ISSN 1004-4140
CN 11-3017/P
Volume 32 Issue 5
Sep.  2023
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HAO Q, LIU X Y, ZHANG Y, et al. The Clinical Value of Thin-section Chest Computed Tomography Scan for the Classification of Coronavirus Disease 2019 (COVID-19)[J]. CT Theory and Applications, 2023, 32(5): 675-683. DOI: 10.15953/j.ctta.2023.041. (in Chinese).
Citation: HAO Q, LIU X Y, ZHANG Y, et al. The Clinical Value of Thin-section Chest Computed Tomography Scan for the Classification of Coronavirus Disease 2019 (COVID-19)[J]. CT Theory and Applications, 2023, 32(5): 675-683. DOI: 10.15953/j.ctta.2023.041. (in Chinese).
shu

The Clinical Value of Thin-section Chest Computed Tomography Scan for the Classification of Coronavirus Disease 2019 (COVID-19)

  • 1.

    Department of Radiology, Ninth Clinical Medical College, Peking University, Beijing 100038, China

  • 2a.

    Capital Medical University, Beijing 100038, China Department of Radiology

  • 2b.

    Capital Medical University, Beijing 100038, China Department of Infectious Diseases, Beijing Shijitan Hospital

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  • Received Date: March 06, 2023
  • Accepted Date: April 13, 2023
  • Available Online: April 19, 2023
  • Published Date: September 21, 2023
    Graphical Abstract
    • Abstract
  • Objective: To investigate the clinical value of thin-section chest computed tomography (CT) in the typing of coronavirus disease 2019 (COVID-19). Methods: A retrospective analysis was performed on 134 patients diagnosed with COVID-19 in our hospital’s Department of Infectious Diseases from December 20, 2022, to December 31, 2022. All patients underwent thin-section chest CT scan with complete clinical data. According to clinical classification, patients were divided into the non-severe and severe groups. Clinical data and imaging features of the two groups were compared and analyzed, and statistical analysis was conducted. Results: There was a statistically significant difference with respect to diabetes mellitus between the two groups, and the incidence of diabetes mellitus in the severe group (45.8%) was higher than that in the non-severe group (25.5%); There were no significant differences in sex, age, average course of disease, and clinical symptoms between the two groups; There were significant differences in the number of lesions, symmetrical distribution, predominant peripheral distribution, diffuse distribution, blurred edge, morphology of large flake and band, vascular bundle thickening, paving stone sign, arcade sign, and fried egg sign between the two groups, the number of lesions >10, diffuse distribution, morphology of large flake and band, vascular bundle thickening, paving stone sign, and arcade sign were more common in the severe group than in the non-severe group, while predominant peripheral distribution, blurred edge, and fried egg sign were more common in the non-severe group than in the severe group. Conclusions: Thin-section chest CT scan can identify the abnormal imaging manifestations of the lung in patients with COVID-19 and evaluate the number, distribution range, and morphological characteristics of the lesions. Combined background diseases, number, distribution characteristics, blurred edge, large flake and band morphology, vascular bundle thickening, paving stone sign, arcade sign, and fried egg sign can effectively indicate the classification of patients with COVID-19. This can provide imaging evidence for the diagnosis and treatment of COVID-19.
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    • References (16)
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