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Propensity score–matched comparison of robot-assisted rectal cancer surgery using hinotori and da Vinci

Hidetoshi Katsuno, Koji Morohara, Tomoyoshi Endo, Kenji Kikuchi, Kenichi Nakamura, Kazuhiro Matsuo, Takahiko Higashiguchi, Tetsuya Koide, Hiromi Kanai, Satoshi Arakawa, Tsunekazu Hanai, Zenichi Morise

Year
2025
Citations
9

Abstract

PURPOSE: The hinotori Surgical Robot System (hereafter "hinotori") is a novel platform for robot-assisted surgery, while the da Vinci Surgical System ("da Vinci") remains the field standard. This study compared short-term surgical outcomes of rectal cancer surgery between these systems using propensity score-matched analysis. METHODS: A retrospective analysis was conducted of 209 consecutive patients who underwent robot-assisted surgery with the da Vinci and 58 patients with the hinotori system. After 2:1 propensity score matching, 108 da Vinci and 54 hinotori cases were included. Surgical outcomes, including operative time, blood loss, postoperative complications, length of hospital stay, and pathological findings, were compared. RESULTS: After matching, the baseline demographics were well balanced between groups. The hinotori system was associated with significantly longer operative time (266 minutes vs. 227 minutes, P=0.014) and console time (156 minutes vs. 110 minutes, P=0.001). However, estimated blood loss and postoperative complication rate did not differ significantly. Pathological findings, including the number of lymph nodes retrieved and the incidence of positive surgical margins, were comparable between systems. CONCLUSIONS: In rectal surgery, the hinotori system demonstrates comparable short-term safety outcomes to da Vinci. Despite longer operative times and limited integrated instrumentation, hinotori-assisted procedures may be feasible in selected patients. Further research should address long-term oncological outcomes and strategies to improve procedural efficiency.

Keywords

Propensity score matchingRobotic surgeryColorectal cancerMedicineRobotSurgical robotSurgeryCancerArtificial intelligenceComputer science

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