Development and multicohort validation of an interpretable postoperative model for intravesical recurrence after radical nephroureterectomy in upper tract urothelial carcinoma

Intravesical recurrence (IVR) after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC) is frequent, but postoperative recurrence risk is heterogeneous. We developed and multicohort-validated an interpretable postoperative model for IVR risk stratification after RNU.

We included 813 patients with pathologically confirmed UTUC treated with RNU across four predefined cohorts: retrospective development (n = 400), retrospective external validation (n = 173), internal prospective validation (n = 166), and external prospective validation (n = 74). Missing data were handled using cohort-specific multiple imputation. A Cox model integrating clinicopathological factors and neutrophil-to-lymphocyte ratio (NLR) was developed and locked in the development cohort, then transported unchanged to validation cohorts. Bootstrap confidence intervals, competing-risk analyses, benchmark comparisons, and sensitivity analyses were performed.

The locked Cox + NLR model retained tumor location, ureteroscopic manipulation, hydronephrosis, pathological stage, surgical margin status, lymphovascular/perineural invasion, history of bladder cancer, and NLR. Harrell’s C-indices were 0.709, 0.746, 0.868, and 0.811 across the four cohorts. At the primary 12-month horizon, AUCs were 0.739, 0.771, 0.953, and 0.791, respectively. Low-risk and high-risk groups remained separated under the competing-risk framework. More complex survival machine-learning models did not show a consistent transportability advantage. The 24-month estimates and surveillance simulation were considered exploratory because prospective follow-up was limited.

This interpretable postoperative Cox + NLR model showed favorable multicohort performance for IVR risk stratification after RNU. It may support postoperative risk assessment, but prospective implementation studies are needed before surveillance schedules are changed.