Evidence map›Paper›PMID 34295812›Full record

ArticleFrontiers in oncology2021

Assessment of Vascular Network Connectivity of Hepatocellular Carcinoma Using Graph-Based Approach.

Qiaoyu Liu, Boyu Zhang, Luna Wang, Rencheng Zheng, Jinwei Qiang, He Wang, Fuhua Yan, Ruokun Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Molecular Mediated Angiogenesis and Vasculogenesis Networks.International journal of molecular sciences · 2025
    Article
4 · The record

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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

8 authors.

Qiaoyu LiuDepartment of Radiology, Tenth People's Hospital of Tongji University, Shanghai, China.
Boyu ZhangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Luna WangDepartment of Radiology, Shanghai Chest Hospital, Shanghai JiaoTong University, Shanghai, China.
Rencheng ZhengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Jinwei QiangDepartment of Radiology, Jinshan hospital, Fudan University, Shanghai, China.
He WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Fuhua YanDepartment of Radiology, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Ruokun LiDepartment of Radiology, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe angiogenesis of liver cancer is a key condition for its growth, invasion, and metastasis. This study aims to investigate vascular network connectivity of hepatocellular carcinoma (HCC) using graph-based approach.

methodsOrthotopic HCC xenograft models (n=10) and the healthy controls (n=10) were established. After 21 days of modeling, hepatic vascular casting and Micro-CT scanning were performed for angiography, followed by blood vessels automatic segmentation and vascular network modeling. The topologic parameters of vascular network, including clustering coefficient (CC), network structure entropy (NSE), and average path length (APL) were quantified. Topologic parameters of the tumor region, as well as the background liver were compared between HCC group and normal control group.

resultsCompared with normal control group, the tumor region of HCC group showed significantly decreased CC [(0.046 ± 0.005)

conclusionGraph-based approach allows quantification of vascular connectivity of HCC. Disrupted vascular topological connectivity exists in the tumor region, as well as the background liver of HCC.

Indexed as

graph-based approachhepatocellular carcinomaMicro-CTtopologic connectivityvascular network

Identifiers

PMID34295812
PMCPMC8290165

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