A novel risk stratification to predict local-regional failures in urothelial carcinoma of the bladder after radical cystectomy.

Abstract

262 Background: Invasive urothelial bladder carcinoma is typically treated with radical cystectomy (RC) plus pelvic lymph node dissection (PLND) +/− chemotherapy. Local-regional failures (LF) following cystectomy are a significant problem. Adjuvant radiation therapy (RT) could potentially reduce LF but currently has no defined role because of previously reported morbidity. Modern RT techniques with improved normal tissue sparing have rekindled interest in adjuvant RT. Stratifying patients by differing LF risk could facilitate selection for adjuvant RT. Methods: From 1990–2008, 442 patients with urothelial bladder carcinoma were prospectively followed at the University of Pennsylvania after RC+PLND +/− chemotherapy with routine pelvic CT or MRI. Univariate and multivariate competing risk analyses identified subgroups with differing LF risk. Results: On univariate analysis, stage pT3-4, total nodes removed (<10 vs. ≥10), positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology were significant predictors of LF, while use of chemotherapy, number of positive nodes, surgical diversion type, age, gender, race, smoking history and BMI were not. Node density was a marginal predictor of LF. On multivariate analysis, only stage ≥pT3-4 and nodes removed (<10) were significant independent predictors of LF with hazard ratios of 3.17 and 2.37 respectively (p<0.01). Analysis identified 3 patient subgroups with significantly different LF risk: low-risk (pT0-2), intermediate-risk (pT3-4, ≥10 nodes), and high-risk (pT3-4, <10 nodes) with 5-year LF rates of 8%, 23%, and 42% (p <0.01). Conclusions: This study of local-regional recurrence risk factors after RC is based on the largest reported, prospectively maintained patient database with routine follow-up pelvic CT surveillance. LF after RC varies significantly among different subgroups. This risk stratification model could facilitate selection for future adjuvant radiotherapy trials.