BACKGROUND AND PURPOSE: The presence of IPH and/or FCR in the carotid atherosclerotic plaque indicates a high-risk lesion. The aim of this multicenter cross-sectional study was to establish the characteristics of lesions that may precede IPH and/or FCR. We further sought to construct a CAS that stratifies carotid disease severity. MATERIALS AND METHODS: Three hundred forty-four individuals from 4 imaging centers with 16%-99% carotid stenosis by duplex sonography underwent carotid MR imaging. In approximately 60% of the study sample (training group), multivariate analysis was used to determine factors associated with IPH and FCR. Statistically significant parameters identified during multivariate analysis were used to construct CAS. CAS was then applied to the remaining arteries (40%, test group), and the accuracy of classification for determining the presence versus absence of IPH or, separately, FCR was determined by ROC analysis and calculation of the AUC. RESULTS: The maximum proportion of the arterial wall occupied by the LRNC was the strongest predictor of IPH (P < .001) and FCR (P < .001) during multivariate analysis of the training group. The subsequently derived CAS applied to the test group was an accurate classifier of IPH (AUC = 0.91) and FCR (AUC = 0.93). Compared with MRA stenosis, CAS was a stronger classifier of both IPH and FCR. CONCLUSIONS: LRNC quantification may be an effective complementary strategy to stenosis for classifying carotid atherosclerotic disease severity. CAS forms the foundation for a simple imaging-based risk-stratification system in the carotid artery to classify severity of atherosclerotic disease.
BACKGROUND AND PURPOSE: The presence of IPH and/or FCR in the carotid atherosclerotic plaque indicates a high-risk lesion. The aim of this multicenter cross-sectional study was to establish the characteristics of lesions that may precede IPH and/or FCR. We further sought to construct a CAS that stratifies carotid disease severity. MATERIALS AND METHODS: Three hundred forty-four individuals from 4 imaging centers with 16%-99% carotid stenosis by duplex sonography underwent carotid MR imaging. In approximately 60% of the study sample (training group), multivariate analysis was used to determine factors associated with IPH and FCR. Statistically significant parameters identified during multivariate analysis were used to construct CAS. CAS was then applied to the remaining arteries (40%, test group), and the accuracy of classification for determining the presence versus absence of IPH or, separately, FCR was determined by ROC analysis and calculation of the AUC. RESULTS: The maximum proportion of the arterial wall occupied by the LRNC was the strongest predictor of IPH (P < .001) and FCR (P < .001) during multivariate analysis of the training group. The subsequently derived CAS applied to the test group was an accurate classifier of IPH (AUC = 0.91) and FCR (AUC = 0.93). Compared with MRA stenosis, CAS was a stronger classifier of both IPH and FCR. CONCLUSIONS: LRNC quantification may be an effective complementary strategy to stenosis for classifying carotid atherosclerotic disease severity. CAS forms the foundation for a simple imaging-based risk-stratification system in the carotid artery to classify severity of atherosclerotic disease.
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