OBJECTIVE: To identify a set of computed tomographic (CT) features of carotid atherosclerotic plaques that is significantly associated with ischemic stroke. METHODS: In a cross-sectional study, we retrospectively identified 136 consecutive patients admitted to our emergency department with suspected stroke who underwent a CT-angiogram of the carotid arteries. CT-angiographic studies of the carotid arteries were processed automatically using automated computer classifier algorithm that quantitatively assesses a battery of carotid CT features. Acute stroke patients were categorized into "acute carotid stroke patients" and "nonacute carotid stroke patients" independent of carotid wall CT features, using the Causative Classification System for Ischemic Stroke, which includes the neuroradiologist's review of the imaging studies of the brain parenchyma and of the degree of carotid stenosis, and charted test results (such as electrocardiogram). Univariate followed by multivariate analyses were used to build models to differentiate between these patient groups and to differentiate between the infarct and unaffected sides in the "acute carotid stroke patients." RESULTS: Forty "acute carotid stroke" patients and 50 "nonacute carotid stroke" patients were identified. Multivariate modeling identified a small number of the carotid wall CT features that were significantly associated with acute carotid stroke, including wall volume, fibrous cap thickness, number and location of lipid clusters, and number of calcium clusters. INTERPRETATION: Patients with acute carotid stroke demonstrate significant differences in the appearance of their carotid wall ipsilateral to the side of their infarct, when compared with either nonacute carotid stroke patients or the carotid wall contralateral with the infarct side.
OBJECTIVE: To identify a set of computed tomographic (CT) features of carotid atherosclerotic plaques that is significantly associated with ischemic stroke. METHODS: In a cross-sectional study, we retrospectively identified 136 consecutive patients admitted to our emergency department with suspected stroke who underwent a CT-angiogram of the carotid arteries. CT-angiographic studies of the carotid arteries were processed automatically using automated computer classifier algorithm that quantitatively assesses a battery of carotid CT features. Acute strokepatients were categorized into "acute carotid strokepatients" and "nonacute carotid stroke patients" independent of carotid wall CT features, using the Causative Classification System for Ischemic Stroke, which includes the neuroradiologist's review of the imaging studies of the brain parenchyma and of the degree of carotid stenosis, and charted test results (such as electrocardiogram). Univariate followed by multivariate analyses were used to build models to differentiate between these patient groups and to differentiate between the infarct and unaffected sides in the "acute carotid strokepatients." RESULTS: Forty "acute carotid stroke" patients and 50 "nonacute carotid stroke" patients were identified. Multivariate modeling identified a small number of the carotid wall CT features that were significantly associated with acute carotid stroke, including wall volume, fibrous cap thickness, number and location of lipid clusters, and number of calcium clusters. INTERPRETATION:Patients with acute carotid stroke demonstrate significant differences in the appearance of their carotid wall ipsilateral to the side of their infarct, when compared with either nonacute carotid stroke patients or the carotid wall contralateral with the infarct side.
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