Show item record

dc.contributor.authorCerny, Milena
dc.contributor.authorBergeron, Catherine
dc.contributor.authorBilliard, Jean-Sébastien
dc.contributor.authorMurphy-Lavallée, Jessica
dc.contributor.authorOlivié, Damien
dc.contributor.authorBérubé, Joshua
dc.contributor.authorFan, Boyan
dc.contributor.authorCastel, Hélène
dc.contributor.authorTurcotte, Simon
dc.contributor.authorPerreault, Pierre
dc.contributor.authorChagnon, Miguel
dc.contributor.authorTang, An
dc.date.accessioned2023-05-17T14:17:13Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2023-05-17T14:17:13Z
dc.date.issued2018-04-10
dc.identifier.urihttp://hdl.handle.net/1866/27980
dc.publisherRadiological Society of North Americafr
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.fr
dc.subjectLI-RADSfr
dc.subjectHepatocellular carcinomafr
dc.subjectMajor featuresfr
dc.subjectAncillary featuresfr
dc.subjectCategoryfr
dc.subjectCirrhosisfr
dc.subjectSensitivity and specificityfr
dc.subjectMRIfr
dc.titleLI-RADS for MR imaging diagnosis of hepatocellular carcinoma: performance of major and ancillary featuresfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté de médecine. Département de radiologie, radio-oncologie et médecine nucléairefr
dc.identifier.doi10.1148/radiol.2018171678
dcterms.abstractPurpose To evaluate the performance of major features, ancillary features, and categories of Liver Imaging Reporting and Data System (LI-RADS) version 2014 at magnetic resonance (MR) imaging for the diagnosis of hepatocellular carcinoma (HCC). Materials and Methods This retrospective institutional review board–approved study included patients with liver MR imaging and at least one pathologically proved lesion. Between 2004 and 2016, 102 patients (275 observations including 113 HCCs) met inclusion criteria. Two radiologists independently assessed major and ancillary imaging features for each liver observation and assigned a LI-RADS category. Per-lesion estimates of diagnostic performance of major features, ancillary features, and LI-RADS categories were assessed by using generalized estimating equation models. Results Major features (arterial phase hyperenhancement, washout, capsule, and threshold growth) had a sensitivity of 88.5%, 60.6%, 32.9%, and 41.6%, and a specificity of 18.6%, 84.8%, 98.8%, and 83.2% for HCC, respectively. Ancillary features (mild-moderate T2 hyperintensity, restricted diffusion, mosaic architecture, intralesional fat, lesional fat sparing, blood products, and subthreshold growth) had a sensitivity of 62.2%, 54.8%, 9.9%, 30.9%, 23.1%, 2.8%, and 48.3%, and a specificity of 79.4%, 90.6%, 99.4%, 94.2%, 83.1%, 99.3%, and 91.4% for HCC, respectively. The LR-5 or LR-5 V categories had a per-lesion sensitivity of 50.8% and a specificity of 95.8% for HCC, respectively. The LR-4, LR-5, or LR-5 V categories (determined by using major features only vs combination of major and ancillary features) had a per-lesion sensitivity of 75.9% and 87.9% and a per-lesion specificity of 87.5% and 86.2%, respectively. Conclusion The use of ancillary features in combination with major features increases the sensitivity while preserving a high specificity for the diagnosis of HCC.fr
dcterms.isPartOfurn:ISSN:0033-8419fr
dcterms.isPartOfurn:ISSN:1527-1315fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantCerny M, Bergeron C, Billiard JS, Murphy-Lavallee J, Olivie D, Berube J, Fan B, Castel H, Turcotte S, Perreault P, Chagnon M, Tang A. LI-RADS for MR Imaging Diagnosis of Hepatocellular Carcinoma: Performance of Major and Ancillary Features. Radiology 2018;288(1):118-128. doi: 10.1148/radiol.2018171678fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleRadiologyfr
oaire.citationVolume288fr
oaire.citationIssue1fr
oaire.citationStartPage118fr
oaire.citationEndPage128fr


Files in this item

Microsoft Word

This item appears in the following Collection(s)

Show item record

Attribution 4.0 International (CC BY 4.0)
Usage rights : Attribution 4.0 International (CC BY 4.0)