Translational andrology and urology · 2026
Long-term survival prognosis and risk stratification after radical cystectomy with Mainz Pouch II urinary diversion: a retrospective cohort study and prognostic model construction.
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Fan Lei, Mo Chunhao, Li Jiawei, Chen Chuanjian, Ning Zhongyun, Fan Ning, Ding Hui
Аннотация
BACKGROUND: Despite its clinical utility, specific long-term prognostic factors for radical cystectomy (RC) with Mainz Pouch II diversion remain unclear. We aimed to identify key overall survival (OS) predictors and construct an algorithmically optimized prognostic nomogram.
METHODS: We retrospectively analyzed 309 patients who underwent RC with Mainz Pouch II diversion (2004-2023). Regularized Elastic Net regression was evaluated against traditional Cox models and machine learning algorithms (e.g., XGBoost, random survival forest). Predictive performance was comprehensively validated via 10-fold cross-validation and 1,000-sample bootstrap resampling, evaluated by time-dependent area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS: Advanced age [hazard ratio (HR), 1.03; P=0.001], elevated lymph node density (LND) (HR 8.13, P=0.03), and advanced pathological T stage (T3: HR 2.75; T4: HR 3.94) emerged as significant independent prognostic factors. Binary lymph node metastasis showed only marginal significance (P=0.08), while other features (e.g., tumor grade, lymphovascular invasion, surgical margins) lacked independent predictive value. By mitigating the pronounced overfitting bias observed in complex nonlinear models, the Elastic Net model achieved the highest optimism-corrected Concordance Index (C-index) (0.712) with excellent 5-year calibration. The derived nomogram effectively stratified patients into low-, intermediate-, and high-risk groups with highly distinct survival trajectories (P<0.001).
CONCLUSIONS: Advanced age, elevated LND, and advanced T stage are the core drivers of long-term OS in this cohort. Built upon the optimal Elastic Net algorithm, the derived nomogram achieved a moderate optimism-corrected C-index (0.712). While it demonstrates risk stratification potential, its current utility is strictly exploratory, requiring rigorous external validation prior to any clinical application.
METHODS: We retrospectively analyzed 309 patients who underwent RC with Mainz Pouch II diversion (2004-2023). Regularized Elastic Net regression was evaluated against traditional Cox models and machine learning algorithms (e.g., XGBoost, random survival forest). Predictive performance was comprehensively validated via 10-fold cross-validation and 1,000-sample bootstrap resampling, evaluated by time-dependent area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS: Advanced age [hazard ratio (HR), 1.03; P=0.001], elevated lymph node density (LND) (HR 8.13, P=0.03), and advanced pathological T stage (T3: HR 2.75; T4: HR 3.94) emerged as significant independent prognostic factors. Binary lymph node metastasis showed only marginal significance (P=0.08), while other features (e.g., tumor grade, lymphovascular invasion, surgical margins) lacked independent predictive value. By mitigating the pronounced overfitting bias observed in complex nonlinear models, the Elastic Net model achieved the highest optimism-corrected Concordance Index (C-index) (0.712) with excellent 5-year calibration. The derived nomogram effectively stratified patients into low-, intermediate-, and high-risk groups with highly distinct survival trajectories (P<0.001).
CONCLUSIONS: Advanced age, elevated LND, and advanced T stage are the core drivers of long-term OS in this cohort. Built upon the optimal Elastic Net algorithm, the derived nomogram achieved a moderate optimism-corrected C-index (0.712). While it demonstrates risk stratification potential, its current utility is strictly exploratory, requiring rigorous external validation prior to any clinical application.