Quantitative Histogram Analysis on Intracranial Atherosclerotic Plaques A High-Resolution Magnetic Resonance Imaging Study
Quantitative Histogram Analysis on Intracranial Atherosclerotic Plaques A High-Resolution Magnetic Resonance Imaging Study
复制标题
颅内动脉粥样硬化斑块的定量直方图分析
DOI：
10.1161/strokeaha.120.029062
复制
发表时间：
2020-07-01
期刊：
影响因子：
8.3
通讯作者：
Lu, Jianping
中科院分区：
文献类型：
--
作者：
Shi, Zhang;Li, Jing;Lu, Jianping
补充数字内容可在文本中找到。背景与目的：颅内动脉粥样硬化是脑卒中的主要原因之一，高分辨率磁共振成像提供了与缺血性事件风险相关的有用成像生物标志物。本研究旨在利用高分辨率磁共振成像评估罪魁祸首与非罪魁祸首颅内动脉粥样硬化直方图特征的差异。方法：选取2015年1月至2016年12月期间连续行高分辨率磁共振成像的颅内动脉粥样硬化患者247例。基于T2、T1和对比增强T1加权图像，分析定量特征，包括狭窄、斑块负荷、最小管腔面积、斑块内出血、增强比、信号强度弥散度（变异系数）。采用逐步回归分析确定区分罪魁祸首斑块和非罪魁祸首斑块的关键决定因素，并计算95% ci的优势比（or）。结果：共发现斑块190个，其中大脑中动脉斑块88个（罪魁祸首37个，非罪魁祸首51个），基底动脉斑块102个（罪魁祸首57个，非罪魁祸首45个）。近90%的罪魁祸首病变管腔狭窄程度<70%。多元logistic回归分析显示，斑块内出血（OR, 16.294 [95% CI, 1.043-254.632]， P=0.047）、最小管腔面积（OR, 1.468 [95% CI, 1.032-2.087]， P=0.033）和变异系数（OR, 13.425 [95% CI, 3.987-45.204]， P<0.001）是确定大脑中动脉罪魁祸首斑块的3个显著特征。增强比（OR, 9.476 [95% CI, 1.256 ~ 71.464]， P=0.029）、斑块内出血（OR, 2.847 [95% CI, 0.971 ~ 10.203]， P=0.046）和变异系数（OR, 10.068 [95% CI, 2.820 ~ 21.343]， P<0.001）与基底动脉斑块类型显著相关。变异系数是确定大脑中动脉和基底动脉斑块类型的一个强有力的独立预测因子，其敏感性、特异性和准确性分别为0.79、0.80和0.80。结论：高分辨率磁共振成像特征对颅内动脉粥样硬化的病变类型与管腔狭窄具有互补价值；直方图分析中信号强度的离散度是一个特别有效的预测参数。
Supplemental Digital Content is available in the text. Background and Purpose: Intracranial atherosclerosis is one of the main causes of stroke, and high-resolution magnetic resonance imaging provides useful imaging biomarkers related to the risk of ischemic events. This study aims to evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using high-resolution magnetic resonance imaging. Methods: Two hundred forty-seven patients with intracranial atherosclerosis who underwent high-resolution magnetic resonance imaging sequentially between January 2015 and December 2016 were recruited. Quantitative features, including stenosis, plaque burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and dispersion of signal intensity (coefficient of variation), were analyzed based on T2-, T1-, and contrast-enhanced T1-weighted images. Step-wise regression analysis was used to identify key determinates differentiating culprit and nonculprit plaques and to calculate the odds ratios (ORs) with 95% CIs. Results: In total, 190 plaques were identified, of which 88 plaques (37 culprit and 51 nonculprit) were located in the middle cerebral artery and 102 (57 culprit and 45 nonculprit) in the basilar artery. Nearly 90% of culprit lesions had a degree of luminal stenosis of <70%. Multiple logistic regression analyses showed that intraplaque hemorrhage (OR, 16.294 [95% CI, 1.043–254.632]; P=0.047), minimum luminal area (OR, 1.468 [95% CI, 1.032–2.087]; P=0.033), and coefficient of variation (OR, 13.425 [95% CI, 3.987–45.204]; P<0.001) were 3 significant features in defining culprit plaques in middle cerebral artery. The enhancement ratio (OR, 9.476 [95% CI, 1.256–71.464]; P=0.029), intraplaque hemorrhage (OR, 2.847 [95% CI, 0.971–10.203]; P=0.046), and coefficient of variation (OR, 10.068 [95% CI, 2.820–21.343]; P<0.001) were significantly associated with plaque type in basilar artery. Coefficient of variation was a strong independent predictor in defining plaque type for both middle cerebral artery and basilar artery with sensitivity, specificity, and accuracy being 0.79, 0.80, and 0.80, respectively. Conclusions: Features characterized by high-resolution magnetic resonance imaging provided complementary values over luminal stenosis in defined lesion type for intracranial atherosclerosis; the dispersion of signal intensity in histogram analysis was a particularly effective predictive parameter.