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<p>Predicting the prognosis of bladder cancer remains challenging despite standard treatments. We developed an interpretable bladder cancer deep learning (BCDL) model using preoperative CT scans to predict overall survival. The model was trained on a cohort (<i>n</i> = 765) and validated in three independent cohorts (<i>n</i> = 438; <i>n</i> = 181; <i>n</i> = 72). The BCDL model outperformed other models in survival risk prediction, with the SHapley Additive exPlanation method identifying pixel-level features contributing to predictions. Patients were stratified into high- and low-risk groups using deep learning score cutoff. Adjuvant therapy significantly improved overall survival in high-risk patients (<i>p</i> = 0.028) and women in the low-risk group (<i>p</i> = 0.046). RNA sequencing analysis revealed differential gene expression and pathway enrichment between risk groups, with high-risk patients exhibiting an immunosuppressive microenvironment and altered microbial composition. Our BCDL model accurately predicts survival risk and supports personalized treatment strategies for improved clinical decision-making.</p>

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An interpretable CT-based deep learning model for predicting overall survival in patients with bladder cancer: a multicenter study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月16日

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  • Meng Zhang,
  • Yizhong Zhao,
  • Dapeng Hao,
  • Yancheng Song,
  • Xiaotong Lin,
  • Feng Hou,
  • Yonghua Huang,
  • Shifeng Yang,
  • Haitao Niu,
  • Cheng Lu,
  • Hexiang Wang

摘要

Predicting the prognosis of bladder cancer remains challenging despite standard treatments. We developed an interpretable bladder cancer deep learning (BCDL) model using preoperative CT scans to predict overall survival. The model was trained on a cohort (n = 765) and validated in three independent cohorts (n = 438; n = 181; n = 72). The BCDL model outperformed other models in survival risk prediction, with the SHapley Additive exPlanation method identifying pixel-level features contributing to predictions. Patients were stratified into high- and low-risk groups using deep learning score cutoff. Adjuvant therapy significantly improved overall survival in high-risk patients (p = 0.028) and women in the low-risk group (p = 0.046). RNA sequencing analysis revealed differential gene expression and pathway enrichment between risk groups, with high-risk patients exhibiting an immunosuppressive microenvironment and altered microbial composition. Our BCDL model accurately predicts survival risk and supports personalized treatment strategies for improved clinical decision-making.