• Atoosa Adibi, Kimia Kazemi, Somaye Hajiahmadi, Azin Shayganfar, Ibrahim Abdollahpour, Amirreza Manteghinejad, Golnaz Vaseghi, and Shaghayegh Haghjooye Javanmard.
• Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran.
• J Res Med Sci. 2021 Jan 1; 26: 117.

BackgroundNovel coronavirus disease of 2019 (COVID-19) is the current pandemic causing massive morbidity and mortality worldwide. The gold standard diagnostic method in use is reverse transcription-polymerase chain reaction (RT-PCR) which cannot be solely relied upon. Computed tomography (CT) scan is a method currently used for diagnosis of lung disease and can play a substantial role if proved helpful in COVID-19 diagnosis. We conducted this study to evaluate the diagnostic value of CT scan compared to RT-PCR in the diagnosis of COVID-19.Materials And MethodsWe recruited 291 hospitalized patients suspicious of COVID-19 according to typical clinical findings during February-March 2020. The patients underwent CT-scan and RT-PCR procedures on the day of hospital admission. CT scans were reported by two radiologists as typical, indeterminate, negative, and atypical. Statistical indices were calculated twice: once considering "typical" and "indeterminate" categories as positive and the other time counting "typical" results as positive.ResultsThe CT reports were classified as typical (64.95%), indeterminate (10.31%), atypical (11%), and negative (13.75%). Considering "typical" and "intermediate" as positive, sensitivity and specificity were 85.3% and 38.8%, respectively, and using the second assumption, the mentioned indices were 75.9% and 50.4%, respectively.ConclusionAccording to our study, CT results do not create enough diagnostic benefit and could result in incorrect confidence if negative. Since widely available, CT integration in the clinical process may be helpful in screening of suspected patients in epidemics. Yet, suspected patients should be isolated till confirmed by (multiple) PCRs.Copyright: © 2021 Journal of Research in Medical Sciences.

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