Liu M, Chang N, Zhang S, Du Y, Zhang X, Ren W, Sun J, Bai J, Wang L, Zhang G
Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Department of Medical Technology, Jinan Nursing Vocational College, No. 3636 Gangxi Road, Jinan 250021, Shandong, China.
Clin Radiol. 2023 Nov;78(11):e856-e863. doi: 10.1016/j.crad.2023.07.018. Epub 2023 Aug 14.
To develop and validate a radiomics nomogram for identifying high-risk carotid plaques on computed tomography (CT) angiography (CTA).
A total of 280 patients with symptomatic (n=131) and asymptomatic (n=139) carotid plaques were divided into a training set (n=135), validation set (n=58), and external test set (n=87). Radiomic features were extracted from CTA images. A radiomics model was constructed based on selected features and a radiomics score (rad-score) was calculated. A clinical factor model was constructed by demographics and CT findings. A radiomics nomogram combining independent clinical factors and the rad-score was constructed. The diagnostic performance of three models was evaluated and validated by region of characteristic curves.
Calcification and maximum plaque thickness were the independent clinical factors. Twenty-four features were used to build the radiomics signature. In the validation set, the nomogram (area under the curve [AUC], 0.977; 95% CI, 0.899-0.999) performed better (p=0.017 and p=0.031) than the clinical factor model (AUC, 0.862; 95% CI, 0.746-0.938) and radiomics signature (AUC, 0.944; 95% CI, 0.850-0.987). In external test set, the nomogram (AUC, 0.952; 95% CI, 0.884-0.987) and radiomics signature (AUC, 0.932; 95% CI, 0.857-0.975) showed better discrimination capability (p=0.002 and p=0.037) than clinical factor model (AUC, 0.818; 95% CI, 0.721-0.892).
The CT-based nomogram showed satisfactory performance in identification of high-risk plaques in carotid arteries, and it may serve as a potential non-invasive tool to identify carotid plaque vulnerability and risk stratification.
开发并验证一种用于在计算机断层扫描（CT）血管造影（CTA）上识别高危颈动脉斑块的影像组学列线图。
将总共280例有症状（n = 131）和无症状（n = 139）颈动脉斑块患者分为训练集（n = 135）、验证集（n = 58）和外部测试集（n = 87）。从CTA图像中提取影像组学特征。基于选定特征构建影像组学模型并计算影像组学评分（rad-score）。通过人口统计学和CT表现构建临床因素模型。构建一个结合独立临床因素和rad-score的影像组学列线图。通过特征曲线区域评估并验证三种模型的诊断性能。
钙化和最大斑块厚度是独立临床因素。使用24个特征构建影像组学特征。在验证集中，列线图（曲线下面积[AUC]，0.977；95%可信区间，0.899 - 0.999）的表现优于临床因素模型（AUC，0.862；95%可信区间，0.746 - 0.938）和影像组学特征（AUC，0.944；95%可信区间，0.850 - 0.987）（p = 0.017和p = 0.031）。在外部测试集中，列线图（AUC，0.952；95%可信区间，0.884 - 0.987）和影像组学特征（AUC，0.932；95%可信区间，0.857 - 0.975）的鉴别能力优于临床因素模型（AUC，0.818；95%可信区间，0.721 - 0.892）（p = 0.002和p = 0.037）。
基于CT的列线图在识别颈动脉高危斑块方面表现出令人满意的性能，它可能作为一种潜在的非侵入性工具来识别颈动脉斑块易损性和风险分层。
