Prognostic Factors for Urachal Cancer: A Bayesian Model-Averaging Approach

PURPOSE This study was conducted to evaluate prognostic factors and cancer-specific survival (CSS) in a cohort of 41 patients with urachal carcinoma by use of a Bayesian model-averaging approach. MATERIALS AND METHODS Our cohort included 41 patients with urachal carcinoma who underwent extended partial cystectomy, total cystectomy, transurethral resection, chemotherapy, or radiotherapy at a single institute. All patients were classified by both the Sheldon and the Mayo staging systems according to histopathologic reports and preoperative radiologic findings. Kaplan-Meier survival curves and Cox proportional-hazards regression models were carried out to investigate prognostic factors, and a Bayesian model-averaging approach was performed to confirm the significance of each variable by using posterior probabilities. RESULTS The mean age of the patients was 49.88 ± 13.80 years and the male-to-female ratio was 24:17. The median follow-up was 5.42 years (interquartile range, 2.8-8.4 years). Five- and 10-year CSS rates were 55.9% and 43.4%, respectively. Lower Sheldon (p=0.004) and Mayo (p<0.001) stage, mucinous adenocarcinoma (p=0.005), and larger tumor size (p=0.023) were significant predictors of high survival probability on the basis of a log-rank test. By use of the Bayesian model-averaging approach, higher Mayo stage and larger tumor size were significant predictors of cancer-specific mortality in urachal carcinoma. CONCLUSIONS The Mayo staging system might be more effective than the Sheldon staging system. In addition, the multivariate analyses suggested that tumor size may be a prognostic factor for urachal carcinoma.