Development and external validation of a novel nomogram to predict intravesical recurrence after radical nephroureterectomy: a multicenter study

ObjectiveThis study aimed to establish and validate nomograms to predict the probability of intravesical recurrence (IVR) after radical nephroureterectomy (RNU) for upper urinary tract epithelial carcinoma (UTUC).MethodsClinical data of 528 patients with UTUC after RNU were collected from two medical centers between 2009 and 2020. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables for multivariable Cox regression analysis in the training cohort and included independent risk factors into nomogram models predicting IVR-free survival (IVRFS). Another center was applied as the external cohort to validate the predictive accuracy and discriminative ability of the nomogram by performing area under the receiver operating curve (AUC), consistency index (C-index), and calibration curve.ResultsHistory of bladder cancer, tumor size, preoperative urine cytology, postoperative instillation, Ki-67, and platelet-to-lymphocyte ratio (PLR) were identified as independent risk factors for IVR. The prognosis model including these predictors demonstrated excellent discriminatory performance in both the training cohort (C-index, 0.814) and external validation cohort (C-index, 0.748). The calibration plots of the nomogram revealed good consistency in both cohorts. Finally, patients could be classified into two risk groups based on scores obtained from the nomogram, with significant differences in IVRFS.ConclusionOur study provided a reliable nomogram for predicting the probability of IVR in patients with UTUC after RNU. Risk stratification based on this model may assist urologists make optimal clinical decisions on the management of UTUC.

Automated summary

This study aimed to establish and validate nomograms to predict the probability of intravesical recurrence after radical nephroureterectomy for upper urinary tract epithelial carcinoma. Clinical data of 528 patients were collected and analyzed using LASSO regression and Cox regression analysis to build a predictive nomogram model. The model demonstrated excellent discriminatory performance and was validated in an external cohort.