Why the study?
Coronary atherosclerosis is a chronic inflammatory condition and pericoronary adipose tissue attenuation relates to coronary inflammation, prompting investigation of its relationship with CAD using dual-layer spectral detector computed tomography.
Do pericoronary adipose tissue attenuation parameters derived from dual-layer spectral detector computed tomography predict the presence of coronary atherosclerotic heart disease?
Do pericoronary adipose tissue attenuation parameters derived from dual-layer spectral detector computed tomography predict the presence of coronary atherosclerotic heart disease?
Pericoronary adipose tissue attenuation parameters derived from dual-layer spectral detector CT can effectively distinguish between patients with and without coronary atherosclerotic plaques.
Elevated PCAT attenuation on SDCT marks high-risk plaques; leaves open whether it refines risk stratification or alters management.
Background: Coronary atherosclerosis is a chronic inflammatory condition. Pericoronary adipose tissue (PCAT) attenuation is closely related to coronary inflammation. This study aimed to investigate the relationship between PCAT attenuation parameters and coronary atherosclerotic heart disease (CAD) using dual-layer spectral detector computed tomography (SDCT). Methods: This cross-sectional study included eligible patients who underwent coronary computed tomography angiography using SDCT at the First Affiliated Hospital of Harbin Medical University between April 2021 and September 2021. Patients were classified as CAD (with coronary artery atherosclerotic plaque) or non-CAD (without coronary artery atherosclerotic plaque). Propensity score matching was used to match the two groups. The fat attenuation index (FAI) was used to quantify PCAT attenuation. The FAI was measured on conventional images (120 kVp) and virtual monoenergetic images (VMI) by semiautomatic software. The slope of the spectral attenuation curve (λ) was calculated. Regression models were established to evaluate the predictive value of PCAT attenuation parameters for CAD. Results: , and λ obtained the best performance (AUC =0.8296) of all the models. Conclusions: PCAT attenuation parameters obtained using dual-layer SDCT can aid in distinguishing patients with and without CAD. By detecting increases in PCAT attenuation parameters, it might be possible to predict the formation of atherosclerotic plaques before they appear.
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Zou et al. (2023) studied this question.
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