Hierarchical Graph Pathomic Network for Progression Free Survival Prediction

Zichen Wang, Jiayun Li, Zhufeng Pan, Wenyuan Li, Anthony E. Sisk, Huihui Ye, William Speier, Corey W. Arnold

Abstract: High resolution histology images contain information related to disease prognosis. However, survival prediction based on current clinical grading systems, which rely heavily on a pathologist’s histological assessment, has significant limitations due to the heterogeneity and complexity of tissue phenotypes. To address these challenges, we propose a deep learning framework that leverages hierarchical graph-based representations to enable more precise prediction of progression-free survival in prostate cancer patients. Unlike conventional approaches that analyze patch-based or cell-based pathomic features alone without considering their spatial connectivity, we explore multi-scale topological structures of whole slide images in an integrative context. Extensive experiments have demonstrated the effectiveness of our model for better progression prediction.