Learning curve and factors influencing successful robot-assisted bilateral sentinel lymph node mapping in early-stage cervical cancer: an observational cohort study

Abstract

Objectives: To evaluate whether a learning curve affects the bilateral sentinel lymph node (SLN) detection in early-stage cervical cancer.

Methods: All patients with FIGO (2018) stage IA1-IB2 or IIA1 cervical cancer who had undergone robot-assisted SLN mapping performed with a combination of preoperative technetium-99m nanocolloids (including preoperative imaging) and intraoperative blue dye were retrospectively included. Risk-adjusted cumulative sum (RA-CUSUM) analysis was used to determine if a learning curve based on bilateral SLN detection existed in this cohort.

Results: A total of 227 cervical cancer patients were included. In 98.2% of patients (223/227) at least one SLN was detected. The bilateral SLN detection rate was 87.2% (198/227). Except for age (OR 1.06 per year, 95%CI 1.02-1.09), no significant risk factors for non-bilateral SLN detection were found (e.g., prior conization, BMI or FIGO stage). The RA-CUSUM analysis showed no clear learning phase during the first procedures and cumulative bilateral detection rate remained at least 80% during the entire inclusion period.

Conclusions: In this single-institution experience, we observed no learning curve affecting robot-assisted SLN mapping using a radiotracer and blue dye in early-stage cervical cancer patients, with stable bilateral detection rates of at least 80% when adhering to a standardized methodology.

Keywords: Cervical cancer; learning curve; radiotracer; robotic surgery; sentinel lymph node.