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Published on: June 13, 2025
Initial adaptation to robotic rectal cancer surgery using the hinotori surgical robot system: a CUSUM analysis of
Koji Morohara1, Hidetoshi Katsuno2, Tomoyoshi Endo2
1Department of Surgery, Fujita Health University, Okazaki Medical Center, 6-1 Harisakinishi 2-chome, Okazaki, Aichi, 444-0829, Japan. koji.morohara@fujita-hu.ac.jp.
Abstract:
The early adaptation process after transition to a new robotic platform for rectal cancer surgery remains unclear. This single-center retrospective exploratory study compared time-based adaptation patterns in hinotori-assisted rectal cancer surgery between two certified proctor surgeons with different prior da Vinci experience. Fifty-five hinotori-assisted anterior resections with double-stapling technique anastomosis performed by two predefined institutional lead surgeons were analyzed. Both were certified robotic colorectal surgery proctors and had obtained da Vinci proctor certification before the study; they subsequently obtained hinotori proctor certification during the study period. Before the introduction of hinotori, Surgeon A and Surgeon B performed 262 and 90 da Vinci-assisted rectal cancer procedures, respectively. Total operative time, cockpit time, and setup time were evaluated chronologically using a simple cumulative sum (CUSUM) analysis and interpreted descriptively rather than as proficiency thresholds. Surgeon A performed 26 cases, and Surgeon B performed 29. Surgeon A showed a setup-time transition at case 11, decreasing from 29.5 to 17.6 min, whereas total operative time and cockpit time showed multiphasic patterns. Surgeon B showed sequential transitions in setup time, cockpit time, and total operative time at cases 10, 13, and 22, respectively. No CRM/RM positivity, conversion, or platform-specific adverse events occurred. Different initial adaptation patterns were observed during transition to the hinotori platform between two certified robotic surgeons with differing prior da Vinci-assisted rectal cancer surgery volumes. Prior robotic experience, case complexity, team adaptation, and platform-specific workflow should be considered when interpreting early outcomes after transition to a new robotic platform.
