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Objective <p>This study aims to construct a survival prognosis prediction model for muscle-invasive bladder cancer based on CT imaging features.</p> Materials and methods <p>A total of 91 patients with muscle-invasive bladder cancer were sourced from the TCGA and TCIA dataset and were divided into a training group (64 cases) and a validation group (27 cases). Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. The model’s performance was ultimately assessed using various evaluation methods, including the ROC curve, calibration curve, decision curve, and Kaplan-Meier (KM) analysis.</p> Results <p>Eight radiomic features were identified for modeling analysis. Among the models evaluated, the Gradient Boosting Machine (GBM) In the prediction of OS performed the best. the 2-year AUCs were 0.859, 95% CI (0.767–0.952) for the training group, 0.850, 95% CI (0.705–0.995) for the validation group, and 0.700, 95% CI (0.520–0.880) for the external test group. The 3-year AUCs were 0.809, 95% CI (0.704–0.913) for the training group, 0.895, 95% CI (0.768-1.000) for the validation group, and 0.730, 95% CI (0.569–0.891) for the external test group. The nomogram model incorporating clinical data achieved superior results, the AUCs for predicting 2-year OS were 0.913 (95% CI: 0.83–0.98) for the training group, 0.86 (95% CI: 0.78–0.96) for the validation group, and 0.778 (95% CI: 0.69–0.94) for the external test group; for predicting 3-year OS, the AUCs were 0.837 (95% CI: 0.83–0.98) for the training group, 0.982 (95% CI: 0.84-1.0) for the validation group, and 0.785 (95% CI: 0.75–0.96) for the external test group. The calibration curve demonstrated excellent calibration of the model, while the decision curve and KM analysis indicated that the model possesses substantial clinical utility.</p> Conclusion <p>The GBM model, based on the radiomic features of enhanced CT imaging, holds significant potential for predicting the prognosis of patients with muscle-invasive bladder cancer. Furthermore, the combined model, which incorporates clinical features, demonstrates enhanced performance and is beneficial for clinical decision-making.</p>

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Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Bin Wang,
  • Zijian Gong,
  • Peide Su,
  • Guanghao Zhen,
  • Tao Zeng,
  • Yinquan Ye

摘要

Objective

This study aims to construct a survival prognosis prediction model for muscle-invasive bladder cancer based on CT imaging features.

Materials and methods

A total of 91 patients with muscle-invasive bladder cancer were sourced from the TCGA and TCIA dataset and were divided into a training group (64 cases) and a validation group (27 cases). Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. The model’s performance was ultimately assessed using various evaluation methods, including the ROC curve, calibration curve, decision curve, and Kaplan-Meier (KM) analysis.

Results

Eight radiomic features were identified for modeling analysis. Among the models evaluated, the Gradient Boosting Machine (GBM) In the prediction of OS performed the best. the 2-year AUCs were 0.859, 95% CI (0.767–0.952) for the training group, 0.850, 95% CI (0.705–0.995) for the validation group, and 0.700, 95% CI (0.520–0.880) for the external test group. The 3-year AUCs were 0.809, 95% CI (0.704–0.913) for the training group, 0.895, 95% CI (0.768-1.000) for the validation group, and 0.730, 95% CI (0.569–0.891) for the external test group. The nomogram model incorporating clinical data achieved superior results, the AUCs for predicting 2-year OS were 0.913 (95% CI: 0.83–0.98) for the training group, 0.86 (95% CI: 0.78–0.96) for the validation group, and 0.778 (95% CI: 0.69–0.94) for the external test group; for predicting 3-year OS, the AUCs were 0.837 (95% CI: 0.83–0.98) for the training group, 0.982 (95% CI: 0.84-1.0) for the validation group, and 0.785 (95% CI: 0.75–0.96) for the external test group. The calibration curve demonstrated excellent calibration of the model, while the decision curve and KM analysis indicated that the model possesses substantial clinical utility.

Conclusion

The GBM model, based on the radiomic features of enhanced CT imaging, holds significant potential for predicting the prognosis of patients with muscle-invasive bladder cancer. Furthermore, the combined model, which incorporates clinical features, demonstrates enhanced performance and is beneficial for clinical decision-making.