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Purpose <p>To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.</p> Methods <p>A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score. External validation was conducted on 139 patients from Renmin Hospital of Wuhan University (RHWU; Wuhan, China).</p> Results <p>The DL model achieved area under the receiver operating characteristic curves of 0.79 (95% confidence interval [CI], 0.69–0.88) in the internal validation set for predicting LNM status, and 0.72 (95% CI, 0.68–0.75) in the external validation set. In multivariable Cox analysis, the model-predicted aiN score emerged as an independent predictor of survival for MIBC patients, with a hazard ratio of 1.608 (95% CI, 1.128–2.291; <i>p</i> = 0.008) in the TCGA cohort and 2.746 (95% CI, 1.486–5.076; <i>p</i> &lt; 0.001) in the RHWU cohort. Additionally, the aiN score maintained prognostic value across different subgroups.</p> Conclusion <p>In this study, DL-based image analysis showed promising results by directly extracting relevant prognostic information from H&amp;E-stained histology to predict the LNM status of MIBC patients. It might be used for personalized management of MIBC patients following prospective validation in the future.</p>

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Deep learning-based lymph node metastasis status predicts prognosis from muscle-invasive bladder cancer histopathology

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Qingyuan Zheng,
  • Panpan Jiao,
  • Rui Yang,
  • Junjie Fan,
  • Yunxun Liu,
  • Xiangxiang Yang,
  • Jingping Yuan,
  • Zhiyuan Chen,
  • Xiuheng Liu

摘要

Purpose

To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.

Methods

A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score. External validation was conducted on 139 patients from Renmin Hospital of Wuhan University (RHWU; Wuhan, China).

Results

The DL model achieved area under the receiver operating characteristic curves of 0.79 (95% confidence interval [CI], 0.69–0.88) in the internal validation set for predicting LNM status, and 0.72 (95% CI, 0.68–0.75) in the external validation set. In multivariable Cox analysis, the model-predicted aiN score emerged as an independent predictor of survival for MIBC patients, with a hazard ratio of 1.608 (95% CI, 1.128–2.291; p = 0.008) in the TCGA cohort and 2.746 (95% CI, 1.486–5.076; p < 0.001) in the RHWU cohort. Additionally, the aiN score maintained prognostic value across different subgroups.

Conclusion

In this study, DL-based image analysis showed promising results by directly extracting relevant prognostic information from H&E-stained histology to predict the LNM status of MIBC patients. It might be used for personalized management of MIBC patients following prospective validation in the future.