Advancing techniques, navigating liver resection complexity, and mastering the learning curve for safe expansion of minimally invasive hepatectomy

In a recent issue of the International Journal of Surgery, Li et al.¹ conducted a multi-institutional study across 11 centers in China, evaluating the outcomes of patients undergoing minimally invasive anatomical hepatectomy using the Laennec approach. Among the 445 patients included, 106 underwent the traditional approach, while 339 received the Laennec approach. The study's most significant finding was that the Laennec approach was associated with a shorter operation time, including reduced hepatic pedicle isolation duration, compared to the traditional approach. Additionally, the authors conducted a subgroup analysis, stratifying patients by tumor pathology and surgical technique (laparoscopic vs. robotic).

Laennec described the capsule surrounding the hepatic vein as follows: “At the sites where major hepatic veins exit the liver and flow into the vena cava, fibrous membranes envelop the hepatic parenchyma, with the capsule intervening between the hepatic vein and hepatic parenchyma. In contrast, peripheral hepatic veins attach directly to the liver parenchyma without this intervening capsule.”2, 3, 4 Sugioka et al. conceptualized extrahepatic Glissonean pedicle isolation, emphasizing the gap between the Glissonean pedicle and Laennec's capsule.³ The authors' group demonstrated the effectiveness of the Laennec approach for minimally invasive anatomic hepatectomy in a large cohort of patients. Patients who underwent liver resection using the Laennec approach experienced significantly shorter durations for hepatic pedicle isolation, liver mobilization, hepatic vein exposure, and parenchymal transection compared to those treated with the traditional approach. Notably, these effects were further enhanced when the procedure was performed with a robotic platform rather than a laparoscopic platform.

Li et al.¹ stratified outcomes by liver resection procedures across the groups, highlighting the varying complexity levels within minimally invasive liver resection. Our group proposed a three-tier classification system for liver resection complexity: Grade I (low complexity)—wedge resection for anterolateral or posterosuperior segments and left lateral sectionectomy; Grade II (intermediate complexity)—segmentectomy of anterolateral segments and left hepatectomy; and Grade III (high complexity)—posterosuperior segmentectomy, right posterior sectionectomy, right hepatectomy, central hepatectomy, and extended left/right hepatectomy.5, 6, 7, 8 Given the heterogeneity of liver resection types, intergroup comparisons may introduce bias in the analysis. Notably, operation time using the traditional approach was shortest for left hepatectomy, longest for right posterior sectionectomy, and intermediate for right hepatectomy—findings consistent with our previous studies.5, 6, 7 To safely expand the indications for minimally invasive hepatectomy, a deeper understanding of emerging techniques, as demonstrated by the authors, and the learning curve effect of minimally invasive hepatectomy⁹ is essential.

In conclusion, we commend Li et al. for conducting a multi-institution study on the Laennec approach in minimally invasive anatomic hepatectomy.¹ By leveraging the “Laennec gap,” the authors demonstrated notable benefits-most prominently, a reduction in operative time, including shorter duration for hepatic pedicle isolation. This study highlights the potential of the Laennec approach as an efficient technique for minimally invasive liver resection. Future directions should include randomized controlled trials and strategic dissemination of the technique within surgical education to ensure safe and effective adoption.

CRediT authorship contribution statement

Yoshikuni Kawaguchi: Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Conceptualization.

Informed consent

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Ethical approval

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Data availability statement

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Declaration of generative AI and AI-assisted technologies in the writing process

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Funding

None.

Declaration of competing interest

Nothing to disclose.