Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

When performing hepatectomy to treat liver tumors, accurately determining the resection margin and ensuring the adequacy of residual liver parenchyma are of utmost importance. At present, intraoperative ultrasound and indocyanine green fluorescence navigation are frequently utilized methods. However, certain technical constraints prevent their extensive application. We have developed the 3D-LAST technique for precise liver tumor resection. This technique makes use of computer post-processing to extract features from computed tomography (CT) scans and generate volumetric images, creating three-dimensional (3D) visualizations. This provides a valuable resource for clinical decision-making, as it can vividly display complex internal anatomical structures in an intuitive and stereoscopic manner. In this study, preoperative 3D positioning was carried out on patients with a single liver tumor to identify anatomical landmarks and calculate the resection range. During the surgical procedure, margin lines of lengths calculated by preoperative 3D software were set up, and silk thread was used to mark the edges. This approach offers a time-saving and accurate way to determine the optimal cutting plane. The objective of this article is to demonstrate the viability of applying 3D-LAST in laparoscopic segmentectomy for liver tumors. The results of the research indicate that 3D-LAST is a safe, effective, and practical new method for intraoperative liver navigation, and it has great potential for widescale promotion.

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Publication Details

Journal
Journal of Visualized Experiments
Published
2025-05-23
DOI
https://doi.org/10.3791/68395
Primary Topic
Gastrointestinal Tumor Research and Treatment
Type
article
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article

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

Haotian Liao, Jiaxin Li, Zhihong Zhang, Yifan Zhou et al.
Journal of Visualized Experiments
Gastrointestinal Tumor Research and Treatment
article

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

Haotian Liao, Jiaxin Li, Zhihong Zhang, Yifan Zhou, Yong Zeng
article en

Abstract

When performing hepatectomy to treat liver tumors, accurately determining the resection margin and ensuring the adequacy of residual liver parenchyma are of utmost importance. At present, intraoperative ultrasound and indocyanine green fluorescence navigation are frequently utilized methods. However, certain technical constraints prevent their extensive application. We have developed the 3D-LAST technique for precise liver tumor resection. This technique makes use of computer post-processing to extract features from computed tomography (CT) scans and generate volumetric images, creating three-dimensional (3D) visualizations. This provides a valuable resource for clinical decision-making, as it can vividly display complex internal anatomical structures in an intuitive and stereoscopic manner. In this study, preoperative 3D positioning was carried out on patients with a single liver tumor to identify anatomical landmarks and calculate the resection range. During the surgical procedure, margin lines of lengths calculated by preoperative 3D software were set up, and silk thread was used to mark the edges. This approach offers a time-saving and accurate way to determine the optimal cutting plane. The objective of this article is to demonstrate the viability of applying 3D-LAST in laparoscopic segmentectomy for liver tumors. The results of the research indicate that 3D-LAST is a safe, effective, and practical new method for intraoperative liver navigation, and it has great potential for widescale promotion.

Journal of Visualized Experiments(219)
Sichuan University (CN), West China Hospital of Sichuan University (CN)
Openalex Percentile: Top 83%
Gastrointestinal Tumor Research and Treatment
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Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor — Haotian Liao, Jiaxin Li, et al. · Journal of Visualized Experiments (2025) | TGRS Research Map | TGRS