Background Sentinel lymph node (SLN) biopsy is increasingly used in endometrial cancer staging; however, success of the technique is variable, and the learning curve needs to be better understood. Success is defined as identification of a SLN specimen containing nodal tissue in bilateral hemi-pelvises.

Objective To assess the learning curve of surgeons at an academic institution in performing successful SLN mapping and biopsy during robotic staging for endometrial cancer.

Methods After institutional review board approval, patients who underwent staging with robotic SLN mapping using indocyanine green at a single academic program between July 2012 and December 2017 were identified. Demographic, pathologic, and surgical data were retrospectively collected from the medical records. Descriptive and comparative statistics were performed. Surgeon rates of successful bilateral SLN mapping and removal of lymphoid-containing SLN specimens were compared. A logistic model was used to analyze the probability of successful SLN mapping and removal of lymph node-containing tissue with increasing number of procedures performed.

Results Three hundred and seventeen patients met the eligibility criteria. Most had early-stage, low-grade endometrial cancer. A total of 194 (61%) patients had successful bilateral mapping. Among seven surgeons, a plateau in rates of successful bilateral mapping was achieved after 40 cases. No linear correlation was seen between the number of surgeries performed and the rate of removal of lymph node-containing tissue among surgeons. Each additional 10 procedures performed was associated with a 5% and an 11% increase in the odds of successful SLN mapping and removal of lymph node-containing tissue, respectively.

Discussion The successful removal of lymph node-containing specimens appears to be a surgeon-specific phenomenon. The plateau of the learning curve for successful bilateral mapping seems to be reached at around 40 cases. These first 40 cases offer a time for auditing of individual rates of SLN mapping and removal to identify surgeons who may benefit from procedure-specific remediation.