Novel Benchmark for Robotic Liver Resection - Bridging Tradition with Innovation.
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OBJECTIVE: To establish benchmark cutoffs for robotic liver resection (R-LR), encompassing both major and minor resections, and to determine the impact of patient selection on outcomes. BACKGROUND: R-LR is a key advancement in minimally invasive liver surgery but lacks standardized benchmarks, especially for minor resections. While guidelines endorse R-LR, its role in optimizing outcomes remains unclear. This study establishes the first benchmarks for R-LR, enabling comparisons across surgical modalities and refining patient selection. METHODS: This retrospective, multicenter study analyzed consecutive adult patients undergoing R-LR at 30 international centers (2020-2023). Benchmark centers had an annual case volume of ≥15 R-LR. Benchmark criteria included ASA ≤2, no major comorbidities, no prior liver resections, and Child-Pugh A status. Benchmark cutoffs for 14 key outcomes were set at the 50th and 75th percentiles of median values across benchmark centers. Multivariable logistic regression identified predictors of textbook outcomes. RESULTS: Eighteen high-volume centers contributed 4028 cases with 2632 (65.3%) meeting benchmark criteria. Malignancy was the indication in 29.6%, most commonly hepatocellular carcinoma followed by colorectal liver metastases. Major liver resection was performed in 42.6%. The distribution of Iwate difficulty scores was low (25.4%), intermediate (53.9%), advanced (15.9%), and expert (4.9%). Benchmark cutoffs were established for minor and major resection, and stratified by Iwate low (0-3), intermediate (4-6) and high (7-12): Open conversion (≤6.5% minor, ≤10.5% major), major complications within 90 days (≤5.2% minor, ≤16.7% major), R1 resection (≤9.2% minor, ≤6.7% major). Benchmark cases performed at low-volume centers were able to achieve outcomes within the corresponding benchmark cutoffs. In fact, patient selection reflected by the proportion of benchmark patients, rather than case volume, was associated with textbook outcomes. CONCLUSIONS: This study defines R-LR benchmarks, emphasizing patient selection over center volume for optimal outcomes. Benchmark cutoffs guide training and support the expansion of R-LR.