Evidence map›Paper›PMID 31705286›Full record

Trial reportSurgical endoscopy2020

Real-time navigation for laparoscopic hepatectomy using image fusion of preoperative 3D surgical plan and intraoperative indocyanine green fluorescence imaging.

Peng Zhang, Huoling Luo, Wen Zhu, Jian Yang, Ning Zeng, Yingfang Fan, Sai Wen, Nan Xiang, Fucang Jia, Chihua Fang

Abstract readClinical Trial
PubMed Publisher
In one paragraph

Trial report in Surgical endoscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 5 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  13. [3D visualization-based classification of left intrahepatic vessels and its application in precision hepatectomy].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025
    Article
  14. SfMDiffusion: self-supervised monocular depth estimation in endoscopy based on diffusion models.International journal of computer assisted radiology and surgery · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Peng ZhangThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Huoling LuoResearch Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Wen ZhuThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Jian YangThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Ning ZengThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Yingfang FanThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Sai WenThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Nan XiangThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China. zjyyxn@126.com.
Fucang JiaResearch Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. fc.jia@siat.ac.cn.
Chihua FangThe First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China. fangch_dr@163.com.ORCID 0000-0002-1112-1540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnderstanding the internal anatomy of the liver remains a major challenge in anatomical liver resection. Although virtual hepatectomy and indocyanine green (ICG) fluorescence imaging techniques have been widely used in hepatobiliary surgery, limitations in their application for real-time navigation persist.

objectiveThe aim of the present study was to evaluate the feasibility and clinical utility of the novel laparoscopic hepatectomy navigation system (LHNS), which fuses preoperative three-dimensional (3D) models with ICG fluorescence imaging to achieve real-time surgical navigation.

methodsWe conducted a retrospective review of clinical outcome for 64 patients who underwent laparoscopic hepatectomy from January 2018 to December 2018, including 30 patients who underwent the procedure using the LHNS (LHNS group) and 34 patients who underwent the procedure without LHNS guidance (Non-LHNS group).

resultsThere was no significant difference in preoperative characteristics between the two groups. The LHNS group had a significantly less blood loss (285.0 ± 163.0 mL vs. 391.1 ± 242.0 mL; P = 0.047), less intraoperative blood transfusion rate (13.3% vs. 38.2%; P = 0.045), and shorter postoperative hospital stay (7.8 ± 2.1 days vs. 10.6 ± 3.8 days; P < 0.001) than the Non-LHNS group. There was no statistical difference in operative time and the overall complication rate between the two groups. The liver transection line was clearly delineated by the LHNS in 27 patients; however, the projection of boundary was unclear in 2 cases, and in 1 case, the boundary was not clearly displayed by ICG fluorescence imaging.

conclusionsWe developed the LHNS to address limitations of current intraoperative imaging systems. The LHNS is hopefully to become a promising real-time navigation system for laparoscopic hepatectomy.

Indexed as

Surgical Navigation SystemsAdultAgedBlood Loss, SurgicalBlood TransfusionCarcinoma, HepatocellularFeasibility StudiesFemaleFluorescenceHepatectomyHumansImaging, Three-DimensionalIndocyanine GreenLaparoscopyLength of StayLiver NeoplasmsIndocyanine GreenFluorescenceIndocyanine greenLaparoscopic hepatectomySurgical navigationVirtual hepatectomy

Identifiers

PMID31705286

What OpenQuestion holds

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Registered trials

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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.