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Background and purpose <p>Despite the higher incidence of bladder cancer in males, females face a disproportionately worse prognosis with more advanced disease. Accordingly, accurate survival prediction is crucial. This study sought to develop multiple machine learning models for predicting postoperative overall survival (OS) in female bladder cancer patients who have undergone radical cystectomy (RC).</p> Patients and methods <p>A retrospective analysis was conducted on female patients who underwent RC with postoperative pathological confirmation of bladder cancer in the SEER database from 2004 to 2022. These patients were randomly divided into a training set and an internal validation set at a 7:3 ratio. Additionally, 67 female bladder cancer patients from the Second Affiliated Hospital of Nanchang University were included as an external validation set. LASSO-Cox regression and Cox regression were used to identify independent prognostic factors for bladder cancer. Based on these factors, prediction models were constructed using five machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and XGBoost. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, F1 score, concordance index (C-index), calibration curves, and decision curve analysis. The SHapley Additive exPlanations (SHAP) method was employed to interpret the most impactful features of the best-performing model.</p> Results <p>The study included a total of 4,603 patients. Lasso-Cox regression screening and Cox regression analysis revealed that T stage, N stage, age, tumor size, marital status, chemotherapy, and the number of examined lymph nodes (ELN) during surgery were significantly associated with the OS of female bladder cancer patients. Compared with other models, GBDT demonstrated superior discriminative ability in predicting 1-year, 3-year, and 5-year survival rates (1-year AUC(95%CI) = 0.771(0.742–0.801), 3-year AUC(95%CI) = 0.757(0.730–0.783), 5-year AUC(95%CI) = 0.745(0.721–0.770)), along with higher prediction accuracy, precision, C-index and F1 score. The calibration curve and decision curve analysis (DCA) confirmed the excellent predictive accuracy and clinical benefits of the GBDT model. These results were also validated in the external validation cohort. T stage, N stage, and chemotherapy were the most significant features of the optimal model, and SHAP analysis identified their important contributions within the model.</p> Conclusion <p>We have developed interpretable machine learning models to predict the OS of female bladder cancer patients following radical surgery. This model is intended to assist in clinical prognosis evaluation and provide a reference for individualized treatment decision-making.</p>

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Explainable machine learning predicts overall survival in female bladder cancer patients after radical cystectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Ming Yan Zhong,
  • Xin Chang Zou,
  • Pei Huang Chen

摘要

Background and purpose

Despite the higher incidence of bladder cancer in males, females face a disproportionately worse prognosis with more advanced disease. Accordingly, accurate survival prediction is crucial. This study sought to develop multiple machine learning models for predicting postoperative overall survival (OS) in female bladder cancer patients who have undergone radical cystectomy (RC).

Patients and methods

A retrospective analysis was conducted on female patients who underwent RC with postoperative pathological confirmation of bladder cancer in the SEER database from 2004 to 2022. These patients were randomly divided into a training set and an internal validation set at a 7:3 ratio. Additionally, 67 female bladder cancer patients from the Second Affiliated Hospital of Nanchang University were included as an external validation set. LASSO-Cox regression and Cox regression were used to identify independent prognostic factors for bladder cancer. Based on these factors, prediction models were constructed using five machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and XGBoost. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, F1 score, concordance index (C-index), calibration curves, and decision curve analysis. The SHapley Additive exPlanations (SHAP) method was employed to interpret the most impactful features of the best-performing model.

Results

The study included a total of 4,603 patients. Lasso-Cox regression screening and Cox regression analysis revealed that T stage, N stage, age, tumor size, marital status, chemotherapy, and the number of examined lymph nodes (ELN) during surgery were significantly associated with the OS of female bladder cancer patients. Compared with other models, GBDT demonstrated superior discriminative ability in predicting 1-year, 3-year, and 5-year survival rates (1-year AUC(95%CI) = 0.771(0.742–0.801), 3-year AUC(95%CI) = 0.757(0.730–0.783), 5-year AUC(95%CI) = 0.745(0.721–0.770)), along with higher prediction accuracy, precision, C-index and F1 score. The calibration curve and decision curve analysis (DCA) confirmed the excellent predictive accuracy and clinical benefits of the GBDT model. These results were also validated in the external validation cohort. T stage, N stage, and chemotherapy were the most significant features of the optimal model, and SHAP analysis identified their important contributions within the model.

Conclusion

We have developed interpretable machine learning models to predict the OS of female bladder cancer patients following radical surgery. This model is intended to assist in clinical prognosis evaluation and provide a reference for individualized treatment decision-making.