Chest Radiographic and CT Findings of the 2019 Novel Coronavirus Disease (COVID-19): Analysis of Nine Patients Treated in Korea
By S. Yoon, Kyung Yol Lee, Jin Yong Kim, Young Kyung Lee, H. Ko, Ki Hwan Kim, C. Park, Y. Kim. Korean Journal of Radiology, 2020
615 citations recorded by the source corpus.
Objective This study presents a preliminary report on the chest radiographic and computed tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) pneumonia in Korea. Materials and Methods As part of a multi-institutional collaboration coordinated by the Korean Society of Thoracic Radiology, we collected nine patients with COVID-19 infections who had undergone chest radiography and CT scans. We analyzed the radiographic and CT findings of COVID-19 pneumonia at baseline. Fisher's exact test was used to compare CT findings depending on the shape of pulmonary lesions. Results Three of the nine patients (33.3%) had parenchymal abnormalities detected by chest radiography, and most of the abnormalities were peripheral consolidations. Chest CT images showed bilateral involvement in eight of the nine patients, and a unilobar reversed halo sign in the other patient. In total, 77 pulmonary lesions were found, including patchy lesions (39%), large confluent lesions (13%), and small nodular lesions (48%). The peripheral and posterior lung fields were involved in 78% and 67% of the lesions, respectively. The lesions were typically ill-defined and were composed of mixed ground-glass opacities and consolidation or pure ground-glass opacities. Patchy to confluent lesions were primarily distributed in the lower lobes (p = 0.040) and along the pleura (p < 0.001), whereas nodular lesions were primarily distributed along the bronchovascular bundles (p = 0.006). Conclusion COVID-19 pneumonia in Korea primarily manifested as pure to mixed ground-glass opacities with a patchy to confluent or nodular shape in the bilateral peripheral posterior lungs. A considerable proportion of patients with COVID-19 pneumonia had normal chest radiographs.
Subfields: Infectious Diseases, Neurology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/be6dcae434b9cf678804c4337ee1e74b41ddb6f0
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Citations in the corpus
- Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets (2020, methodology): “This is not only because it is strongly correlated with the radiological findings [18], but also useful as a triage.”
- Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review (2020, background): “Air bronchogram was defined as a pattern of air-filled (lowattenuation) bronchi on a background of opaque (highattenuation) airless lung [26] and was reported to be another CT manifestation of COVID-19 [17, 19] (Fig.”
- Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, background): “Yoon et al. [19] reported that one in three patients studied had a single nodular opacity in the left lower lung region.”