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Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review.
Ng, Ming-Yen; Lee, Elaine Y P; Yang, Jin; Yang, Fangfang; Li, Xia; Wang, Hongxia; Lui, Macy Mei-Sze; Lo, Christine Shing-Yen; Leung, Barry; Khong, Pek-Lan; Hui, Christopher Kim-Ming; Yuen, Kwok-Yung; Kuo, Michael D.
Afiliación
  • Ng MY; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Lee EYP; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Yang J; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Yang F; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Li X; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Wang H; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Lui MM; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Lo CS; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Leung B; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Khong PL; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Hui CK; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Yuen KY; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
  • Kuo MD; Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infec
Radiol Cardiothorac Imaging ; 2(1): e200034, 2020 Feb.
Article en En | MEDLINE | ID: mdl-33778547
ABSTRACT

PURPOSE:

To present the findings of 21 coronavirus disease 2019 (COVID-19) cases from two Chinese centers with CT and chest radiographic findings, as well as follow-up imaging in five cases. MATERIALS AND

METHODS:

This was a retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on radiologic features of COVID-19 infection was conducted.

RESULTS:

The predominant imaging pattern was of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrated evolution of the ground-glass opacities into consolidation and subsequent resolution of the airspace changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiography, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified four other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding at CT chest examinations.

CONCLUSION:

Pulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with four previous publications from other sites.© RSNA, 2020See editorial by Kay and Abbara in this issue.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Idioma: En Revista: Radiol Cardiothorac Imaging Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Idioma: En Revista: Radiol Cardiothorac Imaging Año: 2020 Tipo del documento: Article