Background: Owing to the scarcity of upper urinary tract urothelial carcinoma (UUT-UC) it is often necessary for investigators to pool data. A patient-specific survival nomogram based on such data is needed to predict cancer-specific survival (CSS) post nephroureterectomy (NU). Herein, we propose and validate a nomogram to predict CSS post NU.Patients and Methods: Twenty-one French institutions contributed data on 1120 patients treated with NU for UUT-UC. A total of 667 had full data for nomogram development. Study population was divided into the nomogram development cohort (397) and external validation cohort (270). Cox proportional hazards regression models were used for univariate and multivariate analyses and to build a nomogram. A reduced model selection was performed using a backward step-down selection process, and Harrell's concordance index (c-index) was used for quantifying the nomogram accuracy. Internal validation was performed by bootstrapping and the reduced nomogram model was calibrated. Results :Of the 397 patients in the nomogram development cohort, 91 (22.9%) died during follow-up, of which 66 (72.5%) died as a consequence of UUT-UC. The actuarial CSS probability at 5 years was 0.76 (95% CI, 71.62-80.94). On multivariate analysis, T stage (P<0.0001), N status (P=0.014), grade (P=0.026), age (P=0.005) and location (P=0.022) were associated with CSS. The reduced nomogram model had an accuracy of 0.78. We propose a nomogram to predict 3 and 5-year CSS post NU for UUT-UC. Conclusion: We have devised and validated an accurate nomogram (78%), superior to any single clinical variable or current model, for predicting 5-year CSS post NU for UUT-UC. © 2012 Cancer Research UK All rights reserved.
CITATION STYLE
Yates, D. R., Hupertan, V., Colin, P., Ouzzane, A., Descazeaud, A., Long, J. A., … Rouprêt, M. (2012). Cancer-specific survival after radical nephroureterectomy for upper urinary tract urothelial carcinoma: Proposal and multi-institutional validation of a post-operative nomogram. British Journal of Cancer, 106(6), 1083–1088. https://doi.org/10.1038/bjc.2012.64
Mendeley helps you to discover research relevant for your work.