Assessment of the learning curve of robotic liver resection using complexity-adjusted CUSUM analysis

Robotic liver resection (RLR) has progressively been integrated into minimally invasive hepatobiliary surgery. However, comparative evidence evaluating the learning curves of RLR and laparoscopic liver resection (LLR) while accounting for surgical complexity remains limited. This study analyzed the learning curve of RLR through complexity-adjusted cumulative sum (CA-CUSUM) analysis based on operative time and the IWATE difficulty score. This retrospective study was conducted using data from minimally invasive liver surgeries performed by a surgeon at the single institution between 2017 and 2025. Donor hepatectomies and combined organ resections were excluded. A CA-CUSUM analysis adjusted for the IWATE score was utilized to evaluate the learning curves. A total of 55 RLR and 149 LLR cases were analyzed. The CA-CUSUM analysis revealed a breakpoint at case 11 for RLR and case 50 for LLR ([Formula: see text]). The initial 11 RLR cases had a median IWATE score of 9, showing that high-complexity cases were performed from the beginning of the robotic experience. Comparing the competency phase (cases 1-11) and efficiency phase (cases 12-55) of RLR, the median operative time decreased from 215 to 145 min ([Formula: see text]) and median estimated blood loss decreased from 1,000 to 450 ㎖ ([Formula: see text]). Other safety-related outcomes were comparable between the two phases, with zero conversions to open surgery. Adjusting for procedural difficulty, the complexity-adjusted analysis identified an operative-time breakpoint after 11 cases in RLR, compared to 50 cases in LLR. For a surgeon with extensive laparoscopic background, this rapid transition reflects technical adaptation and platform-specific skill transfer. These findings highlight the efficiency of transitioning to robotic liver surgery when leveraging prior minimally invasive experience.