PD51-09 THE CANCER OF THE BLADDER RISK ASSESSMENT (COBRA) SCORE FOR ESTIMATING CANCER-SPECIFIC SURVIVAL AFTER RADICAL CYSTECTOMY

(1) Background: This study aimed to evaluate the associations of lymphovascular invasion (LVI) at first transurethral resection of bladder (TURBT) and radical cystectomy (RC) with survival outcomes, and to evaluate the concordance between LVI at first TURBT and RC. (2) Methods: We analyzed 216 patients who underwent first TURBT and 64 patients who underwent RC at Toho University Sakura Medical Center. (3) Results: LVI was identified in 22.7% of patients who underwent first TURBT, and in 32.8% of patients who underwent RC. Univariate analysis identified ≥cT3, metastasis and LVI at first TURBT as factors significantly associated with overall survival (OS) and cancer-specific survival (CSS). Multivariate analysis identified metastasis (hazard ratio (HR) 6.560, p = 0.009) and LVI at first TURBT (HR 9.205, p = 0.003) as significant predictors of CSS. On the other hand, in patients who underwent RC, ≥pT3, presence of G3 and LVI was significantly associated with OS and CSS in univariate analysis. Multivariate analysis identified inclusion of G3 as a significant predictor of OS and CSS. The concordance rate between LVI at first TURBT and RC was 48.0%. Patients with positive results for LVI at first TURBT and RC displayed poorer prognosis than other patients (p < 0.05). (4) Conclusions: We found that the combination of LVI at first TURBT and RC was likely to provide a more significant prognostic factor.

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866 ASSOCIATION BETWEEN THE EXTENT OF PELVIC LYMPHADENECTOMY AND CANCER-SPECIFIC SURVIVAL IN PATIENTS WITH LYMPH NODE NEGATIVE UROTHELIAL CARCINOMA OF THE BLADDER UNDERGOING RADICAL CYSTECTOMY

European Urology Supplements ◽

10.1016/s1569-9056(11)60852-x ◽

2011 ◽

Vol 10 (2) ◽

pp. 273

Author(s):

P.J. Bastian ◽

M. May ◽

E. Hermann ◽

A. Buchner ◽

C. Bolenz ◽

...

Keyword(s):

Lymph Node ◽

Urothelial Carcinoma ◽

Radical Cystectomy ◽

Pelvic Lymphadenectomy ◽

Cancer Specific Survival ◽

Node Negative ◽

Carcinoma Of The Bladder

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Preoperative Serum Gamma-Glutamyltransferase as a Prognostic Biomarker in Patients Undergoing Radical Cystectomy for Bladder Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.648904 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shiqiang Su ◽

Lizhe Liu ◽

Chao Sun ◽

Yanhua Nie ◽

Hong Guo ◽

...

Keyword(s):

Bladder Cancer ◽

Radical Cystectomy ◽

Elevated Serum ◽

Disease Free Survival ◽

Prognostic Models ◽

Lymph Node Status ◽

Cancer Specific Survival ◽

Gamma Glutamyltransferase ◽

Preoperative Serum

BackgroundSerum gamma-glutamyltransferase (GGT) has been reported to be correlated with survival in a variety of malignancies. However, its effect on patients with bladder cancer (BC) treated by radical cystectomy has never been evaluated.Patients and MethodsWe retrospectively evaluated 263 patients who underwent radical surgery in our center. Baseline features, hematologic variables, and follow-up data were obtained. The endpoints included overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). The relationship between GGT and survival were evaluated.ResultsThe median follow-up period for all patients was 34.7 (22.9-45.9) months. At the last follow-up, 67 patients died, 51 patients died of cancer, 92 patients experienced disease recurrence. Patients with an elevated serum GGT had a higher rate of pT3-T4 tumors. Patients with a higher preoperative serum GGT had a lower rate of OS, CSS and DFS (P < 0.001 for all). Multivariate analysis identified that preoperative serum GGT was independent predictor of OS (HR: 3.027, 95% CI: 1.716-5.338; P < 0.001), CSS (HR: 2.115, 95% CI: 1.093-4.090; P = 0.026), DFS (HR: 2.584, 95% CI: 1.569-4.255; P < 0.001). Age, diabetes history, pathologic T stage, and lymph node status also were independent predictors of prognosis for BC patients.ConclusionsOur results indicated that preoperative serum GGT was an independent prognosis predictor for survival of BC patients after radical cystectomy, and can be included in the prognostic models.

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