Mastering Robotic Liver Resection

Objective: To evaluate the learning curve for robotic liver resection (RLR) in France and determine the impact of procedural frequency on surgical performance (conversion) and postoperative outcomes (complications). Summary Background Data: RLR is increasingly adopted for its ergonomic and technical advantages. However, proficiency in RLR is primarily limited by restricted access to robotic platforms, which hinders consistent case exposure and learning. Understanding how procedural frequency influences learning and outcomes is critical for optimizing training and implementation. Methods: This retrospective multicenter cohort study (FRIES-ACHBPT-2024) included 650 patients who underwent RLR across 10 French hepatobiliary centers (2010–2024). Learning curves were analyzed using cumulative sum (CUSUM) analysis for conversion and blood loss, stratified by procedural complexity (IMM grades 1–3) and recurrence interval (high-frequency: ≤7 days; low-frequency: >7 days). Multivariable logistic regression identified independent predictors of postoperative complications. Results: The conversion rate was 10.8%. Blood loss increased with IMM grade (184 mL for IMM 1, 381 mL for IMM 2, and 753 mL for IMM 3). Learning curve inflection points were reached at 41 cases for conversion and 58 for blood loss. Surgeons with high-frequency access achieved competency earlier (35 vs. 47 cases). Independent predictors of postoperative complications included low-frequency access, age >70, metabolic syndrome, tumor size >5 cm, high IMM grade, and conversion. Conclusions: Frequent access to robotic platforms (at least once a week) facilitates earlier proficiency in RLR and reduces complication risk. Structured training pathways and equitable access are essential to safe and effective RLR adoption.