Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network

Abstract

Gleason grading of histopathology slides has been the “gold standard” for diagnosis, treatment and prognosis of prostate cancer. For the heterogenous Gleason score 7, patients with Gleason score 3+4 and 4+3 show a significant statistical difference in cancer recurrence and survival outcomes. Considering patients with Gleason score 7 reach up to 40% among all prostate cancers diagnosed, the question of choosing appropriate treatment and management strategy for these people is of utmost importance. In this paper, we present a Graph Neural Network (GNN) based weakly supervised framework for the classification of Gleason score 7. First, we construct the slides as graphs to capture both local relations among patches and global topological information of the whole slides. Then GNN based models are trained for the classification of heterogeneous Gleason score 7. According to the results, our approach obtains the best performance among existing works, with an accuracy of 79.5% on TCGA dataset. The experimental results thus demonstrate the significance of our proposed method in performing the Gleason grading task.