Related Experiment Video
Updated: Oct 13, 2025

Robot-assisted Total Mesorectal Excision and Lateral Pelvic Lymph Node Dissection for Locally Advanced Middle-low Rectal Cancer
Published on: February 12, 2022
Learning curve for robotic bedside assistance for rectal cancer: application of the cumulative sum method
Kazunosuke Yamada1, Norimichi Kogure2, Hitoshi Ojima2
1Department of Gastroenterological Surgery, Gunma Prefectural Cancer Center, 617-1, Nishimachi, Oota, Gunma, 373-0828, Japan. kazuyama@gunma-cc.jp.
Background:
This investigation assesses the learning curve for dedicated bedside assistance at a facility that recently adopted robot-assisted rectal resection.
Methods:
Data from patients with rectal cancer who underwent robotic rectal resections from September 2019 through April 2020 were retrospectively analyzed. Before starting robotic surgery, we set the rule that a console surgeon would not enter the sterile field and all of those maneuvers would be left to a dedicated physician. Docking time was analyzed using the cumulative sum (CUSUM) method to evaluate the learning curve. Different phases in the learning curve were identified according to CUSUM plot configuration. A comparison was made of phases 1 and 2 combined, and phase 3.
Result:
The procedures were performed in 30 patients. Median docking time, console time was 13 min. A total of nine patients had histories of abdominal surgery. CUSUM analysis of docking time demonstrated 3 phases. Each docking time was longer in Phase 1 (the first 3 cases) than the average docking time over the all cases. The docking time in Phase 2 (the 9 middle cases) approximated the average time over the all cases. Phase 3 (the remaining 18 cases) showed further improvement of the docking procedure and time was reduced. A comparison of Phases 1 and 2 combined, and Phase 3, revealed that Phase 3 had a significantly higher rate of history of abdominal surgery.
Conclusion:
Docking manipulation proficiency was achieved in approximately 10 cases without the influence of surgical difficulty.

