Integrated multicenter deep learning system for prognostic prediction in bladder cancer.

ABSTRACT: Precise survival risk stratification is crucial for personalized therapy in bladder cancer (BCa). This study developed and validated an end-to-end deep learning system using histological slides to predict overall survival (OS) risk in BCa patients. We employed the BlaPaSeg tile classifier to generate tissue probability heatmaps and segmentation maps, trained two prognostic networks, MacroVisionNet and UniVisionNet, and explored six potential BCa prognostic biomarkers. Across all cohorts, the AUC for BlaPaSeg ranged from 0.9906 to 0.9945, while the C-index varied from 0.655 to 0.834 for MacroVisionNet and 0.661 to 0.853 for UniVisionNet. After covariate adjustment, the hazard ratio (HR) values for high-risk groups were 1.97 to 5.06 in MacroVisionNet and 2.13 to 4.01 in UniVisionNet. The hig

SUBMITTER: He Q 

PROVIDER: S-EPMC11484793 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature