Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients.

By Sana Salehi, Aidin Abedi, Sudheer Balakrishnan, Ali Gholamrezanezhad. AJR. American journal of roentgenology, 2020

1,399 citations recorded by the source corpus.

OBJECTIVE. Available information on CT features of the 2019 novel coronavirus disease (COVID-19) is scattered in different publications, and a cohesive literature review has yet to be compiled. MATERIALS AND METHODS. This article includes a systematic literature search of PubMed, Embase (Elsevier), Google Scholar, and the World Health Organization database. RESULTS. Known features of COVID-19 on initial CT include bilateral multilobar ground-glass opacification (GGO) with a peripheral or posterior distribution, mainly in the lower lobes and less frequently within the right middle lobe. Atypical initial imaging presentation of consolidative opacities superimposed on GGO may be found in a smaller number of cases, mainly in the elderly population. Septal thickening, bronchiectasis, pleural thickening, and subpleural involvement are some of the less common findings, mainly in the later stages of the disease. Pleural effusion, pericardial effusion, lymphadenopathy, cavitation, CT halo sign, and pneumothorax are uncommon but may be seen with disease progression. Follow-up CT in the intermediate stage of disease shows an increase in the number and size of GGOs and progressive transformation of GGO into multifocal consolidative opacities, septal thickening, and development of a crazy paving pattern, with the greatest severity of CT findings visible around day 10 after the symptom onset. Acute respiratory distress syndrome is the most common indication for transferring patients with COVID-19 to the ICU and the major cause of death in this patient population. Imaging patterns corresponding to clinical improvement usually occur after week 2 of the disease and include gradual resolution of consolidative opacities and decrease in the number of lesions and involved lobes. CONCLUSION. This systematic review of current literature on COVID-19 provides insight into the initial and follow-up CT characteristics of the disease.

Subfields: Infectious Diseases, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/32b8d63a8f75d366b40ca997b2d72463b9af2b82

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. F. Cilia
2. C. Mastroianni
3. V. Panebianco
4. C. Andreoli
5. G. Masci
6. L. Manganaro
7. Simona Coco
8. F. Pugliese

Citations in the corpus

• The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic (2020, neutral): “For purposes of image interpretation and reporting, readers are referred to a recently published systematic review of imaging findings of COVID-19 (36) and a multi-society consensus paper on reporting chest CT findings related to COVID-19 (37).”
• The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, neutral): “Besides GGO, bilateral patchy shadowing is one of the most common radiologic findings on chest CT [5], [16], [25].”
• Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets (2020, neutral): “Since most frequently observed distribution patterns of COVID-19 in CXR are bilateral involvement, peripheral distribution and ground-glass opacification (GGO) [13], a properly designed neural network should reflect such radiological findings.”
• A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact (2020, neutral): “The authors of [24] have reviewed the existing literature on computed tomography (CT) characteristics of COVID-19 available on platforms such as PubMed, Google Scholar, and Elsevier, among others.”
• Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis (2020, positive): “Based on previous publication [13, 14], diagnosis of a suspected SARS-CoV-2 pneumonia was established considering the following chest CT patterns: GGO, crazy-paving, and consolidation.”
• Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020, neutral): “and multi-lobar glass ground opacifications (mainly posteriorly and/or peripherally distributed) [4], [5].”