BACKGROUND: Nephroureterectomy is the surgical standard of care for patients with upper urinary-tract urothelial carcinoma. The objectives of the current study were to identify the most informative predictors of cancer-specific mortality after nephroureterectomy, to devise an algorithm capable of predicting the individual probability of cancer-specific mortality, and to compare its prognostic accuracy to that of the International Union Against Cancer (UICC) staging system. METHODS: Within the Surveillance, Epidemiology, and End Results database, the authors identified 5918 patients who had been treated with nephroureterectomy. Within the development cohort (n=2959), multivariate Cox regression models predicting cancer-specific mortality were fitted by using age, stage, nodal status, sex, grade, race, type of surgery (nephroureterectomy with or without bladder-cuff removal), and tumor location (renal pelvis vs ureter). Backward variable elimination according to the Akaike information criterion identified the most accurate and parsimonious model. Model validation and calibration were performed within the external validation cohort (n=2959). External validation was also applied to the UICC staging system. RESULTS: The 5-year freedom from cancer-specific mortality rates in both the development and external validation cohorts was 77.3%. The most informative and parsimonious nomogram for cancer-specific-mortality-free survival relied on age, pT and pN stages, and tumor grade. In external validation, nomogram prediction of 5-year cancer-specific-mortality-free rate was 75.4% accurate and was significantly better (P<.001) than the UICC staging system (64.8%). CONCLUSIONS: The current nomogram is capable of predicting the prognosis in patients with upper urinary-tract urothelial carcinoma treated by nephroureterectomy with better accuracy than the UICC staging system. The authors recommend the application of this nomogram to routine clinical practice when counseling or making clinical decisions. Copyright (c) 2010 American Cancer Society.
BACKGROUND: Nephroureterectomy is the surgical standard of care for patients with upper urinary-tract urothelial carcinoma. The objectives of the current study were to identify the most informative predictors of cancer-specific mortality after nephroureterectomy, to devise an algorithm capable of predicting the individual probability of cancer-specific mortality, and to compare its prognostic accuracy to that of the International Union Against Cancer (UICC) staging system. METHODS: Within the Surveillance, Epidemiology, and End Results database, the authors identified 5918 patients who had been treated with nephroureterectomy. Within the development cohort (n=2959), multivariate Cox regression models predicting cancer-specific mortality were fitted by using age, stage, nodal status, sex, grade, race, type of surgery (nephroureterectomy with or without bladder-cuff removal), and tumor location (renal pelvis vs ureter). Backward variable elimination according to the Akaike information criterion identified the most accurate and parsimonious model. Model validation and calibration were performed within the external validation cohort (n=2959). External validation was also applied to the UICC staging system. RESULTS: The 5-year freedom from cancer-specific mortality rates in both the development and external validation cohorts was 77.3%. The most informative and parsimonious nomogram for cancer-specific-mortality-free survival relied on age, pT and pN stages, and tumor grade. In external validation, nomogram prediction of 5-year cancer-specific-mortality-free rate was 75.4% accurate and was significantly better (P<.001) than the UICC staging system (64.8%). CONCLUSIONS: The current nomogram is capable of predicting the prognosis in patients with upper urinary-tract urothelial carcinoma treated by nephroureterectomy with better accuracy than the UICC staging system. The authors recommend the application of this nomogram to routine clinical practice when counseling or making clinical decisions. Copyright (c) 2010 American Cancer Society.
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Authors: Ramy F Youssef; Laura-Maria Krabbe; Shahrokh F Shariat; Yair Lotan; Arthur I Sagalowsky; Jay Raman; Christopher G Wood; Alon Weizer; Marco Roscigno; Francesco Montorsi; Christian Bolenz; Mesut Remzi; Karim Bensalah; Wassim Kassouf; Vitaly Margulis Journal: World J Urol Date: 2015-05-10 Impact factor: 4.226
Authors: Aditya Bagrodia; Eugene K Cha; John P Sfakianos; Emily C Zabor; Bernard H Bochner; Hikmat A Al-Ahmadie; David B Solit; Jonathan A Coleman; Gopa Iyer; Sasinya N Scott; Ronak Shah; Irina Ostrovnaya; Byron Lee; Neil B Desai; Qinghu Ren; Jonathan E Rosenberg; Guido Dalbagni; Dean F Bajorin; Victor E Reuter; Michael F Berger Journal: J Urol Date: 2016-01-14 Impact factor: 7.450