Lin A, Kolossváry M, Cadet S, McElhinney P, Göller M, Han D, Yuvaraj J, Nerlekar N, Slomka PJ, Marwan M, Nicholls SJ, Achenbach S, Maurovich-Horvat P, Wong DT, Dey D (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 15
Pages Range: 859-871
Journal Issue: 5
DOI: 10.1016/j.jcmg.2021.11.016
Objectives: The aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis. Background: It remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events. Methods: A total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes. Results: Culprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm3 vs 110.7 mm3 vs 102.7 mm3; LDNCP: 14.2 mm3 vs 9.8 mm3 vs 8.4 mm3; both P
APA:
Lin, A., Kolossváry, M., Cadet, S., McElhinney, P., Göller, M., Han, D.,... Dey, D. (2022). Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography. Journal of the American College of Cardiology : Cardiovascular imaging, 15(5), 859-871. https://doi.org/10.1016/j.jcmg.2021.11.016
MLA:
Lin, Andrew, et al. "Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography." Journal of the American College of Cardiology : Cardiovascular imaging 15.5 (2022): 859-871.
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