Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers

Abstract Bladder cancer (BCa) prognostication is pivotal for tailored clinical interventions. Using machine learning, this study assesses prognostic capabilities of H&E-stained BCa images. From 569 slides across The Cancer Genome Atlas, Sun Yat-sen Memorial Hospital, and Zhongshan City People’s Hospital, we extracted 150 histopathological markers each. LASSO regression yielded a pathomic fingerprint, which was further validated. An integrated model, fusing this fingerprint with salient clinicopathological indicators, displayed notable efficacy in both training (C-index: 0.658) and validation cohorts (C-index: 0.590–0.597). Incorporating the fingerprint, age, and N stage, the model excelled in training (C-index: 0.703) and validations (C-index: 0.612–0.646). Decision curve analysis underscored its clinical relevance. Conclusively, our pathomic-clinical framework offers advanced precision in BCa patient prognosis, enhancing clinical decision-making.