Adraktas Dionesia D, Tong Elizabeth, Furtado Andre D, Cheng Su-Chun, Wintermark Max
Department of Radiology, Neuroradiology Division, University of California, San Francisco, CA.
J Neuroimaging. 2014 Jan-Feb;24(1):1-6. doi: 10.1111/j.1552-6569.2012.00705.x. Epub 2012 Sep 17.
The purpose of this study was to identify imaging markers and clinical risk factors that significantly predict the evolution of computed tomography (CT) imaging features of carotid artery atherosclerotic disease over a 1-year period.
Our prospective study involved 120 consecutive patients undergoing emergent CT evaluation for symptoms of acute stroke. These patients were asked to consent to a follow-up CT exam in 1 year. To evaluate for atherosclerotic plaque, both at baseline and on follow-up, we employed a comprehensive computed tomography angiography (CTA) protocol that captured the carotid, vertebral, aortic, and coronary arteries. To further evaluate carotid artery plaque components, we used an automated classifier computer algorithm that distinguishes among the histological components of the carotid artery wall (lipids, calcium, fibrous tissue) based on appropriate thresholds of CT density. Baseline values of carotid imaging features and clinical variables were assessed for their ability to significantly predict changes in these imaging features over 1 year.
Of these 120 consecutive patients, 17 received both a baseline and a follow-up CTA exam. Wall volume increased more when the largest lipid cluster was located close to the lumen (coefficient -7.61, -13.83 to -1.40, P = .016). The volume of lipid increased with age (coefficient .36, .21 to .50, P = .000), in smokers (coefficient 8.89, 6.82 to 10.95, P = .000) and when fewer lipid clusters were present at baseline (coefficient -0.11, -0.17 to -.04, P = .001). The volume of calcium increased with greater volume of lipid at baseline (coefficient .35, .02 to .68, P = .035) and in patients on statins (coefficient 4.79, 1.73 to 7.86, P = .002).
There are a number of imaging markers and risk factors that significantly predict the evolution of CT imaging features of carotid artery atherosclerotic disease over a 1-year period.
本研究的目的是确定在1年时间内能够显著预测颈动脉粥样硬化疾病计算机断层扫描（CT）成像特征演变的成像标志物和临床危险因素。
我们的前瞻性研究纳入了120例因急性中风症状而接受紧急CT评估的连续患者。这些患者被要求同意在1年后进行随访CT检查。为了在基线期和随访期评估动脉粥样硬化斑块，我们采用了一种全面的计算机断层扫描血管造影（CTA）方案，该方案可采集颈动脉、椎动脉、主动脉和冠状动脉的图像。为了进一步评估颈动脉斑块成分，我们使用了一种自动分类计算机算法，该算法根据CT密度的适当阈值区分颈动脉壁的组织学成分（脂质、钙、纤维组织）。评估颈动脉成像特征和临床变量的基线值预测这些成像特征在1年内变化的能力。
在这120例连续患者中，17例接受了基线期和随访期的CTA检查。当最大脂质簇靠近管腔时，管壁体积增加更为明显（系数为-7.61，-13.83至-1.40，P = 0.016）。脂质体积随年龄增长而增加（系数为0.36，0.21至0.50，P = 0.000），在吸烟者中增加（系数为8.89，6.82至10.95，P = 0.000），并且在基线期脂质簇较少时增加（系数为-0.11，-0.17至-0.04，P = 0.001）。钙的体积在基线期脂质体积较大时增加（系数为0.35，0.02至0.68，P = 0.035），在服用他汀类药物的患者中增加（系数为4.79，1.73至7.86，P = 0.002）。
有许多成像标志物和危险因素能够显著预测颈动脉粥样硬化疾病CT成像特征在1年时间内的演变。
