Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images

Junxian Wu, Xinyi Ke, Xiaoming Jiang, Huanwen Wu, Youyong Kong, Lizhi Shao

Keywords: Whole Slide Image, Survival Prediction, Tumor Heterogeneity, Heterogeneous Graph, Graph Convolutional Network

Abstract: Survival prediction is a significant challenge in cancer management. Tumor micro-environment is a highly sophisticated ecosystem consist of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impacts the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods.

Primary Area: Machine learning for healthcare

Submission Number: 16013