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Association of Pericoronary Adipose Tissue Quality Determined by Dual-Layer Spectral Detector CT With Severity of Coronary Artery Disease: A Preliminary Study

Background: Pericoronary adipose tissue (PCAT) is considered as a source of inflammatory mediators, leading to the development of coronary atherosclerosis. The study aimed to investigate the correlation between PCAT quality derived from dual-layer spectral detector CT (SDCT) and the severity of coro...

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Detalles Bibliográficos
Autores principales: Dang, Yuxue, Chen, Xujiao, Ma, Shaowei, Ma, Yue, Ma, Quanmei, Zhou, Ke, Liu, Ting, Wang, Kunhua, Hou, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514719/
https://www.ncbi.nlm.nih.gov/pubmed/34660721
http://dx.doi.org/10.3389/fcvm.2021.720127
Descripción
Sumario:Background: Pericoronary adipose tissue (PCAT) is considered as a source of inflammatory mediators, leading to the development of coronary atherosclerosis. The study aimed to investigate the correlation between PCAT quality derived from dual-layer spectral detector CT (SDCT) and the severity of coronary artery disease (CAD), and whether PCAT parameters were independently associated with the presence of CAD. Materials and Methods: A total of 403 patients with symptoms of chest pain who underwent SDCT were included. PCAT quality including fat attenuation index (FAI) measured from conventional polychromatic CT images (FAI(120kvp)) and spectral virtual mono-energetic images at 40 keV (FAI(40keV)), slope of spectral HU curve (λ(HU)), and effective atomic number (Eff-Z) were measured around the lesions representing the maximal degree of vascular stenosis in each patient. Meanwhile, overall epicardial adipose tissue (EAT) attenuation was acquired in the conventional polychromatic energy imaging. Results: FAI(40keV), λ(HU), Eff-Z, and FAI(120kvp) increased along with the degree of CAD in general and were superior to the overall EAT attenuation for detecting the presence of CAD. Multivariate logistic regression analysis indicated that FAI(40keV) was the most powerful independent indicator (odds ratio 1.058, 95% CI 1.044–1.073; p < 0.001) of CAD among these parameters. Using an optimal cut-off (−131.8 HU), FAI(40keV) showed higher diagnostic accuracy of 80.6% compared with the other parameters. Conclusions: These preliminary findings suggest that FAI(40keV) on SDCT may be an appealing surrogate maker to allow monitoring of PCAT changes in the development of CAD.