Department of Radiology, Vascular Imaging Lab, University of Washington, 815 Mercer Street, Seattle, WA 98109, USA.
AJNR Am J Neuroradiol. 2010 Jun;31(6):1068-75. doi: 10.3174/ajnr.A2007. Epub 2010 Jan 21.
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.
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.
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.
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.
颈动脉硬化斑块中存在 IPH 和/或 FCR 表明存在高危病变。本多中心横断面研究旨在确定可能先于 IPH 和/或 FCR 出现的病变特征。我们进一步寻求构建一种 CAS,以分层颈动脉疾病严重程度。
来自 4 个影像学中心的 344 名患者通过双功能超声检查发现存在 16%-99%的颈动脉狭窄,他们接受了颈动脉磁共振成像检查。在大约 60%的研究样本(训练组)中,采用多元分析来确定与 IPH 和 FCR 相关的因素。在多元分析中确定的统计学上显著的参数被用于构建 CAS。然后将 CAS 应用于其余动脉(40%,测试组),并通过 ROC 分析和 AUC 计算来确定分类的准确性,以确定 IPH 或 FCR 的存在与否。
在训练组的多元分析中,LRNC 占据动脉壁的最大比例是预测 IPH(P<0.001)和 FCR(P<0.001)的最强因素。随后衍生的 CAS 应用于测试组,是 IPH(AUC=0.91)和 FCR(AUC=0.93)的准确分类器。与 MRA 狭窄相比,CAS 是 IPH 和 FCR 的更强分类器。
LRNC 定量可能是一种有效的补充策略,可用于对颈动脉粥样硬化疾病严重程度进行分类,而不仅仅是基于狭窄程度。CAS 为基于成像的简单风险分层系统奠定了基础,用于对颈动脉粥样硬化疾病的严重程度进行分类。