Ensemble of Prior-guided Expert Graph Models for Survival Prediction in Digital Pathology

Vishwesh Ramanathan, Pushpak Pati, Matthew McNeil, Anne L. Martel

Abstract: Survival prediction in pathology is a dynamic research field focused on identifying predictive biomarkers to enhance cancer survival models, providing valuable guidance for clinicians in treatment decisions. Graph-based methods, especially Graph Neural Networks (GNNs) leveraging rich interactions among different biological entities, have recently successfully predicted survival. However, the inherent heterogeneity among the entities within tissue slides significantly challenges the learning of GNNs. GNNs, operating with the homophily assumption, diffuse the intricate interactions among heterogeneous tissue entities in a tissue microenvironment. Further, the convoluted downstream task relevant information is not effectively exploited by graph-based methods when working with large slide-graphs. We propose a novel prior-guided, edge-attributed tissue-graph construction to address these challenges, followed by an ensemble of expert graph-attention survival models. Our method exploits diverse prognostic factors within numerous targeted tissue subgraphs of heterogeneous large slide-graphs. Our method achieves state-of-the-art results on four cancer types, improving overall survival prediction by 4.33% compared to the competing methods. Our code is publically available on https://github.com/Vishwesh4/DGNN.