Key result
Combining stenosis, plaque burden, and PVAT Zeff accurately identifies symptomatic carotid plaques with an AUC of ~0.85.
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Why the study?
Accurate identification of symptomatic carotid plaques requires improved models combining plaque morphology and perivascular adipose tissue parameters from DLCT.
Does the addition of dual-layer spectral detector computed tomography-derived perivascular adipose tissue parameters to conventional plaque morphology improve the identification of symptomatic carotid atherosclerotic plaques?
Observational (n=256)
No
Does the addition of dual-layer spectral detector computed tomography-derived perivascular adipose tissue parameters to conventional plaque morphology improve the identification of symptomatic carotid atherosclerotic plaques?
Effect estimate: AUC 0.848 in test set
The addition of DLCT-derived PVAT Zeff to conventional plaque characteristics improves the discrimination of symptomatic carotid atherosclerotic plaques.
OBJECTIVES To establish and validate a multidimensional model for identifying symptomatic carotid plaques based on the combination of plaque morphological characteristics and perivascular adipose tissue (PVAT) parameters obtained from dual-layer spectral detector computed tomography (DLCT). METHODS This single-center study included 256 patients with carotid atherosclerosis. Patients were divided into symptomatic and asymptomatic groups based on the presence or absence of transient ischemic attack or ipsilateral ischemic stroke within 2 weeks before DLCT. The study comprised a retrospective training cohort (n = 133), a retrospective internal validation cohort (n = 58), and a prospectively enrolled independent temporal test cohort (n = 65). Conventional plaque characteristics and multiple PVAT parameters were compared between the two groups. Imaging variables independently associated with symptomatic carotid plaques were identified using multivariable logistic regression and incorporated into classification models. RESULTS Degree of stenosis, plaque burden, and the effective atomic number (Zeff) of PVAT were independently associated with symptomatic carotid plaques. Model 2 incorporated all three parameters demonstrating strong discriminatory performance, with areas under the receiver operating characteristic curves of 0.907, 0.825, and 0.848 in the training, validation, and test sets, respectively. Incorporation of PVAT Zeff improved the discriminative performance and reclassification beyond conventional stenosis and plaque-burden assessment. CONCLUSIONS Degree of stenosis, plaque burden, and PVAT Zeff were independently associated with symptomatic carotid plaques. Adding PVAT Zeff to conventional plaque characteristics improved model discrimination and provided complementary information for the characterization of symptomatic lesions.
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Chi et al. (2026) conducted an observational in Carotid atherosclerosis (n=256). Perivascular adipose tissue effective atomic number (PVAT Zeff) combined with plaque morphology vs. Conventional plaque characteristics (degree of stenosis and plaque burden) was evaluated on Discriminatory performance for identifying symptomatic carotid plaques (AUC 0.848 in test set). A model incorporating degree of stenosis, plaque burden, and PVAT Zeff demonstrated strong discriminatory performance for identifying symptomatic carotid plaques (AUC 0.848 in the test set).
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