Abstract

BackgroundSite-specific postoperative risk models for localized upper tract urothelial carcinoma (UTUC) are unavailable. ObjectiveTo create specific risk models for renal pelvic urothelial carcinoma (RPUC) and ureteral urothelial carcinoma (UUC), and to compare the predictive accuracy with the overall UTUC risk model. Design, setting, and participantsA multi-institutional database retrospective study of 1917 UTUC patients who underwent radical nephroureterectomy (RNU) between 2000 and 2018 was conducted. Outcome measurements and statistical analysisA multivariate hazard model was used to identify the prognostic factors for extraurinary tract recurrence (EUTR), cancer-specific death (CSD), and intravesical recurrence (IVR) after RNU. Patients were stratified into low-, intermediate-, high-, and highest-risk groups. External validation was performed to estimate a concordance index of the created risk models. We investigated whether our risk models could aid decision-making regarding adjuvant chemotherapy (AC) after RNU. Results and limitationsThe UTUC risk models could stratify the risk of cumulative incidence of three endpoints. The RPUC- and UUC-specific risk models showed better stratification than the overall UTUC risk model for all the three endpoints, EUTR, CSD, and IVR (RPUC: concordance index, 0.719 vs 0.770, 0.714 vs 0.794, and 0.538 vs 0.569, respectively; UUC: 0.716 vs 0.767, 0.766 vs 0.809, and 0.553 vs 0.594, respectively). The UUC-specific risk model can identify the high- and highest-risk patients likely to benefit from AC after RNU. A major limitation was the potential selection bias owing to the retrospective nature of this study. ConclusionsWe recommend using site-specific risk models instead of the overall UTUC risk model for better risk stratification and decision-making for AC after RNU. Patient summaryUpper tract urothelial carcinoma comprises renal pelvic and ureteral carcinomas. We recommend using site-specific risk models instead of the overall upper tract urothelial carcinoma risk model in risk prediction and decision-making for adjuvant therapy after radical surgery.

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