RESUMO
Introduction Nowadays, the expectations for functional prostatectomy outcomes are quite high. Robot-assisted laparoscopic radical prostatectomy (RALRP) has become an increasingly common treatment option for men with localized prostate cancer. In this study, we aimed to present the results of our bilateral nerve-sparing RALRP procedure and to evaluate the effects of the learning curve (LC) on perioperative data, early oncologic, and functional outcomes. Methods The records of 132 RALRP cases performed between January 2016 and March 2019 by a single surgeon experienced in open and laparoscopic radical prostatectomy were evaluated retrospectively. Results of 91 cases with the bilateral nerve-sparing technique were analyzed. The learning curve was determined using the moving average method. LC analysis using the moving average method showed that the LC stabilized between cases 40 and 50. So, patients were divided into two groups: group 1 consisted of the first 45 cases, while group 2 consisted of 46-91st cases. The groups were compared in terms of surgical, functional, and oncologic outcomes. Results The mean duration of surgery was significantly reduced in the second group (250 vs 235 min, p <0.002). However, there was no statistically significant difference between the groups in terms of hemoglobin decrease, hospitalization and catheterization time, and intraoperative and postoperative complication rates. The rates of pT2 cancers' positive surgical margins (PSMs) were 32.4% and 19.4%, respectively. The recovery rate of continence in all the patients was 90.1% at 12 months. The potency ratios were calculated as 33.8% at 12 months. There was no statistically significant difference between the groups in terms of potency and continence rates at 3 months and 12 months, postoperatively. Conclusion For surgeons experienced in retropubic radical prostatectomy (RRP) and laparoscopic radical prostatectomy (LRP) surgeries, RALRP is a safe and feasible surgical procedure for both oncological and functional outcomes even during the learning curve.