PURPOSE: While multiple independent clinicopathologic variables have been associated with outcome following radical cystectomy (RC) for bladder cancer (BC), limited prediction tools exist to facilitate an individualized risk assessment.
Herein, we developed a prediction model for bladder cancer specific survival (CSS) after RC.
MATERIALS AND METHODS: We evaluated 2403 patients who underwent RC without neoadjuvant therapy at our institution between 1980-2008 with pathologic re-review of all specimens. Of these, 1,776 with non-metastatic urothelial carcinoma were identified for analysis. A multivariate model was developed using stepwise selection to determine variables associated with CSS. A scoring system based on the β-coefficients of this model was created.
RESULTS: Median follow-up after RC for patients alive at last follow-up was 10.5 years (IQR 7.3, 15.3), during which time 610 patients died from BC. In addition to pathologic tumor stage, nodal status, multifocality, and lymphovascular invasion, patient specific factors of Charlson comorbidity index, ECOG performance status, current smoking, preoperative hydronephrosis, and receipt of adjuvant chemotherapy were significantly associated with the risk of BC death. Cumulative scores from these variables were used to stratify patients into risk groups with a 5-year CSS from the lowest to highest risk group of 95%, 80%, 60%, 38%, and 23%, respectively (p< 0.0001). The c-index for this model was 0.75.
CONCLUSION: We present a model for individualizing estimation of CSS following RC. Pending external validation, these data may be used for patient counseling, specifically with regard to recommendations for adjuvant therapy and surveillance frequency, as well as in clinical trial development.
Written by:
Eisenberg MS, Boorjian SA, Cheville JC, Thompson RH, Thapa P, Kaushik D, Frank I. Are you the author?
Department of Urology, Mayo Clinic, Rochester, Minnesota.
Reference: J Urol. 2013 Jun 13. pii: S0022-5347(13)04610-7.
doi: 10.1016/j.juro.2013.06.022
PubMed Abstract
PMID: 23770147
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