The Learning Curve for Robotic Lateral Pelvic Lymph Node Dissection for Rectal Cancer: A View from the West.

Abstract

Lateral pelvic lymph-node dissection is performed for selected patients with rectal cancer with persistent lateral nodal disease after neoadjuvant therapy. This technique has been slow to be adopted in the West due to concerns regarding technical difficulty. This is the first report on the learning curve for lateral pelvic lymph node dissection in the US or Europe. The aim of this study was to analyze the learning curve associated with robotic lateral pelvic lymph node dissection. Retrospective observational cohort. Tertiary academic cancer center. Consecutive patients from 2012 to 2021. All patients underwent robotic lateral pelvic lymph node dissection. The primary endpoints were the learning curves for maximum number of nodes retrieved and urinary retention which was evaluated with simple cumulative-sum and two-sided Bernoulli cumulative-sum charts. Fifty-four procedures were included. A single-surgeon (n = 35) and an institutional learning curve are presented in the analysis. In the single-surgeon learning curve, a turning point marking the end of a learning phase was detected at the 12th procedure for the number of retrieved nodes and at the 20th for urinary retention. In the institutional learning curve analysis, two turning points were identified at the 13th and 26th procedures indicating progressive improvements for the number of retrieved nodes and at the 27th for urinary retention. No sustained alarm signals were detected at any time point. The retrospective nature, small sample size and the referral center nature of the reporting institution that may limit generalizability. In a setting of institutional experience with robotic colorectal surgery including beyond TME resections, the learning curve for robotic lateral pelvic lymph node dissection is acceptably short. Our results demonstrate feasibility of acquisition of this technique in a controlled setting, with sufficient case volume and proctoring can optimize the learning curve. See Video Abstract.