Title:Chest Radiograph (CXR) Manifestations of the Novel Coronavirus Disease 2019 (COVID-19): A Mini-review
Volume: 17
Author(s): Wai Yee Chan*, Marlina Tanty Ramli Hamid*, Nadia Fareeda Muhammad Gowdh, Kartini Rahmat, Nur Adura Yaakup and Chee Shee Chai
Affiliation:
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur,Malaysia
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur,Malaysia
Keywords:
COVID-19, CXR, pneumonia, ground-glass opacity, consolidation, peripheral, ARDS.
Abstract:
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 areas
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 peripheral,
or lower zone distribution, whereas atypical CXR features comprised cavitation and pleural
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.
