Zhang Bo, Wang Xia, Tian Xiaoyan, Zhao Xiaoying, Liu Bin, Wu Xingwang, Du Yaqing, Huang Guoquan, Zhang Qing
Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
Department of Imaging, Fuyang Second People's Hospital.
Medicine (Baltimore). 2020 Oct 16;99(42):e22747. doi: 10.1097/MD.0000000000022747.
To study the differences in imaging characteristics and prediction of COVID-19 and non-COVID-19 viral pneumonia through chest CT.Chest CT data of 128 cases of COVID-19 and 47 cases of non-COVID-19 viral pneumonia confirmed by several hospitals were retrospectively collected, the imaging performance was evaluated and recorded, different imaging features were statistically analyzed, and a prediction model and independent predicted imaging features were obtained by multivariable analysis.COVID-19 was more likely than non-COVID-19 pneumonia to have a high-grade ground glass opacities (P = .01), extensive lesion distribution (P < .001), mixed lesions of varying sizes (27.7% vs 57.0%, P = .001), subpleural prominence (23.4% vs 86.7%, P < .001), and lower lobe prominence (48.9% vs 82.0%, P < .001). However, peribronchial interstitial thickening was more likely to occur in non-COVID-19 viral pneumonia (36.2% vs 19.5%, P = .022). The statistically significant differences from multivariable analysis were the degree of ground glass opacities (P = .001), lesion distribution (P = .045), lesion size (P = .020), subpleural prominence (P < .001), and lower lobe prominence (P = .041). The sensitivity and specificity of the model were 94.5% and 76.6%, respectively, with an AUC of 0.91.The imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia are different, and the prediction model can further improve the specificity of chest CT diagnosis.
通过胸部CT研究新型冠状病毒肺炎（COVID-19）与非COVID-19病毒性肺炎的影像特征差异及预测情况。回顾性收集多家医院确诊的128例COVID-19患者和47例非COVID-19病毒性肺炎患者的胸部CT数据，评估并记录影像表现，对不同影像特征进行统计学分析，通过多变量分析获得预测模型及独立的预测影像特征。与非COVID-19肺炎相比，COVID-19更易出现高级别磨玻璃影（P = 0.01）、病变分布广泛（P < 0.001）、大小不一的混合性病变（27.7% 对57.0%，P = 0.001）、胸膜下突出（23.4% 对86.7%，P < 0.001）以及下叶突出（48.9% 对82.0%，P < 0.001）。然而，支气管周围间质增厚在非COVID-19病毒性肺炎中更易出现（36.2% 对19.5%，P = 0.022）。多变量分析的统计学显著差异在于磨玻璃影程度（P = 0.001）、病变分布（P = 0.045）、病变大小（P = 0.020）、胸膜下突出（P < 0.001）以及下叶突出（P = 0.041）。该模型的敏感性和特异性分别为94.5%和76.6%，曲线下面积（AUC）为0.91。COVID-19与非COVID-19病毒性肺炎的影像特征不同，该预测模型可进一步提高胸部CT诊断的特异性。
