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Background <p>To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).</p> Methods <p>This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology.</p> Results <p>On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745–0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall.</p> Conclusions <p>The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.</p>

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A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月10日

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  • Xiaomeng Han,
  • Jing Guan,
  • Li Guo,
  • Qiyan Jiao,
  • Kexin Wang,
  • Feng Hou,
  • Shunli Liu,
  • Shifeng Yang,
  • Chencui Huang,
  • Wenbin Cong,
  • Hexiang Wang

摘要

Background

To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).

Methods

This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology.

Results

On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745–0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall.

Conclusions

The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.