Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/130682
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dc.contributor.authorCheng, K.en
dc.contributor.authorLin, A.en
dc.contributor.authorYuvaraj, J.en
dc.contributor.authorNicholls, S.J.en
dc.contributor.authorWong, D.T.L.en
dc.date.issued2021en
dc.identifier.citationCells, 2021; 10(4):879-1-879-17en
dc.identifier.issn2073-4409en
dc.identifier.issn2073-4409en
dc.identifier.urihttp://hdl.handle.net/2440/130682-
dc.description.abstractRadiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.en
dc.description.statementofresponsibilityKevin Cheng, Andrew Lin, Jeremy Yuvaraj, Stephen J. Nicholls and Dennis T.L. Wongen
dc.language.isoenen
dc.publisherMDPI AGen
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en
dc.subjectMachine learning; radiomics; coronary computed tomography angiography; acute coronary syndrome; atherosclerosis; plaque; peri-coronary adipose tissueen
dc.titleCardiac computed tomography radiomics for the non-invasive assessment of coronary inflammationen
dc.typeJournal articleen
dc.identifier.doi10.3390/cells10040879en
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/GNT2002573en
pubs.publication-statusPublisheden
dc.identifier.orcidNicholls, S.J. [0000-0002-9668-4368]en
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