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

BACKGROUND

Novel prognostic factors for patients after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC) have recently been described.

OBJECTIVE

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.

DESIGN, SETTING, AND PARTICIPANTS: Our study included 2244 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 carcinoma in situ (CIS). The cohort was randomly split for development (12 centers, n=1273) and external validation (11 centers, n=971).

INTERVENTIONS

All patients underwent RNU.

MEASUREMENTS

Univariable and multivariable models addressed RFS, CSS, and comparison of discrimination and calibration with American Joint Committee on Cancer (AJCC) stage grouping.

RESULTS AND LIMITATIONS

At a median follow-up of 45 mo, 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 (hazard ratio [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 used for individualized patient counseling, follow-up scheduling, risk stratification for adjuvant therapies, and inclusion criteria for clinical trials.