颅内动脉责任斑块的3D-HRVWI组学特征联合斑块内出血对缺血性卒中患者复发的预测效能研究  

The predictive performance of 3D-HRVWI radiomics features of intracranial arterial culprit plaque combined with intraplaque hemorrhage in predicting recurrence in patients with ischemic stroke

作　　者：曹婷婷 潘兆烨 赵雨薇 冯秋豪 范羡 陆钰 姜洪标[1] 朱丽[1] 王天乐[1] CAO Tingting;PAN Zhaoye;ZHAO Yuwei;FENG Qiuhao;FAN Xian;LU Yu;JIANG Hongbiao;ZHU Li;WANG Tianle(Department of Radiology,Nantong First People's Hospital,Nantong 226001,China)

机构地区：[1]南通市第一人民医院放射科,南通226001

出　　处：《磁共振成像》2025年第3期24-30,50,共8页Chinese Journal of Magnetic Resonance Imaging

基　　金：南通市科技计划项目(编号:MS2023068);南通市卫生健康委员会科研课题项目(编号:MS2024026)。

摘　　要：目的将颅内动脉责任斑块在3D-高分辨率血管壁成像(three-dimensional high-resolution vessel wall imaging,3D-HRVWI)的影像组学特征与斑块内出血(intraplaque hemorrhage,IPH)联合,构建颅内动脉粥样硬化性卒中患者复发的预测模型,从而帮助临床对高风险人群采取针对性的干预措施以降低未来卒中复发的风险。材料与方法回顾性分析2021年11月至2023年8月接受HRVWI检查的脑卒中患者病例296例,在296例患者的平扫序列T1WI和增强序列CE-T1WI图像中测量责任斑块的影像学特征,并勾画斑块、提取影像组学特征,通过特征相关性分析和基于L1正则化的特征筛选(linear models penalized with the L1norm,L1 Based)筛选组学特征,所有数据按7∶3比例随机分为训练组和测试组。在训练组中,将筛选出的责任斑块的放射组学特征用于构建用于预测卒中复发的影像组学模型,将斑块组学特征与IPH构建联合模型,并在测试组中评估其性能。使用受试者工作曲线(receiver operating curve,ROC)和曲线下面积(area under the curve,AUC)评估各模型的预测效能,采用DeLong检验比较AUC之间的差异,最后建立列线图可视化模型。结果参与者平均年龄为66岁,包括207例男性参与者(69.9%)和89例女性参与者(30.1%),其中卒中复发患者58例(19.6%)。单因素和多因素分析显示,临床特征和责任斑块的放射学特征中只有IPH[优势比(odds ratio,OR)=8.577,95%CI:4.374~16.818]是卒中复发的独立危险因素。在CE-T1WI序列和T1WI序列中分别提取了2153个责任斑块的放射组学特征,经过特征筛选后,CE-T1WI数据中保留了4个放射组学特征,T1WI数据中保留了6个放射组学特征。在训练组中,IPH的AUC为0.757(0.693~0.814),斑块组学特征的AUC为0.770(0.707~0.826),联合模型的AUC为0.866(0.811~0.909)。在测试组中,IPH的AUC为0.750(0.647~0.836),斑块组学特征的AUC为0.819(0.723~0.892),联合模型的AUC为0.880(0.794~0.939)。DeLong检验结�Objective:To construct a prediction model for recurrence of intracranial atherosclerotic stroke patients by combining the radiomic features of intracranial culprit plaques in three-dimensional high-resolution vessel wall imaging with MRI(3D-HRVWI) and intraplaque hemorrhage(IPH).This can help clinically target targeted interventions for high-risk populations to reduce the risk of future stroke recurrence.Materials and Methods:A total of 296 stroke patients who underwent HRVWI examination from November 2021 to August 2023 were retrospectively collected,and the imaging features of culprit plaques were measured in the non-contrast sequence T1WI and enhanced sequence CE-T1WI images of 296 patients,and the plaques were delineated,the radiomics features were extracted,and the feature correlation analysis and feature screening based on L1 regularization(linear models penalized with the L1norm,L1Based) screened radiomics features,and all data were randomly divided into training group and test group in a 7:3 ratio.In the training group,the radiomics features of the screened responsible plaques were used to construct a radiomics model for predicting stroke recurrence,and the radiomics features and IPH were used to construct a combined model,and the performance was evaluated in the test group.The receiver operating curve(ROC) and area under the curve(AUC) were used to evaluate the predictive performance of each model,and the DeLong test was used to compare the differences between AUC,and finally the nomogram visualization model was established.Results:The mean age of the participants was 66 years,including 207 male participants(69.9%) and 89 female participants(30.1%),of whom 58(19.6%) had recurrent stroke.IPH(OR = 8.577,95% CI:4.374 to 16.818) was an independent risk factor for stroke recurrence among the clinical features and radiographic features of the culprit plaques.The radiomics features of 2153 culprit plaques were extracted from the CE-T1WI and T1WI sequences,respectively,and after feature screening,4 radiomics fea

关 键 词：颅内动脉粥样硬化性疾病 高分辨率血管壁成像 磁共振成像 影像组学 斑块内出血 卒中复发 

分 类 号：R445.2[医药卫生—影像医学与核医学] R743.3[医药卫生—诊断学]