Scholarlink

<p>Bladder cancer (BCa) prognostication is pivotal for tailored clinical interventions. 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’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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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

摘要

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.