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Using machine learning, this study assesses prognostic capabilities of H&amp;E-stained BCa images. From 569 slides across The Cancer Genome Atlas, Sun Yat-sen Memorial Hospital, and Zhongshan City People\u2019s 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\u20130.597). Incorporating the fingerprint, age, and N stage, the model excelled in training (C-index: 0.703) and validations (C-index: 0.612\u20130.646). Decision curve analysis underscored its clinical relevance. 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The study was approved by the Ethics Committees of Sun Yat-sen Memorial Hospital (SYSH, Guangzhou, China; reference SYSEC-KY-KS-2021-301) and Zhongshan City People\u2019s Hospital (ZCPH, Zhongshan, China; reference K2021-152 for ZCPH). The need for informed consent was waived for the retrospective evaluation of the external validation cohorts.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"266"}}