Predicting Vulnerability Status of Carotid Plaques Using CTA-Based Quantitative Analysis.

IF 2.6 4区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Qun Lao, Rongzhen Zhou, Yitian Wu, ChangFeng Feng, Jianxin Pang, Ling Ma, Yunjun Yang, Wenbin Ji
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引用次数: 0

Abstract

Abstract: The study aimed to develop a radiomics model to assess carotid artery plaque vulnerability using computed tomography angiography images. It retrospectively included 107 patients with carotid artery stenosis who underwent carotid artery stenting from 2017 to 2022. Patients were categorized into stable and vulnerable plaque groups based on pathology. A training group and a testing group were formed in a 7:3 ratio. Clinical data, including demographics and lipid profiles, were collected alongside pretreatment computed tomography angiography images. Radiomics features were extracted and reduced using the LASSO method to minimize redundancy. A radiomics model was then constructed, using 13 features with a minimum penalty coefficient logλ = 0.047. Significant differences were found between stable and vulnerable plaques in terms of stenosis degree. The radiomics model showed high accuracy (area under the curve of 0.959 in training and 0.942 in testing) for identifying vulnerable plaques. When combined with clinical parameters stenosis degree, the model's diagnostic efficacy improved further, with area under the curve values of 0.985 and 0.961 in the training and testing groups, respectively. Decision curve analysis indicated that the combined model offered superior clinical benefits for the clinical model and radiomics model alone. The study concludes that the combined radiomics model, incorporating stenosis degree, presents a promising tool for differentiating vulnerable from stable plaques.

基于cta的定量分析预测颈动脉斑块易损状态。
该研究旨在开发一种放射组学模型,利用CTA图像评估颈动脉斑块易损性。该研究回顾性纳入了2017年至2022年接受颈动脉支架植入术(CAS)的107例颈动脉狭窄患者。根据病理情况将患者分为稳定斑块组和易损斑块组。训练组和测试组按7:3的比例组成。临床数据,包括人口统计学和脂质谱,与治疗前的CTA图像一起收集。利用LASSO方法提取和约简放射组学特征,使冗余最小化。利用最小惩罚系数logλ=0.047的13个特征构建放射组学模型。稳定斑块和易损斑块的狭窄程度存在显著差异。放射组学模型在识别易损斑块方面显示出较高的准确性(训练时AUC为0.959,测试时AUC为0.942)。结合临床参数狭窄程度,模型的诊断效能进一步提高,训练组和试验组的AUC值分别为0.985和0.961。决策曲线分析(Decision curve analysis, DCA)表明,联合模型比单独的临床模型和放射组学模型具有更好的临床效益。该研究得出结论,结合狭窄程度的联合放射组学模型为区分易损斑块和稳定斑块提供了一种很有前途的工具。
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来源期刊
CiteScore
5.10
自引率
3.30%
发文量
367
审稿时长
1 months
期刊介绍: Journal of Cardiovascular Pharmacology is a peer reviewed, multidisciplinary journal that publishes original articles and pertinent review articles on basic and clinical aspects of cardiovascular pharmacology. The Journal encourages submission in all aspects of cardiovascular pharmacology/medicine including, but not limited to: stroke, kidney disease, lipid disorders, diabetes, systemic and pulmonary hypertension, cancer angiogenesis, neural and hormonal control of the circulation, sepsis, neurodegenerative diseases with a vascular component, cardiac and vascular remodeling, heart failure, angina, anticoagulants/antiplatelet agents, drugs/agents that affect vascular smooth muscle, and arrhythmias. Appropriate subjects include new drug development and evaluation, physiological and pharmacological bases of drug action, metabolism, drug interactions and side effects, application of drugs to gain novel insights into physiology or pathological conditions, clinical results with new and established agents, and novel methods. The focus is on pharmacology in its broadest applications, incorporating not only traditional approaches, but new approaches to the development of pharmacological agents and the prevention and treatment of cardiovascular diseases. Please note that JCVP does not publish work based on biological extracts of mixed and uncertain chemical composition or unknown concentration.
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