Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis

被引:5
|
作者
Zhang, Ranying [1 ,2 ]
Zhang, Qingwei [3 ]
Ji, Aihua [1 ,2 ]
Lv, Peng [1 ,2 ]
Acosta- Cabronero, Julio [4 ]
Fu, Caixia [5 ]
Ding, Jing [6 ]
Guo, Daqiao [7 ]
Teng, Zhongzhao [8 ,9 ]
Lin, Jiang [1 ,2 ]
机构
[1] Fudan Univ, Zhongshan Hosp, Dept Radiol, Shanghai, Peoples R China
[2] Shanghai Inst Med Imaging, Shanghai, Peoples R China
[3] Shanghai Jiao Tong Univ, Renji Hosp, Shanghai Inst Digest Dis, Div Gastroenterol & Hepatol,Minist Hlth,Key Lab Ga, 145 Middle Shandong Rd, Shanghai, Peoples R China
[4] Tenoke Ltd, Cambridge, Cambs, England
[5] Siemens Shenzhen Magnet Resonance Ltd, MR Applicat Dev, Shenzhen, Peoples R China
[6] Fudan Univ, Zhongshan Hosp, Dept Neurol, Shanghai, Peoples R China
[7] Fudan Univ, Zhongshan Hosp, Dept Vasc Surg, Shanghai, Peoples R China
[8] Univ Cambridge, Dept Radiol, Cambridge, Cambs, England
[9] Nanjing Jingsan Med Sci & Technol, Nanjing, Peoples R China
关键词
Magnetic resonance imaging; Carotid stenosis; Machine learning; Stents; INTRAPLAQUE HEMORRHAGE; PLAQUE COMPOSITION; HIGH-RISK; IN-VIVO; ENDARTERECTOMY; ATHEROSCLEROSIS; EMBOLISM; ANGIOGRAPHY; PREVENTION; SURGERY;
D O I
10.1007/s00330-022-09302-4
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesCarotid artery stenting (CAS) is an established treatment for local stenosis. The most common complication is new ipsilateral ischemic lesions (NIILs). This study aimed to develop models considering lesion morphological and compositional features, and radiomics to predict NIILs. Materials and methodsOne hundred and forty-six patients who underwent brain MRI and high-resolution vessel wall MR imaging (hrVWI) before and after CAS were retrospectively recruited. Lumen and outer wall boundaries were segmented on hrVWI as well as atherosclerotic components. A traditional model was constructed with patient clinical information, and lesion morphological and compositional features. Least absolute shrinkage and selection operator algorithm was performed to determine key radiomics features for reconstructing a radiomics model. The model in predicting NIILs was trained and its performance was tested. ResultsSixty-one patients were NIIL-positive and eighty-five negative. Volume percentage of intraplaque hemorrhage (IPH) and patients' clinical presentation (symptomatic/asymptomatic) were risk factors of NIILs. The traditional model considering these two features achieved an area under the curve (AUC) of 0.778 and 0.777 in the training and test cohorts, respectively. Twenty-two key radiomics features were identified and the model based on these features achieved an AUC of 0.885 and 0.801 in the two cohorts. The AUCs of the combined model considering IPH volume percentage, clinical presentation, and radiomics features were 0.893 and 0.842 in the training and test cohort respectively. ConclusionsCompared with traditional features (clinical and compositional features), the combination of traditional and radiomics features improved the power in predicting NIILs after CAS.
引用
收藏
页码:4115 / 4126
页数:12
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