[Feasibility on the diagnosis of non-calcified plaque based on radiomics of pericoronary adipose tissue on plain CT scan image]

医学 放射科 脂肪组织 无线电技术 计算机断层摄影术 核医学 计算机断层血管造影 接收机工作特性 霍恩斯菲尔德秤 钙化
作者
Qing Tao,Su-hong Wang,F Xu,Min Chen,X Y Zha,Chunying Chen,Shaoyan Hu,L Y Zhang,H L Shen,C H Hu
出处
期刊:National Medical Journal of China 卷期号:101 (7): 458-463
标识
DOI:10.3760/cma.j.cn112137-20201214-03355
摘要

Objective: To investigate the diagnostic value of radiomics model based on plain CT scan of peripheral coronary artery adipose tissue for non-calcified plaque. Methods: 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 (n=606) and the testing group (n=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. Results: 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. Conclusion: The radiomics model based on plain CT scan of the pericoronary adipose tissue had good diagnostic efficacy for non-calcified plaque.
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