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Mohammad Momen Gharibvand , Gholamreza Rajabi^* , Mohammad Ghasem Hanafi

Abstract

The use of chest CT scans for the diagnosis of Covid-19 is controversial. Covid-19 Reporting and Data System (CO-RADS) classification for measuring the clinical suspicion of Covid-19 might improve diagnostic performance for pulmonary involvement in Covid-19 patients. This study aims to evaluate the diagnostic performance of the CO-RADS system as a new approach based on the CT-scan assessment scheme for Covid-19 patients. This retrospective study was conducted on Covid-19 patients referred to Razi, Golestan, and Imam Khomeini hospitals in Ahvaz in 2021. For this, 108 chest CT scans were gathered from clinically suspected Covid-19 patients undergoing Reverse Transcription Polymerase Chain Reaction (RT-PCR) testing. Using the CO-RADS classification, four radiologists assessed the pulmonary involvement in chest CT images of Covid-19 patients and reported the involvement rate based on a six-point scale ranging from 1 (very low) to 6 (very high). The Interobserver Agreement (IOA) was measured using the intraclass correlation coefficient (ICC). Ultimately, the area under the curve (AUC) results in relation to the CO-RADS classification were compared with those results obtained from RT-PCR and the clinical diagnosis of Covid-19. A significant correlation was observed between the diagnostic performance of observers (p<0.0001). The mean ICC value for evaluating the CT images of observers was 0.954 (CI: 95%; 0.929-0.989; p< 0.0001). Likewise, the mean AUC value was 0.846 (CI: 95%; 0.763-0.908; p<0.0001) for predicting RT-PCR outcome and 0.924 (CI: 95%; 0.857-0.996) for the clinical diagnosis.

The results of this study indicate the efficacy of the CO-RADS classification assessment scheme for the pulmonary involvement in Covid-19 in predicting Covid-19 in patients with moderate to severe symptoms using chest CT images, as supported by a significant IOA value‎.