You have accessJournal of UrologyBladder Cancer: Non-invasive III (MP72)1 Apr 2020MP72-07 VALIDATION AND MODIFICATION OF THE EORTC RISK TABLES TO PREDICT OUTCOMES IN NON-MUSCLE-INVASIVE BLADDER CANCER IN JAPANESE PATIENTS Hirotake Kodama*, Shingo Hatakeyama, Naoki Fujita, Hayato Yamamoto, Atsushi Imai, Takahiro Yoneyama, Yasuhiro Hashimoto, Kazuaki Yoshikawa, Atsushi Sasaki, and Chikara Ohyama Hirotake Kodama*Hirotake Kodama* More articles by this author , Shingo HatakeyamaShingo Hatakeyama More articles by this author , Naoki FujitaNaoki Fujita More articles by this author , Hayato YamamotoHayato Yamamoto More articles by this author , Atsushi ImaiAtsushi Imai More articles by this author , Takahiro YoneyamaTakahiro Yoneyama More articles by this author , Yasuhiro HashimotoYasuhiro Hashimoto More articles by this author , Kazuaki YoshikawaKazuaki Yoshikawa More articles by this author , Atsushi SasakiAtsushi Sasaki More articles by this author , and Chikara OhyamaChikara Ohyama More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000952.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To validate the stratification of the European Organization for Research and Treatment of Cancer (EORTC) risk table and to develop the simplified stratification model to improve usefulness and predictive accuracy on oncological outcomes in patients with non-muscle-invasive bladder cancer (NMIBC) who underwent transurethral resection of bladder tumor (TURBT). METHODS: We retrospectively evaluated 433 patients with primary NMIBC who underwent TURBT at from November 1993 to February 2019. We validated the usefulness of EORTC risk table on a risk classification and intravesical recurrence-free survival (RFS). we developed modified stratification based on multivariate analysis for intravesical RFS (lower- and higher-risk). We compared predictive accuracy on oncological outcomes using the receiver operating characteristic curve (ROC) and the area under the curve (AUC) between the EORTC model and the modified model. Then, we validated the modified model using 73 patients with NMIBC who underwent TURBT. RESULTS: Median age and median follow-up periods were 74 years and 29 months. Of 433 patients, 158 (36%) had recurrence after TURBT. A significant difference was observed in number of tumors (multiple) and renal function between the patients with and without recurrences. Of 433, 389 (89%) patients were classified in the high risk group. To improve the risk classification, we developed a modified risk model using number of tumors (multiple), tumor size (> 3cm), category (pT1), high grade, concurrent CIS, and the chronic kidney disease. Each factor was scored as 1 point and patients were classified into low (0-3) and high (4-6) according to total score (0-6). The AUC for recurrence prediction accuracy was significantly higher in the modified model than that in the EORTC model (0.62 vs. 0.57, P = 0.013, respectively). The recurrence free survival was significantly shorter in the patients with modified-high than that in the -low (P = 0.043). In the validation cohort, intravesical PFS was significantly shorter in the patients with the modified-high than that in the -low (P = 0.005) with the c-index of 0.76 (95% CI: 0.634-0.904). CONCLUSIONS: The modified model might be useful to classify the risk of recurrence of bladder cancer than the EORTC risk model. Further large study is necessary to validate our findings. Source of Funding: none © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1076-e1076 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hirotake Kodama* More articles by this author Shingo Hatakeyama More articles by this author Naoki Fujita More articles by this author Hayato Yamamoto More articles by this author Atsushi Imai More articles by this author Takahiro Yoneyama More articles by this author Yasuhiro Hashimoto More articles by this author Kazuaki Yoshikawa More articles by this author Atsushi Sasaki More articles by this author Chikara Ohyama More articles by this author Expand All Advertisement PDF downloadLoading ...