Evidence map›Paper›PMID 35004273›Full record

ArticleFrontiers in oncology2021

A Non-Invasive Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Hepatocellular Carcinoma.

Huanhuan Wang, Ye Lu, Runkun Liu, Liang Wang, Qingguang Liu, Shaoshan Han

Open access · goldAbstract read
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 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.1field-weighted citation impact, top 6% of its field
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

18 citing papers in PubMed, 26 citations in OpenAlex.

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  17. Analysis of Related Risk Factors of Microvascular Invasion in Hepatocellular Carcinoma.Computational and mathematical methods in medicine · 2022
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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

6 authors at 1 institution in 1 country.

Huanhuan WangDepartment of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ye LuDepartment of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Runkun LiuDepartment of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Liang WangDepartment of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Qingguang LiuDepartment of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Shaoshan HanDepartment of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
First Affiliated Hospital of Xi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicrovascular invasion (MVI) is a significant predictive factor for early recurrence, metastasis, and poor prognosis of hepatocellular carcinoma. The aim of the present study is to identify preoperative factors for predicting MVI, in addition to develop and validate non-invasive nomogram for predicting MVI.

methodsA total of 381 patients with resected HCC were enrolled and divided into a training cohort (

resultThe serum VEGF-A levels in the MVI positive group (n = 198) and MVI negative group (n = 183) were 215.25 ± 105.68 pg/ml and 86.52 ± 62.45 pg/ml, respectively (

conclusionsHigher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery are promising markers for MVI prediction in HCC. A reliable non-invasive nomogram which incorporated blood biomarkers and imaging risk factors was established and validated. The nomogram achieved desirable effectiveness in preoperatively predicting MVI in HCC patients.

Indexed as

hepatocellular carcinomamicrovascular invasionnomogramprediction modelVEGF-A

Identifiers

PMID35004273
PMCPMC8739965
OpenAlexW4200283461

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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.