Evidence map›Paper›PMID 33936969›Full record

ArticleQuantitative imaging in medicine and surgery2021

Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.

Houjiao Dai, Minhua Lu, Bingsheng Huang, Mimi Tang, Tiantian Pang, Bing Liao, Huasong Cai, Mengqi Huang, Yongjin Zhou, Xin Chen and 2 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 4 pooled it
5.8field-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

37 citing papers in PubMed, 4 syntheses or guidelines pooled it, 52 citations in OpenAlex.

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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

12 authors at 5 institutions in 1 country.

Houjiao DaiMedical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Minhua LuSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Bingsheng HuangMedical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Mimi TangDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Tiantian PangSchool of Computer Science and Software Engineering, Jilin University, Changchun, China.
Bing LiaoDepartment of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Huasong CaiDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Mengqi HuangDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Yongjin ZhouSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Xin ChenSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Huijun DingSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Shi-Ting FengDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Shenzhen University Health Science Center · CNSun Yat-sen University · CNShenzhen University · CNThe First Affiliated Hospital, Sun Yat-sen University · CNJilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicrovascular invasion (MVI) has a significant effect on the prognosis of hepatocellular carcinoma (HCC), but its preoperative identification is challenging. Radiomics features extracted from medical images, such as magnetic resonance (MR) images, can be used to predict MVI. In this study, we explored the effects of different imaging sequences, feature extraction and selection methods, and classifiers on the performance of HCC MVI predictive models.

methodsAfter screening against the inclusion criteria, 69 patients with HCC and preoperative gadoxetic acid-enhanced MR images were enrolled. In total, 167 features were extracted from the MR images of each sequence for each patient. Experiments were designed to investigate the effects of imaging sequence, number of gray levels (

resultsThe radiomics model based on the images of the hepatobiliary phase (HBP) had better predictive performance than those based on the arterial phase (AP), portal venous phase (PVP), and pre-enhanced T1-weighted images [area under the receiver operating characteristic (ROC) curve (AUC) =0.792

conclusionsImaging sequences, feature extraction and selection methods, and classifiers can have a considerable effect on the predictive performance of radiomics models for HCC MVI.

Indexed as

Hepatocellular carcinoma (HCC)magnetic resonance imaging (MRI)microvascular invasion (MVI)radiomics

Identifiers

PMID33936969
PMCPMC8047362
OpenAlexW3134304841

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.