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Texture Analysis of Hepatocellular Carcinoma on Magnetic Resonance Imaging: Assessment for Performance in Predicting Histopathologic Grade

DC FieldValueLanguage
dc.contributor.author김주희-
dc.contributor.author유정식-
dc.contributor.author정재준-
dc.contributor.author조은석-
dc.date.accessioned2020-12-11T08:02:12Z-
dc.date.available2020-12-11T08:02:12Z-
dc.date.issued2020-11-
dc.identifier.issn0363-8715-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/180748-
dc.description.abstractObjective: The aim of the study was to evaluate the performance of texture analysis for discriminating the histopathological grade of hepatocellular carcinoma (HCC) on magnetic resonance imaging. Methods: Preoperative magnetic resonance imaging data from 101 patients with HCC, including T2-weighted imaging, arterial phase, and apparent diffusion coefficient mapping, were analyzed using texture analysis software (TexRAD). Differences among the histological groups were analyzed using the Mann-Whitney U test. The performance of texture features was evaluated using receiver operating characteristic analysis. Results: Entropy was the most significantly relevant texture feature for distinguishing each histological grade group of HCC (P < 0.05). In ROC analysis, entropy with spatial scale filter 3 (area under curve the receiver operating characteristic curve [AUC], 0.778), mean with coarse filter (spatial scale filter 5; AUC, 0.670), and skewness without filtration (AUC, 0.760) had the highest AUC value on T2-weighted imaging, arterial phase, and apparent diffusion coefficient maps, respectively. Conclusions: Magnetic resonance imaging texture analysis demonstrated potential for predicting the histopathological grade of HCCs.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherLippincott Williams & Wilkins-
dc.relation.isPartOfJOURNAL OF COMPUTER ASSISTED TOMOGRAPHY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleTexture Analysis of Hepatocellular Carcinoma on Magnetic Resonance Imaging: Assessment for Performance in Predicting Histopathologic Grade-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJeong Min Choi-
dc.contributor.googleauthorJeong-Sik Yu-
dc.contributor.googleauthorEun-Suk Cho-
dc.contributor.googleauthorJoo Hee Kim-
dc.contributor.googleauthorJae-Joon Chung-
dc.identifier.doi10.1097/RCT.0000000000001087-
dc.contributor.localIdA00951-
dc.contributor.localIdA02500-
dc.contributor.localIdA02500-
dc.contributor.localIdA03712-
dc.contributor.localIdA03712-
dc.contributor.localIdA03881-
dc.contributor.localIdA03881-
dc.relation.journalcodeJ01350-
dc.identifier.eissn1532-3145-
dc.identifier.pmid32976263-
dc.identifier.urlhttps://journals.lww.com/jcat/Fulltext/2020/11000/Texture_Analysis_of_Hepatocellular_Carcinoma_on.15.aspx-
dc.contributor.alternativeNameKim, Joo Hee-
dc.contributor.affiliatedAuthor김주희-
dc.contributor.affiliatedAuthor유정식-
dc.contributor.affiliatedAuthor유정식-
dc.contributor.affiliatedAuthor정재준-
dc.contributor.affiliatedAuthor정재준-
dc.contributor.affiliatedAuthor조은석-
dc.contributor.affiliatedAuthor조은석-
dc.citation.volume44-
dc.citation.number6-
dc.citation.startPage901-
dc.citation.endPage910-
dc.identifier.bibliographicCitationJOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, Vol.44(6) : 901-910, 2020-11-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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