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Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: A multicenter study.
Zhang, Bo; Wang, Xia; Tian, Xiaoyan; Zhao, Xiaoying; Liu, Bin; Wu, Xingwang; Du, Yaqing; Huang, Guoquan; Zhang, Qing.
  • Zhang B; Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
  • Wang X; Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
  • Tian X; Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
  • Zhao X; Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
  • Liu B; Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
  • Wu X; Department of Radiology, The First Affiliated Hospital of Anhui Medical University.
  • Du Y; Department of Imaging, Fuyang Second People's Hospital.
  • Huang G; Department of Imaging, Wuhu Second People's Hospital.
  • Zhang Q; Department of imaging, Luian People's Hospital China.
Medicine (Baltimore) ; 99(42): e22747, 2020 Oct 16.
Article in English | MEDLINE | ID: covidwho-933924
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Tomography, X-Ray Computed / Coronavirus Infections Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Medicine (Baltimore) Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Tomography, X-Ray Computed / Coronavirus Infections Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Medicine (Baltimore) Year: 2020 Document Type: Article