Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images

Abstract

Muscle invasive bladder cancer (MIBC) is a highly heterogeneous and costly disease with significant morbidity and mortality. Understanding tumor histopathology leads to tailored therapies and improved outcomes. In this study, we employed weakly supervised learning and neural architecture search to develop a data-driven scoring system. This system aimed to capture prognostic histopathological patterns observed in H&E-stained whole slide images. We constructed and externally validated our scoring system using multi-institutional datasets with 653 whole-slide images. Additionally, we performed survival analyses and explored the association between our scoring system and seven histopathological features, as well as 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The hazard ratio for overall mortality was 1.46 (95% CI 1.05-2.02; z: 2.23; p=0.03). Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, as well as several gene mutations including FGFR3 in MIBC. Our findings underscore the risk of confounding bias when reducing the complex biological and clinical behavior of tumors to a single mutation. Histopathological changes can only be fully captured through the use of comprehensive multi-omics profiles. The introduction of our scoring system has the potential to enhance daily clinical decision-making for MIBC. It facilitates shared decision-making by offering comprehensive and precise risk stratification, treatment planning, and cost-effective preselection for expensive molecular characterization.