Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/130682
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Type: Journal article
Title: Cardiac computed tomography radiomics for the non-invasive assessment of coronary inflammation
Author: Cheng, K.
Lin, A.
Yuvaraj, J.
Nicholls, S.J.
Wong, D.T.L.
Citation: Cells, 2021; 10(4):879-1-879-17
Publisher: MDPI AG
Issue Date: 2021
ISSN: 2073-4409
2073-4409
Statement of
Responsibility: 
Kevin Cheng, Andrew Lin, Jeremy Yuvaraj, Stephen J. Nicholls and Dennis T.L. Wong
Abstract: Radiomics, 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.
Keywords: Machine learning; radiomics; coronary computed tomography angiography; acute coronary syndrome; atherosclerosis; plaque; peri-coronary adipose tissue
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/).
RMID: 1000040708
DOI: 10.3390/cells10040879
Grant ID: http://purl.org/au-research/grants/nhmrc/GNT2002573
Appears in Collections:Medicine publications

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