Chest Radiograph (CXR) Manifestations of the Novel Coronavirus Disease 2019 (COVID-19): A Mini-review

• Published in: Current Medical Imaging
• Main Author: Chan W.Y.; Hamid M.T.R.; Gowdh N.F.M.; Rah-Mat K.; Yaakup N.A.; Chai C.S.
• Format: Short survey
• Language: English
• Published: Bentham Science Publishers 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112124816&doi=10.2174%2f1573405616666201231103312&partnerID=40&md5=6f83f6f978542556b18675dd83e6cad6

Background: Coronavirus disease 2019 (COVID-19) is highly contagious and has claimed more than one million lives, besides causing hardship and disruptions. The Fleischner Society has recommended chest X-ray (CXR) in detecting cases at high risk of disease progression, for triaging suspected patients with moderate-to-severe illness, and for eliminating false negatives in ar-eas with high pre-test probability or limited resources. Although CXR is less sensitive than real--time reverse transcription-polymerase chain reaction (RT-PCR) in detecting mild COVID-19, it is nevertheless useful because of equipment portability, low cost and practicality in serial assessments of disease progression among hospitalized patients. Objective: This study aims to review the typical and relatively atypical CXR manifestations of COVID-19 pneumonia in a tertiary care hospital. Methods: The CXRs of 136 COVID-19 patients confirmed through real-time RT-PCR from March to May 2020 were reviewed. A literature search was performed using PubMed. Results: A total of 54 patients had abnormal CXR whilst the others were normal. Typical CXR findings included pulmonary consolidation or ground-glass opacities in a multifocal, bilateral pe-ripheral, or lower zone distribution, whereas atypical CXR features comprised cavitation and pleu-ral effusion. Conclusion: Typical findings of COVID-19 infection in chest computed tomography studies can also be seen in CXR. The presence of atypical features associated with worse disease outcome. Recognition of these features on CXR will improve the accuracy and speed of diagnosing COVID-19 patients. © 2021 Bentham Science Publishers.