Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT.
Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT.
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DOI：
10.1259/bjr.20211274
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发表时间：
2022-03
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通讯作者：
Lu Zhang;Zhihan Xu;B. Jiang;Yaping Zhang;Lingyun Wang;Geertruida H deBock;R. Vliegenthart;Xueqian Xie
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文献类型：
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作者：
Lu Zhang;Zhihan Xu;B. Jiang;Yaping Zhang;Lingyun Wang;Geertruida H deBock;R. Vliegenthart;Xueqian Xie
目的是建立并验证一种基于机器学习的放射组学方法，通过分析CT图像中的心外膜脂肪组织（EAT）来确定心房颤动（AF）的存在。方法回顾性分析经心电图示踪的200例增强或300例非增强胸部CT扫描的房颤患者。经过EAT分割和放射组学特征提取，分割后的EAT得到1,691个放射组学特征。通过Boruta算法和基于机器学习的随机森林算法选择对AF贡献最大的特征，并将其组合构建放射组学特征（EAT-score）。采用多因素logistic回归建立临床因素模型和嵌套模型。结果在对比增强扫描测试队列（n = 60/200）中，EAT评分识别AF患者的AUC为0.92 (95%CI:0.84 ~ 1.00)，高于临床因素模型（总胆固醇和体重指数）0.71 (0.58 ~ 0.85)(DeLong’s p = 0.01)，高于EAT体积模型0.73 (0.61 ~ 0.86)（p = 0.01）。在非增强扫描测试队列（n = 100/300）中，eat评分的AUC为0.85(0.77-0.92)，高于CT衰减模型（p 0.05）。基于机器学习的放射组学生成的EAT评分在识别房颤患者方面具有很高的性能。知识进展基于机器学习的放射组学分析可以在对比增强和非增强胸部CT的EAT上识别房颤。
OBJECTIVES The purpose is to establish and validate a machine-learning-derived radiomics approach to determine the existence of atrial fibrillation (AF) by analyzing epicardial adipose tissue (EAT) in CT images. METHODS Patients with AF based on electrocardiographic tracing who underwent contrast-enhanced (n = 200) or non-enhanced (n = 300) chest CT scans were analyzed retrospectively. After EAT segmentation and radiomics feature extraction, the segmented EAT yielded 1,691 radiomics features. The most contributive features to AF were selected by the Boruta algorithm and machine-learning-based random forest algorithm, and combined to construct a radiomics signature (EAT-score). Multivariate logistic regression was used to build clinical factor and nested models. RESULTS In the test cohort of contrast-enhanced scanning (n = 60/200), the AUC of EAT-score for identifying patients with AF was 0.92 (95%CI:0.84-1.00), higher than 0.71 (0.58-0.85) of the clinical factor model (total cholesterol and body mass index) (DeLong's p = 0.01), and higher than 0.73 (0.61-0.86) of the EAT volume model (p = 0.01). In the test cohort of non-enhanced scanning (n = 100/300), the AUC of EAT-score was 0.85 (0.77-0.92), higher than that of the CT attenuation model (p 0.05). CONCLUSION EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with AF. ADVANCES IN KNOWLEDGE A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT.