磁共振成像2025，Vol.16Issue(8)：32-40,9.DOI:10.12015/issn.1674-8034.2025.08.006
基于MRI的影像组学模型及临床因素模型对缺血性脑卒中溶栓后出血转化风险的价值
Value of integrated MRI radiomics and clinical factors for post-thrombolytic hemorrhagic transformation in acute ischemic stroke
摘要
Abstract
Objective:To investigate the predictive value of MRI-based radiomics models and clinical factor models for hemorrhagic transformation(HT)risk after thrombolysis in acute ischemic stroke(AIS).Materials and Methods:Clinical and imaging data were retrospectively collected from 730 AIS patients at first presentation across two Centers.Data from Center 1 were randomly divided into a training set(436 cases)and an internal validation set(188 cases)in a 7:3 ratio.Univariate and multivariate logistic regression analyses identified independent clinical predictors of HT.Three models were constructed:(1)a clinical factor model,(2)a MRI radiomics model,and(3)a combined model integrating both features.External validation was performed using data from 106 patients from Center 2.Receiver operating characteristic(ROC)curves and area under the curve(AUC)values evaluated the predictive performance of the models,while DeLong's test was applied to compare statistical differences between AUCs.Results:In the training set,the AUCs for the clinical factor model,radiomics model,and combined model were 0.810(95%CI:0.756 to 0.864),0.896(95%CI:0.865 to 0.928),and 0.928(95%CI:0.899 to 0.958),respectively.In the internal validation set,the corresponding AUCs were 0.757(95%CI:0.671 to 0.843),0.852(95%CI:0.791 to 0.913),and 0.872(95%CI:0.809 to 0.935).In the external validation set,the AUCs were 0.720(95%CI:0.602 to 0.839),0.804(95%CI:0.711 to 0.897),and 0.828(95%CI:0.751 to 0.905),respectively.Decision curve analysis indicated that the combined model provided the highest net benefit.Conclusions:Both MRI-based radiomics models and clinical factor models demonstrated predictive value for HT risk after thrombolysis in AIS.The integration of these two approaches achieved the best performance,offering potential clinical utility for individualized risk stratification.关键词
缺血性脑卒中/出血转化/磁共振成像/机器学习/影像组学Key words
ischemic stroke/hemorrhagic transformation/magnetic resonance imaging/machine learning/radiomics分类
医药卫生引用本文复制引用
伊木然·苏比,帕哈提·吐逊江,艾尼卡尔江·艾合麦提,罕迦尔别克·库锟,徐蕊,韩秉艳,谢超,王云玲..基于MRI的影像组学模型及临床因素模型对缺血性脑卒中溶栓后出血转化风险的价值[J].磁共振成像,2025,16(8):32-40,9.基金项目
National Key R&D Program of China(No.2023YFC2414200) （No.2023YFC2414200）
Central Government's Guide to Local Science and Technology Development Project(No.ZYYD2023D02) （No.ZYYD2023D02）
Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2024D01D20) （No.2024D01D20）
Tianshan Talents Project of Xinjiang Uygur Autonomous Region(No.2023TSYCLJ0027). 国家重点研发计划项目(编号:2023YFC2414200) （No.2023TSYCLJ0027）
中央引导地方科技发展资金项目(编号:ZYYD2023D02) （编号:ZYYD2023D02）
新疆维吾尔自治区自然科学基金项目(编号:2024D01D20) （编号:2024D01D20）
"天山英才"科技创新领军人才项目(编号:2023TSYCLJ0027) （编号:2023TSYCLJ0027）
