OBJECTIVE: To predict overall survival, cancer, and metastasis specific survival in upper tract urothelial carcinoma (UTUC) following radical nephroureterectomy (RNU). MATERIALS AND METHODS: All nonmetastatic UTUC patients who underwent RNU with a curative intent at 1 institution between December 1998 and January 2017 were included. Detailed data were collected. End points for this study included OS, CCS, and MFS. Univariate and multivariate analysis were conducted. Log Rank tests and Kaplan-Meier curves were generated. Backward elimination and boot strapping was used to identify the most parsimonious model with the smallest number of variables in order to predict the outcomes of interest. A separate second institution data base was used for external validation. RESULTS: There were 218 patients in the development cohort. Mean follow-up was 42 months (±39.6). There was 99 (45.4%) deaths, 28 (12.8%) cancer related deaths, 72 (33%) recurrences, and 54 (24.8%) metastases. The c-index for our model was 0.71 for OS, 0.72 for MFS and 0.74 for CSS. The nomograms did not show significant deviation from actual observations using our calibration plots. We divided the patient into 3 different groups (low, intermediate and high risk) based on their final total score for each outcome and compared them. On external validation our accuracy was 78.4%, 71.4%, and 75.3% for OS, CSS, and MFS survival respectively. CONCLUSION: We designed a predictive model for survival outcomes following RNU in UTUC. This model uses simple, readily available data for patients without the need for expensive or additional testing.
OBJECTIVE: To predict overall survival, cancer, and metastasis specific survival in upper tract urothelial carcinoma (UTUC) following radical nephroureterectomy (RNU). MATERIALS AND METHODS: All nonmetastatic UTUC patients who underwent RNU with a curative intent at 1 institution between December 1998 and January 2017 were included. Detailed data were collected. End points for this study included OS, CCS, and MFS. Univariate and multivariate analysis were conducted. Log Rank tests and Kaplan-Meier curves were generated. Backward elimination and boot strapping was used to identify the most parsimonious model with the smallest number of variables in order to predict the outcomes of interest. A separate second institution data base was used for external validation. RESULTS: There were 218 patients in the development cohort. Mean follow-up was 42 months (±39.6). There was 99 (45.4%) deaths, 28 (12.8%) cancer related deaths, 72 (33%) recurrences, and 54 (24.8%) metastases. The c-index for our model was 0.71 for OS, 0.72 for MFS and 0.74 for CSS. The nomograms did not show significant deviation from actual observations using our calibration plots. We divided the patient into 3 different groups (low, intermediate and high risk) based on their final total score for each outcome and compared them. On external validation our accuracy was 78.4%, 71.4%, and 75.3% for OS, CSS, and MFS survival respectively. CONCLUSION: We designed a predictive model for survival outcomes following RNU in UTUC. This model uses simple, readily available data for patients without the need for expensive or additional testing.
Authors: Francesco Soria; B Pradere; R Hurle; D D'Andrea; S Albisinni; R Diamand; E Laukhtina; E Di Trapani; A Aziz; W Krajewski; J Y Teoh; A Mari; M Moschini; F Chiancone; R Autorino; A Porreca; M Marchioni; G Liguori; G Lucarelli; G M Busetto; N Foschi; A Antonelli; P Bove; G I Russo; N Crisan; M Borghesi; L Boeri; A Veccia; F Greco; N Longo; O De Cobelli; S F Shariat; P Gontero; M Ferro Journal: Eur Urol Open Sci Date: 2022-06-13
Authors: Maximilian Pallauf; Frederik König; David D'Andrea; Ekaterina Laukhtina; Hadi Mostafaei; Reza Sari Motlagh; Fahad Quhal; Abdulmajeed Aydh; Takafumi Yanagisawa; Tatsushi Kawada; Pawel Rajwa; Lukas Lusuardi; Francesco Soria; Pierre I Karakiewicz; Morgan Rouprêt; Michael Rink; Yair Lotan; Vitaly Margulis; Nirmish Singla; Evanguelos Xylinas; Shahrokh F Shariat; Benjamin Pradere Journal: Front Oncol Date: 2022-07-01 Impact factor: 5.738