Tao Q, Wang S, Xu F, Chen M, Zha X Y, Chen C, Hu S, Zhang L Y, Shen H L, Hu C H
Department of Radiology, the First Affiliated Hospital of Soochow University,Suzhou 215006,China.
Department of Radiology, Suzhou Kowloon Hospital, Suzhou 215012, China.
Zhonghua Yi Xue Za Zhi. 2021 Feb 23;101(7):458-463. doi: 10.3760/cma.j.cn112137-20201214-03355.
To investigate the diagnostic value of radiomics model based on plain CT scan of peripheral coronary artery adipose tissue for non-calcified plaque. The image data of 461 patients undergoing coronary CT angiography (CCTA) in the Department of Radiology of the First Affiliated Hospital of Suzhou University from August 1,2019 to July 31,2020 were retrospectively analyzed. Two hundred and six cases (355 branches) with non-calcified plaques, and 255 cases (510 branches) with no coronary artery disease were detected by CCTA. The regions of interest (ROI) of the pericoronary adipose tissue were segmented on the plain CT scan images (coronary calcification score (CCS) sequence). The coronary ROI was determined by selecting the coronary artery with a length of 40 mm and starting at 10 mm from the opening of the coronary artery, and the pericoronary adipose ROI was generated automatically. The pericoronary fat attenuation index (FAI) was then performed, and the radiomics features were extracted. The 865 coronary arteries were divided into the training group (=606) and the testing group (=259) at a ratio of 7∶3, and the radiomics model was carried out. The receiver operating characteristic (ROC) analysis was used to assess the FAI value and the diagnostic efficacy of the radiomics model for non-calcified plaque. A total of 1 692 features were extracted from images of pericoronary adipose based on plain scan. All features were screened by using max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), and 14 features were selected for the establishment of the radiomics model. The accuracy, sensitivity, specificity and area under the curve (AUC) of the model in distinguishing patients with non-calcified plaque and those without coronary stenosis in the testing group were 70.3%, 63.2%, 75.2% and 0.75, respectively. The radiomics model based on plain CT scan of the pericoronary adipose tissue had good diagnostic efficacy for non-calcified plaque.
探讨基于冠状动脉周围脂肪组织平扫CT图像的影像组学模型对非钙化斑块的诊断价值。回顾性分析2019年8月1日至2020年7月31日苏州大学附属第一医院放射科461例行冠状动脉CT血管造影（CCTA）患者的影像资料。CCTA检测出206例（355支血管）有非钙化斑块，255例（510支血管）无冠状动脉疾病。在平扫CT图像（冠状动脉钙化积分（CCS）序列）上分割冠状动脉周围脂肪组织的感兴趣区（ROI）。冠状动脉ROI通过选取距冠状动脉开口10 mm处开始的40 mm长的冠状动脉来确定，冠状动脉周围脂肪ROI自动生成。然后进行冠状动脉周围脂肪衰减指数（FAI）测定，并提取影像组学特征。将865支冠状动脉按7∶3比例分为训练组（=606）和测试组（=259），构建影像组学模型。采用受试者操作特征（ROC）分析评估FAI值及影像组学模型对非钙化斑块的诊断效能。基于平扫图像从冠状动脉周围脂肪影像中共提取1692个特征。采用最大相关最小冗余（mRMR）和最小绝对收缩与选择算子（LASSO）对所有特征进行筛选，选取14个特征建立影像组学模型。该模型在测试组区分非钙化斑块患者和无冠状动脉狭窄患者的准确性、敏感性、特异性及曲线下面积（AUC）分别为70.3%、63.2%、75.2%和0.75。基于冠状动脉周围脂肪组织平扫CT的影像组学模型对非钙化斑块具有良好的诊断效能。
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