Key result
Spectral CT perivascular adipose tissue metrics linked to ~17% higher odds of symptomatic carotid plaques.
Why the study?
To evaluate the association between CT spectral parameters of perivascular adipose tissue and carotid plaque composition, and assess their value for identifying symptomatic plaques.
Do spectral CT-derived PVAT parameters improve noninvasive identification of symptomatic carotid atherosclerosis beyond luminal stenosis?
Observational (n=306)
Single-blind
No
Do spectral CT-derived PVAT parameters improve noninvasive identification of symptomatic carotid atherosclerosis beyond luminal stenosis?
Spectral CT-derived PVAT parameters, particularly fat fraction and CT70keV, significantly improve the noninvasive identification of symptomatic carotid atherosclerosis when added to luminal stenosis and plaque composition.
Answers are grounded in this paper and cite their sources.
May aid noninvasive identification of symptomatic carotid plaques; leaves open prospective validation before clinical use.
Objectives To evaluate the association between computed tomography (CT) spectral parameters of perivascular adipose tissue (PVAT) and carotid plaque composition, and to assess their value for identifying symptomatic plaques. Materials and methods In this study, 306 consecutive patients with computed tomography angiography (CTA)-confirmed carotid atherosclerosis who underwent head and neck spectral CT angiography were analyzed. Quantitative plaque parameters and PVAT spectral metrics were extracted. Correlations between quantitative plaque parameters and PVAT spectral indices were analyzed, and logistic regression was used to identify independent predictors of symptomatic plaques. Diagnostic performance was evaluated by generating receiver operating characteristic (ROC) curves. Results Compared with asymptomatic patients, symptomatic carotid plaques showed higher PVAT effective atomic number ( Z eff) and iodine concentration (IC) but lower fat fraction (FF) (all p < 0.05). Z eff and IC correlated positively with fibrous fatty and necrotic core volumes, whereas FF correlated negatively with necrotic core volume (all p < 0.001). In multivariable models, Z eff, IC, FF, and virtual monoenergetic image attenuation at 70 keV (CT 70keV ) were independently associated with symptomatic plaques (all p < 0.05). CT 70keV and FF performed best for identifying symptomatic plaques (AUC 0.857 and 0.820). The AUC increased from 0.716 for stenosis severity alone to 0.821 after the addition of necrotic core volume, and further to 0.897 and 0.916 after the addition of FF and CT 70keV , respectively. Conclusion Spectral CT-derived PVAT parameters differ between symptomatic and asymptomatic carotid atherosclerosis and are associated with plaque composition, providing complementary noninvasive information for identifying symptomatic plaques. Key Points Question: Can spectral CT-derived PVAT parameters improve noninvasive identification of symptomatic carotid atherosclerosis beyond luminal stenosis? Findings: PVAT spectral parameters differed between symptomatic and asymptomatic plaques, and adding FF or CT 70keV improved diagnostic performance. Critical relevance statement: spectral parameters of carotid PVAT differ significantly between symptomatic and asymptomatic carotid atherosclerosis and are associated with plaque components.
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Zheng et al. (2026) conducted an observational in Carotid atherosclerosis (n=306). Spectral CT parameters of perivascular adipose tissue (PVAT) vs. Asymptomatic carotid plaques was evaluated on Independent association of virtual monoenergetic image attenuation at 70 keV (CT70keV) with symptomatic carotid plaques (OR 1.173, 95% CI 1.032-1.333, p=0.015). Spectral CT-derived perivascular adipose tissue parameters, specifically CT70keV and fat fraction, were independently associated with symptomatic carotid plaques and improved diagnostic performance.
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