Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks

Abstract

Prostate cancer is the most common nonskin-related cancer, affecting one in seven men in the United States. Gleason score, a sum of the primary and secondary Gleason patterns, is one of the best predictors of prostate cancer outcomes. Recently, significant progress has been made in molecular subtyping prostate cancer through the use of genomic sequencing. It has been established that prostate cancer patients presented with a Gleason score 7 show heterogeneity in both disease recurrence and survival. We built a unified system using publicly available whole-slide images and genomic data of histopathology specimens through deep neural networks to identify a set of computational biomarkers. Using a survival model, the experimental results on the public prostate dataset showed that the computational biomarkers extracted by our approach had hazard ratio as 5.73 and C -index as 0.74, which were higher than standard clinical prognostic factors and other engineered image texture features. Collectively, the results of this study highlight the important role of neural network analysis of prostate cancer and the potential of such approaches in other precision medicine applications.