Development and External Validation of Machine Learning Models to Predict Postoperative Transitional Cell Carcinoma Mortality: A Retrospective Study

Purpose Transitional cell carcinoma (TCC) of the bladder, also known as urothelial carcinoma, is the most common type of bladder cancer. The primary objective of this study was to develop machine learning (ML) models to predict the survival outcomes of bladder cancer patients. Methods This study utilized the SEER dataset to develop an ML model for predicting postoperative disease-specific overall survival in TCC patients. This retrospective study included patients diagnosed between 2010 and 2015 for model training and validation and patients diagnosed after 2018 for external validation. The performance was assessed via sensitivity, specificity, accuracy, likelihood ratios, predictive values, ROC-AUC, and the Brier score. SHAP values and partial dependence plots were used to evaluate feature importance and interactions. Results The deep neural network model slightly outperformed the other models (linear regression and gradient boosting) across all the datasets. For the external validation dataset, the model had a sensitivity of 69.40% (95% CI: 67.72–71.05%), specificity of 85.32% (95% CI: 84.91–85.72%), and accuracy of 83.84% (95% CI: 83.43–84.24%). The ROC-AUC was 0.8758 (95% CI: 0.8698 to 0.8816). The model demonstrated a negative likelihood ratio of 0.36 (95% CI: 0.34 to 0.38) and a positive likelihood ratio of 4.73 (95% CI: 4.56 to 4.90). The use of external validation indicated minimal overfitting. The key features influencing mortality included tumor stage, age, tumor size, and grade. Conclusion Machine learning models, particularly deep neural networks, can accurately predict mortality in individuals with TCC of the bladder.