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Novel Benchmark for Robotic Liver Resection - Bridging Tradition with Innovation.

Zhihao Li1, Matthias Pfister2, Dimitri A Raptis3

  • 1Department of Surgery and Transplantation, University of Zurich, Zurich, Switzerland.

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|May 20, 2025
PubMed
Summary

This study establishes the first benchmarks for robotic liver resection (R-LR), showing that careful patient selection, not just center volume, leads to better outcomes. These benchmarks will help standardize R-LR and improve surgical training.

Keywords:
benchmarkingcomplicationiwate scoremorbiditypatient selectionrobotic liver resectiontextbook outcome

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Area of Science:

  • Minimally invasive surgery
  • Hepatobiliary surgery
  • Surgical oncology

Background:

  • Robotic liver resection (R-LR) is an advancement in minimally invasive liver surgery.
  • Standardized benchmarks for R-LR, particularly for minor resections, are lacking.
  • The impact of R-LR on optimizing surgical outcomes requires further clarification.

Purpose of the Study:

  • To establish benchmark cutoffs for both major and minor robotic liver resections.
  • To assess the influence of patient selection criteria on R-LR outcomes.
  • To provide a framework for comparing R-LR performance across different centers and modalities.

Main Methods:

  • Retrospective analysis of 4028 consecutive adult R-LR cases from 30 international centers (2020-2023).
  • Benchmark centers defined by ≥15 annual R-LR cases and specific patient criteria (ASA ≤2, no major comorbidities, no prior liver resections, Child-Pugh A).
  • Benchmark cutoffs for 14 outcomes established at the 50th and 75th percentiles; multivariable logistic regression identified predictors of textbook outcomes.

Main Results:

  • 2632 cases (65.3%) from 18 high-volume centers met benchmark criteria.
  • Established benchmark cutoffs for open conversion, major complications, and R1 resection for minor and major R-LR.
  • Patient selection, indicated by the proportion of benchmark patients, was significantly associated with textbook outcomes, outweighing center case volume.

Conclusions:

  • Defines the first set of benchmarks for robotic liver resection (R-LR).
  • Emphasizes the critical role of patient selection in achieving optimal R-LR outcomes.
  • Provides benchmarks to guide R-LR training, quality assessment, and procedural expansion.