patients were subsequently stratified into high- and low-risk groups based on model-generated risk score

SUBMITTER: Zhang M 

PROVIDER: S-EPMC12921705 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature