What question did this study set out to answer?

The research aims to evaluate the prognostic capabilities of pathomic analyses using machine learning in bladder cancer.

April 16, 2026BMC Medical Imaging0 citationsOpen Access

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

JKJianqiu KongSun Yat-sen University YHYi HuangSun Yat-sen University YXYichun XingSun Yat-sen University

Key Points

The research aims to evaluate the prognostic capabilities of pathomic analyses using machine learning in bladder cancer.
Analyzed 569 H&E-stained bladder cancer slides from multiple centers.
Extracted 150 histopathological markers from images.
Applied LASSO regression to develop a pathomic fingerprint.
Integrated the pathomic fingerprint with clinicopathological indicators for model validation.
Achieved a C-index of 0.658 in training and 0.590–0.597 in validation cohorts.
Combined model with age and N stage resulted in a C-index of 0.703 in training and 0.612–0.646 in validations.
Decision curve analysis indicated significant clinical relevance of the integrated model.

Abstract

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.

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