CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)

By M. Chung, Adam Bernheim, X. Mei, Ning Zhang, Mingqian Huang, Xianjian Zeng, J. Cui, Wenjian Xu, Yang Yang, Z. Fayad and 4 more. Radiology, 2020

2,605 citations recorded by the source corpus.

In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities. © RSNA, 2020

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

View Paper: https://www.semanticscholar.org/paper/c9e6f9830966d35e607b337db8a55bfb59a37901

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Citations in the corpus

Predictors of COVID-19 severity: A literature review (2020, background): “The most common CT findings are ground-glass opacities or bilateral consolidation in the peripheral lower lung fields.(45,46) However, early in the disease course or in those with mild symptoms, chest imaging may be normal.”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “In early studies, it was found that certain abnormalities in chest CT images are indicative of COVID19 infection, with ground-glass opacities, patchy shadows, crazy-paving pattern, and consolidation being some of the most commonly reported abnormalities, typically with bilateral involvement [11, 12,”
Coronavirus Disease 2019 (COVID-19): A Perspective from China (2020, background): “Pleural effusion, lung cavitation, lymphadenopathy and calcification are rarely reported (40-44,49-50).”
Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? (2020, background): “Imaging plays an important role in the diagnosis and evaluation of the disease [2, 3] .”
Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review (2020, background): “Bilateral distribution of ground glass opacities (GGO) with or without consolidation in posterior and peripheral lungs was the cardinal hallmark of COVID-19 [8, 9].”
Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, methodology): “Therefore, for COVID-19 detection, a CT scan of the chest is drawing the researchers’ attention [10], [11].”
C-reactive protein levels in the early stage of COVID-19 (2020, background): “CT dynamic monitoring may be used to identify the characteristic imaging of lung changes: multiple small patch shadows and stromal changes are observed in the early stage and the lung exudate is obvious [12] , which then develops into multiple ground-glass shadows and infiltrating shadows in both lu”
Syrian hamsters as a small animal model for SARS-CoV-2 infection and countermeasure development (2020, methodology): “Because no substantial difference in viral titers in the respiratory organs was observed between the two age groups, we performed qualitative and semiquantitative image analysis of the lung abnormalities only in the younger age group after SARS-CoV-2 infection, by using a CT severity score adapted f”
Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label (2020, background): “In a word, chest CT is a key component of the diagnostic procedure for suspected patients and its CT manifestations have been emphasized in several recent reports [1, 9, 10, 11, 12].”
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia (2020, neutral): “Noisy-or Bayesian function to deduce the overall report One of the remarkable features of COVID-19 is that there is more than one independent focus of infection in a single CT case [29,30].”