Detection of Dolichoectasia and Atherosclerosis by Automated MRA Tortuosity Metrics in a Population-Based Study.
Detection of Dolichoectasia and Atherosclerosis by Automated MRA Tortuosity Metrics in a Population-Based Study.
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在一项基于人群的研究中，通过自动 MRA 扭曲度指标检测纤维扩张和动脉粥样硬化。
DOI：
10.1002/jmri.28923
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Caughey,MelissaC
中科院分区：
文献类型：
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作者：
Zhou,Shang;Qiao,Ye;Zhou,Xinwei;Wasserman,BruceA;Caughey,MelissaC
背景颅内血管迂曲是长扩张症的一个重要组成部分，与动脉粥样硬化和不良神经功能结局相关。然而，目前对弯曲度的评价主要是描述性的，目的比较三种自动化弯曲度度量方法的性能（角度度量[AM]、距离度量[DM]和轴向距离度量[DTA]）用于检测长扩张和节段特异性斑块的存在。研究类型观察性横截面度量评估。平均年龄= 76岁，女性= 59%，黑人= 29%。场强/序列3-T、三维（3D）飞行时间MRA和3D血管壁MRI。评估对颈内动脉、大脑中动脉、大脑前动脉、大脑后动脉、椎动脉、和基底动脉（BA）的全长。根据吸烟者的视觉标准对BA长扩张的定性解释进行评估。统计学检验用于组间比较的描述性统计量（2样本检验，Pearson卡方检验）。用于检测BA长扩张或节段特异性斑块的受试者工作特征曲线下面积（AUC）。模型输入包括1）迂曲度指标，2）平均管腔面积，和3）人口统计学（年龄，种族和性别）。结果定性长扩张被确定在336（18%）的参与者，和动脉粥样硬化斑块检测192（10%）的参与者。AM-、DM-和DTA-计算的迂曲度是基底动脉延长扩张的良好个体判别因素（AUC分别为0.76、0.74和0.75），模型性能随平均管腔面积的增加而改善：（AUC分别为0.88、0.87和0.87）。组合特征（迂曲度和平均管腔面积）识别出的斑块在前壁表现较好。（AUC范围为0.66至0.78）（AUC范围为0.54 - 0.65）循环，所有的模型都通过增加人口统计数据来改进（AUC范围从0.62到0.84）.Data ConclusionQuantitative vessel tortuosity metrics produce good diagnosis accuracy for the detection of lichoectasia.Level of Evidence 1 Technical Efficacy Stage2
BackgroundIntracranial vessel tortuosity is a key component of dolichoectasia and has been associated with atherosclerosis and adverse neurologic outcomes. However, the evaluation of tortuosity is mainly a descriptive assessment.PurposeTo compare the performance of three automated tortuosity metrics (angle metric [AM], distance metric [DM], and distance‐to‐axis metric [DTA]) for detection of dolichoectasia and presence of segment‐specific plaques.Study TypeObservational, cross‐sectional metric assessment.Population1899 adults from the general population; mean age = 76 years, female = 59%, and black = 29%.Field Strength/Sequence3‐T, three‐dimensional (3D) time‐of‐flight MRA and 3D vessel wall MRI.AssessmentTortuosity metrics and mean luminal area were quantified for designated segments of the internal carotid artery, middle cerebral artery, anterior cerebral artery, posterior cerebral artery, vertebral artery, and entire length of basilar artery (BA). Qualitative interpretations of BA dolichoectasia were assessed based on Smoker's visual criteria.Statistical TestsDescriptive statistics (2‐samplet‐tests, Pearson chi‐square tests) for group comparisons. Receiver operating characteristics area under the curve (AUC) for detection of BA dolichoectasia or segment‐specific plaque. Model inputs included 1) tortuosity metrics, 2) mean luminal area, and 3) demographics (age, race, and sex).ResultsQualitative dolichoectasia was identified in 336 (18%) participants, and atherosclerotic plaques were detected in 192 (10%) participants. AM‐, DM‐, and DTA‐calculated tortuosity were good individual discriminators of basilar dolichoectasia (AUCs: 0.76, 0.74, and 0.75, respectively), with model performance improving with the mean lumen area: (AUCs: 0.88, 0.87, and 0.87, respectively). Combined characteristics (tortuosity and mean luminal area) identified plaques with better performance in the anterior (AUCs ranging from 0.66 to 0.78) than posterior (AUCs ranging from 0.54 to 0.65) circulation, with all models improving by the addition of demographics (AUCs ranging from 0.62 to 0.84).Data ConclusionQuantitative vessel tortuosity metrics yield good diagnostic accuracy for the detection of dolichoectasia.Level of Evidence1Technical Efficacy Stage2