Evidence map›Paper›PMID 32567245›Full record

ArticleClinical and translational medicine2020

Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study.

Xiuming Zhang, Shijian Ruan, Wenbo Xiao, Jiayuan Shao, Wuwei Tian, Weihai Liu, Zhao Zhang, Dalong Wan, Jiacheng Huang, Qiang Huang and 6 more

Open access · goldAbstract read
In one paragraph

Article in Clinical and translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 5 pooled it
5.9field-weighted citation impact, top 3% 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

63 citing papers in PubMed, 5 syntheses or guidelines pooled it, 91 citations in OpenAlex.

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  6. Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022
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3 more citing papers are in PubMed but not listed here.

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

16 authors at 4 institutions in 1 country.

Xiuming ZhangDepartment of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Shijian RuanCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
Wenbo XiaoDepartment of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Jiayuan ShaoCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
Wuwei TianCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
Weihai LiuDepartment of Radiology, The People's Hospital of Beilun District, Ningbo, China.
Zhao ZhangDepartment of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
Dalong WanDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Jiacheng HuangDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Qiang HuangDepartment of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Yunjun YangDepartment of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
Hanjin YangDepartment of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Yong DingCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
Wenjie LiangDepartment of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-2741-8285
Xueli BaiDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Tingbo LiangDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zhejiang University · CNFirst Affiliated Hospital Zhejiang University · CNWenzhou Medical University · CNUniversity of Nottingham Ningbo China · CN

Funding

National Key Research and Development Program of China 2018YFE0183900National Natural Science Foundation of China 81971686
6 · The paper itself

Abstract

backgroundThe present study constructed and validated the use of contrast-enhanced computed tomography (CT)-based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC).

methodsWe enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers.

resultsThe developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α-fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier-integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence.

conclusionsOur CT radiomics-based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.

Indexed as

contrast-enhanced CThepatocellular carcinomamicrovascular invasionmultivariable logistic regressionradiomics

Identifiers

PMID32567245
PMCPMC7403665
OpenAlexW3036295504

What OpenQuestion holds

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LicenceCC BY
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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.