Abstract：
Intracranial 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.
To 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.
Observational, cross-sectional metric assessment.
1899 adults from the general population; mean age = 76 years, female = 59%, and black = 29%.
3-T, three-dimensional (3D) time-of-flight MRA and 3D vessel wall MRI.
Tortuosity 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.
Descriptive statistics (2-sample t-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).
Qualitative 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).
Quantitative vessel tortuosity metrics yield good diagnostic accuracy for the detection of dolichoectasia.
1 TECHNICAL EFFICACY STAGE: 2.
To 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.
Observational, cross-sectional metric assessment.
1899 adults from the general population; mean age = 76 years, female = 59%, and black = 29%.
3-T, three-dimensional (3D) time-of-flight MRA and 3D vessel wall MRI.
Tortuosity 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.
Descriptive statistics (2-sample t-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).
Qualitative 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).
Quantitative vessel tortuosity metrics yield good diagnostic accuracy for the detection of dolichoectasia.
1 TECHNICAL EFFICACY STAGE: 2.
摘要：
背景：颅内血管扭曲是毛细血管扩张症的关键组成部分，并与动脉粥样硬化和不良神经系统结局相关。然而,弯曲度的评价主要是描述性评价。
目的：比较三种自动弯曲度度量（角度度量[AM]，距离度量[DM]，和距离到轴的度量[DTA]），用于检测纤维扩张和节段特异性斑块的存在。
方法：观察性，横截面度量评估。
方法：来自普通人群的1899名成年人；平均年龄=76岁，女性=59%，黑色=29%。
■3-T,三维（3D）飞行时间MRA和3D血管壁MRI。
结果：对颈内动脉指定段的弯曲度指标和平均管腔面积进行了量化，大脑中动脉,大脑前动脉,大脑后动脉,椎动脉,和基底动脉（BA）的整个长度。根据Smoker的视觉标准评估了BA的定性解释。
方法：描述性统计(2样本t检验，Pearson卡方检验)用于组比较。用于检测BA舒张性扩张或节段特异性斑块的接收器操作特征曲线下面积(AUC)。模型输入包括1)弯曲度度量，2)平均管腔面积，和3)人口统计(年龄，种族,和性别）。
结果：在336名（18％）参与者中发现了定性的扩张扩张，在192名(10%)参与者中检测到动脉粥样硬化斑块.AM-,DM-,和DTA计算的弯曲度是基底动脉扩张症的良好个体鉴别器（AUC分别为0.76、0.74和0.75），模型性能随平均管腔面积而改善：（AUC：分别为0.88、0.87和0.87）。结合特征（弯曲度和平均管腔面积）确定的斑块在前循环（AUC范围为0.66至0.78）比后循环（AUC范围为0.54至0.65）表现更好，随着人口统计学的增加，所有模型都得到了改善(AUC范围从0.62到0.84)。
结论：定量血管弯曲度指标对血管扩张症的检测具有良好的诊断准确性。
方法：1技术效率阶段：2.
目的：比较三种自动弯曲度度量（角度度量[AM]，距离度量[DM]，和距离到轴的度量[DTA]），用于检测纤维扩张和节段特异性斑块的存在。
方法：观察性，横截面度量评估。
方法：来自普通人群的1899名成年人；平均年龄=76岁，女性=59%，黑色=29%。
■3-T,三维（3D）飞行时间MRA和3D血管壁MRI。
结果：对颈内动脉指定段的弯曲度指标和平均管腔面积进行了量化，大脑中动脉,大脑前动脉,大脑后动脉,椎动脉,和基底动脉（BA）的整个长度。根据Smoker的视觉标准评估了BA的定性解释。
方法：描述性统计(2样本t检验，Pearson卡方检验)用于组比较。用于检测BA舒张性扩张或节段特异性斑块的接收器操作特征曲线下面积(AUC)。模型输入包括1)弯曲度度量，2)平均管腔面积，和3)人口统计(年龄，种族,和性别）。
结果：在336名（18％）参与者中发现了定性的扩张扩张，在192名(10%)参与者中检测到动脉粥样硬化斑块.AM-,DM-,和DTA计算的弯曲度是基底动脉扩张症的良好个体鉴别器（AUC分别为0.76、0.74和0.75），模型性能随平均管腔面积而改善：（AUC：分别为0.88、0.87和0.87）。结合特征（弯曲度和平均管腔面积）确定的斑块在前循环（AUC范围为0.66至0.78）比后循环（AUC范围为0.54至0.65）表现更好，随着人口统计学的增加，所有模型都得到了改善(AUC范围从0.62到0.84)。
结论：定量血管弯曲度指标对血管扩张症的检测具有良好的诊断准确性。
方法：1技术效率阶段：2.
