Predicting clinical outcomes after radical nephroureterectomy for upper tract urothelial carcinoma.

Abstract

267 Background: Novel prognostic factors for patients after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC) have recently been described. We tested the prognostic value of pathologic characteristics and developed models to predict the individual probabilities of recurrence-free survival (RFS) and cancer-specific survival (CSS) after RNU. Methods: Our study included 2,244 patients treated with RNU without neoadjuvant or adjuvant therapy at 23 international institutions. Tumor characteristics included T classification, grade, lymph node status, lymphovascular invasion, tumor architecture, location, and concomitant CIS. The cohort was split for development (12 centers, n=1273) and external validation (11 centers, n=971). Results: At a median follow-up of 45 months, 501 patients (22.3%) experienced disease recurrence and 418 patients (18.6%) died of UTUC. On multivariable analysis, T classification (p-for-trend <0.001), lymph node metastasis (HR:1.98, p=0.002), lymphovascular invasion (HR:1.66, p<0.001), sessile tumor architecture (HR:1.76, p<0.001), and concomitant CIS (HR:1.33, p=0.035) were associated with disease recurrence. Similarly, T classification (p-for-trend <0.001), lymph node metastasis (HR:2.23, p=0.001), lymphovascular invasion (HR:1.81, p<0.001), and sessile tumor architecture (HR:1.72, p=0.001) were independently associated with cancer-specific mortality. Our models achieved 76.8% and 81.5% accuracy for predicting RFS and CSS, respectively. In contrast to these well-calibrated models, stratification based upon AJCC stage grouping resulted in a large degree of heterogeneity and did not improve discrimination. Conclusions: Using standard pathologic features, we developed highly accurate prognostic models for the prediction of RFS and CSS after RNU for UTUC. These models offer improvements in calibration over AJCC stage grouping and can be utilized for individualized patient counseling, follow-up scheduling, risk stratification for adjuvant therapies, and inclusion criteria for clinical trials.