Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT

By H. Bai, B. Hsieh, Z. Xiong, K. Halsey, Ji Whae Choi, T. Tran, I. Pan, Linbo Shi, Dongcui Wang, J. Mei and 10 more. Radiology, 2020

1,038 citations recorded by the source corpus.

Background Despite its high sensitivity in diagnosing COVID-19 in a screening population, chest CT appearances of COVID 19 pneumonia are thought to be non-specific. Purpose To assess the performance of United States (U.S.) and Chinese radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Methods A total of 219 patients with both positive COVID-19 by RT-PCR and abnormal chest CT findings were retrospectively identified from 7 Chinese hospitals in Hunan Providence, China from January 6 to February 20, 2020. A total of 205 patients with positive Respiratory Pathogen Panel for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia by original radiology interpretation within 7 days of each other were identified from Rhode Island Hospital in Providence, RI. Three Chinese radiologists blindly reviewed all chest CTs (n=424) to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched cases was randomly selected and evaluated by 4 U.S. radiologists in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CTs, three Chinese radiologists correctly differentiated COVID-19 from non-COVID-19 pneumonia 83% (350/424), 80% (338/424), and 60% (255/424) of the time, respectively. The seven radiologists had sensitivities of 80%, 67%, 97%, 93%, 83%, 73% and 70% and specificities of 100%, 93%, 7%, 100%, 93%, 93%, 100%. Compared to non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs. 57%, p<0.001), ground-glass opacity (91% vs. 68%, p<0.001), fine reticular opacity (56% vs. 22%, p<0.001), and vascular thickening (59% vs. 22%, p<0.001), but less likely to have a central+peripheral distribution (14.% vs. 35%, p<0.001), pleural effusion (4.1 vs. 39%, p<0.001) and lymphadenopathy (2.7% vs. 10.2%, p<0.001). Conclusion Radiologists in China and the United States distinguished COVID-19 from viral pneumonia on chest CT with high specificity but moderate sensitivity. A translation of this abstract in Farsi is available in the supplement. - ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5c73dbe54faf08c0bbc3df72bd2929e7b7870a64

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In Infectious Diseases

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “As a result, the performance of radiologists in distinguishing COVID-19-related abnormalities from abnormalities of other etiology may vary considerably [15, 16].”
CO-RADS – A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation (2020, background): “COVID-19 presents with CT findings that partially overlap with other diseases, mainly viral infections, but also shows characteristic features seen less frequently in other settings (12).”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, neutral): “(Bai et al., 2020).”
The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, background): “…with COVID-19 are more likely to exhibit CT abnormalities characterized by peripheral distribution, ﬁne reticular opacity, vascular thickening, and reverse halo sign, but are less likely to have signiﬁcant levels of central-peripheral distribution, pleural effusion, and lymphadenopathy [29].”