Analysis of prediction of carotid in-stent restenosis based on ultrasonographic carotid plaque radiomics
Received date: 2024-11-22
Online published: 2025-03-20
目的 探索超声影像组学在预测颈动脉支架置入（carotid artery stent， CAS）术后再狭窄（in-stent restenosis， ISR）发生的能力。 方法 回顾性收集广州中医药大学第二临床医学院接受CAS术治疗患者共206例病例。将入组患者按7∶3的比例随机分为训练集（144例）和测试集（62例），使用达尔文智能科研平台提取超声影像组学特征，从每个感兴趣区域中提取的1 125个超声影像组学特征中筛选特征，使用不同的机器学习算法构建诊断模型，选择模型表现最好的分类器，建立了不同的预测模型，包括临床-超声特征模型、超声影像组学模型、临床-超声-超声影像组学的联合模型。 结果 在训练集中多因素逻辑回归分析显示，高血压病、高尿酸血症、甘油三酯和斑块位置是CAS术后发生ISR的独立危险因素。对于临床-超声模型，训练集和测试集的曲线下面积（area under the curve， AUC）分别为0.896和0.644。超声影像组学模型对应的AUC值分别为0.961和0.715，联合模型对应的AUC值分别为0.947和0.727。 结论 超声影像组学模型预测ISR的能力优于传统的临床-超声模型。联合模型能够更好地预测ISR发生，从而提高传统评估的诊断性能。
赖丹惠 , 江燕辉 , 叶思婷 , 庄淑莲 , 杨爽 , 薛文 , 张建兴 . 颈动脉斑块超声影像组学特征对颈动脉支架置入术后再狭窄的预测能力[J]. 实用医学杂志, 2025 , 41(5) : 742 -750 . DOI: 10.3969/j.issn.1006-5725.2025.05.019
Objective This study aimed to explore the ability of ultrasonographic radiomics in predicting the occurrence of in-stent restenosis （ISR） after carotid artery stenting （CAS） by analyzing the correlation between radiomic features of responsible plaques in carotid artery stenosis and the incidence of ISR. Methods A retrospective collection was conducted on 206 cases that underwent CAS treatment at our hospital. The enrolled patients were randomly split into a training set （144 cases） and a test set （62 cases） at a 7：3 ratio. We utilized the Darwin Intelligent Research Platform to extract radiomic features from each region of interest， and then screened 1125 ultrasonographic radiomic features. Different machine learning algorithms were employed to construct diagnostic models， and the best-performing classifier was selected. Various prediction models were established， including a clinical-ultrasonographic feature model， a radiomic model， and a combined clinical-ultrasonographic-radiomic model. Results Multivariate logistic regression analysis in the training set revealed that hypertension， hyperuricemia， triglycerides， and plaque location were independent risk factors for ISR after CAS. For the clinical-ultrasonographic model， the area under the curve （AUC） values for the training and validation sets were 0.896 and 0.644， respectively. The corresponding AUC values for the radiomic model were 0.961 and 0.715， while those for the combined model were 0.947 and 0.727. Conclusion The radiomic model demonstrates superior performance in predicting ISR compared to the traditional clinical-ultrasonographic model. The combined model exhibited an enhanced ability to predict ISR occurrence， thereby improving the diagnostic performance of traditional assessments。
Key words： ultrasonography; radiomics; carotid artery; stent implantation; restenosis
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