ArticleBMC medical imaging2026
Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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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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