Ranking-Based Survival Prediction on Histopathological Whole-Slide Images

Survival prediction for patients based on gigapixel histopathological whole-slide images (WSIs) has attracted increasing attention in recent years. Previous studies mainly focus on the framework of predicting the survival hazard scores based on one individual WSI for each patient directly. These prediction methods ignore the relative survival differences among patients, i.e., the ranking information, which is important for a regression task. Under such circumstances, we propose a ranking-based survival prediction method on WSIs – RankSurv, which takes the ranking information into consideration during the learning process. First, a hypergraph representation is introduced to conduct hazard prediction on each WSI respectively, which is able to learn the high-order correlation among different patches in the WSI. Then, a ranking-based prediction process is conducted using pairwise survival data. Experiments are conducted on three public carcinoma datasets (i.e., LUSC, GBM, and NLST). Quantitative results show that the proposed method significantly outperforms state-of-the-art methods on all three datasets, which demonstrates the effectiveness of the proposed ranking-based survival prediction framework.