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

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.

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 scores. The model's prognostic performance was assessed using Kaplan-Meier analysis and Cox regression. Interpretability was evaluated through GNNExplainer-generated heatmaps.

A total of 80 patients (148 WSIs) were included from three medical centers. The best-performing deep learning model achieved a mean C-index of 0.8254 and stratified patients into high-risk and low-risk groups. Patients in the high-risk group demonstrated significantly shorter overall survival (OS) than those in the low-risk group (log-rank p = 7.4 × 10^-8). Multivariate Cox analysis confirmed AI-based risk stratification as an independent prognostic factor (p = 0.000298), exhibiting a higher hazard ratio (HR = 7.974) than both FIGO stage (HR = 5.877) and tumor grade (HR = 4.248). GNNExplainer further visualized key regions associated with the model's predictions, including infiltrative growth patterns and pronounced nuclear atypia.

This deep learning model offers accurate prognostic predictions from histopathology, presenting a promising tool to improve risk stratification and guide personalized treatment in PMOC.