The Many Faces of COVID-19: Spectrum of Imaging                     Manifestations

Kay, Fernando Uliana; Abbara, Suhny

doi:10.1148/ryct.2020200037

Cited by 42 publications

(40 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We can evaluate the disease severity of COVID-19 and the efficacy of treatment through dynamic observation of CT imaging to guide clinical management. GGO is the most typical imaging feature of COVID-19 [49]. In a retrospective study, the analysis of the CT images of 21 patients showed that most patients had single or multiple GGOs in the early stages of the disease, and the scope of GGOs continued to expand with disease progression.…”

Section: Imaging Features At Different Stagesmentioning

confidence: 99%

Diagnostic value and key features of computed tomography in Coronavirus Disease 2019

Wang

et al. 2020

Emerging Microbes & Infections

124

104

View full text Add to dashboard Cite

On 31 December 2019, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, Hubei province, China, and caused the outbreak of the Coronavirus Disease 2019 . To date, computed tomography (CT) findings have been recommended as major evidence for the clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the imaging characteristics and changes throughout the disease course in patients with COVID-19 in order to provide some help for clinicians. Typical CT findings included bilateral ground-glass opacity, pulmonary consolidation, and prominent distribution in the posterior and peripheral parts of the lungs. This review also provides a comparison between COVID-19 and other diseases that have similar CT findings. Since most patients with COVID-19 infection share typical imaging features, radiological examinations have an irreplaceable role in screening, diagnosis and monitoring treatment effects in clinical practice.

show abstract

Section: Imaging Features At Different Stagesmentioning

confidence: 99%

Diagnostic value and key features of computed tomography in Coronavirus Disease 2019

Wang

et al. 2020

Emerging Microbes & Infections

124

104

View full text Add to dashboard Cite

show abstract

“…[10][11][12], which may shed light on the possible mechanism of lung injury in COVID-19. A recent editorial by Kay et al [13] also encouraged researchers to focus the many faces of COVID-19 for its better recognition and accurate diagnosis. Therefore, with a comprehensive review of published studies and the experience of COVID-19 imaging interpretation in frontline, we aim to review the typical and relatively atypical CT manifestations of COVID-19 in a pictorial fashion and help radiologists to familiarize these possible imaging features of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review

et al. 2020

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, has rapidly swept around the world just within a month, causing global public health emergency. In diagnosis, chest computed tomography (CT) manifestations can supplement parts of limitations of real-time reverse transcription polymerase chain reaction (RT-PCR) assay. Based on a comprehensive literature review and the experience in the frontline, we aim to review the typical and relatively atypical CT manifestations with representative COVID-19 cases at our hospital, and hope to strengthen the recognition of these features with radiologists and help them make a quick and accurate diagnosis.

show abstract

“…The latest research studies described the characteristic imaging manifestations of COVID-19, including ground-glass opacities (GGO) (57 to 88%), bilateral involvement (76 to 88%), and peripheral distribution (33 to 85%) [7][8][9][10]. Other imaging features such as consolidation, cavitation, and interlobular septal thickening are also reported in some patients [11][12][13]. However, these imaging manifestations of COVID-19 are nonspecific and are difficult to distinguish from other pneumonia.…”

Section: Introductionmentioning

confidence: 99%

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

et al. 2020

View full text Add to dashboard Cite

Objectives Rapid and accurate diagnosis of coronavirus disease 2019 is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. Methods A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. Results Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. Conclusions Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.Xiaofeng Chen and Yanyan Tang contributed equally to this work.Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users. Key Points• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

show abstract

The Many Faces of COVID-19: Spectrum of Imaging Manifestations

Cited by 42 publications

References 17 publications

Diagnostic value and key features of computed tomography in Coronavirus Disease 2019

Diagnostic value and key features of computed tomography in Coronavirus Disease 2019

Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Contact Info

Product

Resources

About