Evidence map›Paper›PMID 38911193›Full record

ArticleHepatobiliary surgery and nutrition2024

3D laparoscopic anatomical hepatectomy guided by 2D real-time indocyanine green fluorescence imaging for hepatocellular carcinoma.

Feng Tian, Li Cao, Jian Chen, Shuguo Zheng, Jianwei Li

Abstract read
In one paragraph

Article in Hepatobiliary surgery and nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Feng TianDepartment of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Li CaoDepartment of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Jian ChenDepartment of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Shuguo ZhengDepartment of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Jianwei LiDepartment of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Laparoscopic anatomical hepatectomy (LAH) for patients with hepatocellular carcinoma (HCC) has been advocated by many surgeons in the hope of producing better oncological outcomes. Two recent techniques, 3D laparoscopic system and 2D real-time indocyanine green fluorescence imaging (r-ICG) guidance, are benefit for improving the operative precision of LAH in different aspects. However, these two techniques cannot be applied concomitantly because of the technical limitation. Although a new modern laparoscopic system with both 3D and indocyanine green (ICG) imaging mode has been designed, it has not been listed in many countries including China. Thus, we design a new procedure to perform the 3D LAH with 2D r-ICG guidance for HCCs with conventional laparoscopic systems. In this procedure, both 3D and 2D laparoscopic systems were used. A total of 11 patients with HCC received 3D laparoscopic right posterior sectionectomy (LRPS) with 2D r-ICG guidance. The right posterior Glissonian pedicle was clamped under the 3D vision. Then ICG solution was then intravenously administrated. The liver parenchyma was transected under the 3D vision and guided by 2D ICG vision simultaneously. There was no severe complications (Clavien-Dindo ≥III) and operation related death. The 90-day mortality was also nil. By using this procedure, the advantages of two techniques, 3D laparoscopic system and 2D r-ICG guidance, were combined so that LAH could be performed with more precision. However, it should be validated in more studies.

Indexed as

2D laparoscopic ICG fluorescence imaging3D laparoscopyindocyanine green fluorescence imaging (ICG fluorescence imaging)Laparoscopic anatomical hepatectomy (LAH)

Identifiers

PMID38911193
PMCPMC11190512

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.