[Carotid plaque composition and volume evaluated by multi-detector computed tomography angiography].

Area of Science:

• Vascular imaging and diagnostics
• Cardiovascular research
• Neurology and stroke prevention

Background:

• Carotid artery stenosis is a significant risk factor for ischemic stroke.
• Accurate plaque characterization is crucial for risk stratification.
• Multi-detector computed tomography angiography (MDCTA) offers non-invasive imaging of carotid plaques.

Purpose of the Study:

• To compare plaque composition and volume between symptomatic and asymptomatic patients with carotid artery stenosis.
• To evaluate the utility of MDCTA in differentiating plaque types.
• To identify imaging biomarkers for stroke risk in carotid artery stenosis.

Main Methods:

• Retrospective analysis of 78 patients with internal carotid artery stenosis ≥70% diagnosed by digital subtraction angiography (DSA).
• MDCTA was used to analyze carotid plaque composition, categorizing into calcified, fibrous, and lipid-rich necrotic core (LRNC) plaques based on Hounsfield units (HU).
• Statistical methods were employed to compare plaque characteristics between symptomatic and asymptomatic patient groups.

Main Results:

• Asymptomatic patients showed a significantly higher proportion of calcified plaques and a lower proportion of LRNC plaques compared to symptomatic patients.
• Multivariate analysis confirmed a positive correlation between calcified plaque proportion and asymptomatic stenosis, and a negative correlation for LRNC plaque proportion.
• A LRNC plaque proportion exceeding 30.3% demonstrated high specificity (94.3%) for identifying symptomatic carotid artery stenosis.

Conclusions:

• Asymptomatic carotid artery stenosis is associated with a higher proportion of calcified plaques and a lower proportion of LRNC plaques.
• MDCTA can non-invasively assess carotid plaque composition, aiding in risk stratification for patients with carotid artery stenosis.
• The proportion of LRNC plaques may serve as a clinically useful cutoff for identifying high-risk symptomatic lesions.