Oncological and functional outcomes of robot-assisted radical cystectomy in bladder cancer patients in a single tertiary center: Can these be preserved throughout the learning curve?

在单一三级中心,机器人辅助根治性膀胱切除术治疗膀胱癌患者的肿瘤学和功能性结果:这些结果能否在学习曲线中得以保持?

阅读:1

Abstract

PURPOSE: To evaluate the overall and segmental oncological and functional outcome of robot-assisted radical cystectomy (RARC) during the learning curve. MATERIALS AND METHODS: From August 2007 to November 2017, a total of 120 bladder cancer patients were treated with RARC in a single-tertiary hospital. These were divided into three groups of 40 consecutive cases. Overall and subgroup analysis of each group was used to evaluate oncological and functional outcomes throughout the learning curve. RESULTS: Among the 120 RARC cases, 42, 73, and 5 patients received extracorporeal urinary diversion (ECUD), intracorporeal urinary diversion (ICUD), and ureterocutaneostomy, respectively. There was a transition from ECUD to ICUD during the learning curve. The positive surgical margin rate was 0.8%. The mean lymph node yield for the standard and extended pelvic lymph node dissection was 12.5 and 30.1, respectively, and increased to 19.8 and 31.2 and further to 20.0 and 37.9, respectively, with each additional series of 40 cases. The 5-year overall survival and 3-year recurrence-free survival rates were 86.6% and 81.4%, respectively. The 1-year daytime continence rate was 75.7%, while the nighttime continence rate was 51.4%. The potency preservation rate was 66.7% (n=8) with or without phosphodiesterase-5 inhibitors (PDE5-I) at 1 year and 33.3% without PDE5-I (n=4). CONCLUSIONS: RARC results in comparable oncological and functional outcomes to open radical cystectomy. In addition, the oncological and functional outcomes were well maintained throughout the learning curve. ECUD transition to ICUD was safe and did not compromise oncological or functional outcome.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。