轻中度狭窄性大脑中动脉粥样硬化斑块特征与缺血性卒中责任斑块形成的相关性

目的  采用高分辨率磁共振血管壁成像(VW-HRMRI)分析导致轻中度狭窄的大脑中动脉粥样硬化斑块特征，探讨其与责任斑块形成的相关性及临床诊断价值。方法  回顾性分析于徐州市中心医院就诊的大脑中动脉轻中度狭窄的有症状患者（n= 33）和无症状患者（n=19），所有患者均行VW-HRMRI检查，共识别出68个斑块，将识别出的斑块分为责任斑块组（n=33）与非责任斑块组（n=35）。使用二元Logistic回归分析和ROC曲线分析斑块特征与缺血性卒中发生的关联及临床诊断价值。结果  相较于非责任斑块，责任斑块表现出更高的重构指数(P=0.03)与强化等级(P=0.01)以及更大的斑块体积(P=0.003)与陡峭度(P= 0.002)，二元Logistic回归分析显示，正性重构（OR=5.400；95% CI：1.334~21.857）、强化等级（OR=4.431，95% CI：1.750~ 11.219）、斑块体积(OR=1.09，95% CI：1.014~1.171)及陡峭度(OR=1.122，95% CI：1.038~1.214)均与责任斑块独立相关。ROC曲线分析显示，正性重构、强化等级、斑块体积与陡峭度4种危险因素的曲线下面积分别0.631、0.696、0.705、0693，而预测模型（4种危险因素联合）的曲线下面积为0.875，敏感度和特异性分别为0.818与0.800。结论  正性重构、强化等级、斑块体积与陡峭度为责任斑块的独立危险因素，4种影像特征联合应用能更好的预测责任斑块的发生，为及早进行临床治疗提供影像依据。

Correlation between atherosclerotic plaque characteristics of mild-to-moderate stenotic middle cerebral artery and ischemic stroke responsible plaque

Objective To analyze the characteristics of atherosclerotic plaques that lead to mild to moderate narrowing in the middle cerebral artery, utilizing high-resolution magnetic resonance imaging of the vascular wall (VW-HRMRI), and explore the connection between these characteristics and the formation of culprit plaques, in addition to their clinical diagnostic significance. Methods A retrospective analysis was performed on both symptomatic (n=33) and asymptomatic (n=19) patients experiencing mild to moderate stenosis of the middle cerebral artery at Xuzhou Central Hospital. VW-HRMRI was employed to examine each patient, resulting in the identification of 68 plaques. These plaques were then classified into two categories: those deemed to be culprit plaques (n=33) and those identified as non-culprit plaques (n=35). Binary Logistic regression analysis and ROC curve analysis were applied to determine the relationship between the characteristics of these plaques and the risk of ischemic stroke, as well as their clinical diagnostic relevance. Results Culprit plaques were identified by notably higher remodeling indices (P=0.03), enhancement grades (P=0.01), volumes (P=0.003), and slopes (P=0.002) in comparison to non-culprit plaques. Logistic regression analysis showed that positive remodeling (OR=5.400, 95% CI: 1.334-21.857), enhancement grade (OR=4.431, 95% CI: 1.750-11.219), plaque volume (OR=1.09, 95% CI: 1.004-1.171), and slope (OR=1.112, 95% CI: 1.038-1.214) were independently associated with the occurrence of culprit plaques. The AUC for the four risk factors, which include positive remodeling, enhancement grade, plaque volume, and slope, was measured at 0.631, 0.696, 0.705 and 0.693. Subsequently, the predictive model that integrates these risk factors reached an AUC of 0.875, showing a sensitivity of 0.818 and a specificity of 0.800. Conclusion Positive remodeling, enhancement grade, plaque volume, and slope stand as independent risk factors for culprit plaques. The collective use of these four imaging features significantly enhances the predictive accuracy for identifying culprit plaques, thereby offering crucial imaging-based evidence to support early clinical interventions.