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

Authors

Jianqiu Kong, Yi Huang, Yichun Xing, Shuogui Fang, Kaiwen Tan, Juanjuan Yong, Sha Fu, Yaqiang Huang, Chun Jiang, Xinxiang Fan

Published in

BMC medical imaging. Volume 26. Issue 1. Apr 14, 2026. Epub Apr 14, 2026.

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.

PMID:
42443785
Bibliographic data and abstract were imported from PubMed on 14 Jul 2026.

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