Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review

By M. Ng, Elaine Y P Lee, Jin Yang, Fangfang Yang, Xia Li, Hongxia Wang, M. Lui, C. Lo, B. Leung, P. Khong and 3 more. Radiology: Cardiothoracic Imaging, 2020

1,052 citations recorded by the source corpus.

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, 2020 See editorial by Kay and Abbara in this issue.

Subfields: Critical Care and Intensive Care Medicine, Infectious Diseases, Surgery

View Paper: https://www.semanticscholar.org/paper/7c1d72e77a8e026977c4b794ccca4edc000acc83

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In Critical Care and Intensive Care Medicine

1. Marzyeh Ghassemi

In Infectious Diseases

1. Abdul Hafeez
2. Muhammad Shoaib Farooq
3. Youness Chawki
4. Alexander Wong
5. Khalid El Asnaoui
6. Linda Wang

In Surgery

1. Youness Chawki
2. Khalid El Asnaoui
3. Marzyeh Ghassemi

Citations in the corpus

• Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, background): “Although screening, diagnosis, and progress assessment of COVID-19 have been effectively performed through reliance on radiological examinations, including CT and digital photography (DR) [50], [51], there has been not much prior expe-”
• Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. (2020, neutral): “Ng [38] Yes Yes CD CD NA Yes CD Yes Yes Fair Fair”
• COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, background): “Particularly in the beginning of the outbreak, computed tomography (CT) scans captured the attention of both the medical [Ng et al., 2020; Kanne et al., 2020] and the ML [McCall, 2020] communities.”
• COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs (2020, background): “Some studies have shown the use of imaging techniques such as X-rays or Computed Tomography (CT-scans) for finding characteristic symptoms of the novel corona virus in these imaging techniques [2], [3].”
• Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks (2020, background): “Recently, researchers have perceived the imaging patterns on chest CT for detecting the COVID-19 in chest CT [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] .”
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “This makes image analysis from lung CT and X-Ray scans of COVID-19 patients a prime candidate for ML-based approaches, which could help accelerate the analysis of these scans, although the extent to which imaging can be used for diagnosis is still under discussion [58, 59] .”
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, result): “Similar observations were also reported in other studies [7], [8], suggesting that radiological imaging may be helpful in supporting early screening of COVID-19.”
• Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19 (2020, background): “Thin-section chest CT is more sensitive than chest radiography, showing abnormal changes in the lung parenchyma in the early stages of disease (19,20).”
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection (2020, methodology): “The researchers in [18], [19] concluded that chest radiography should be used as the main COVID-19 screening method (also known as SARS-CoV-2).”
• Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review (2020, background): “In patients with COVID-19, unilaterally or bilaterally GGO with a peripheral lung and subpleural distribution are commonly encountered [16, 19, 22] (Fig.”