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Objective: To investigate the performance of semi-quantitative CT (SCT) and automated quantitative CT (QCT) analyses for differentiating mild disease from the severe disease in COVID-19 pneumonia.
Materials and Methods: Sixty-seven laboratory confirmed COVID-19 patients were enrolled. The patients were grouped into mild and severe disease regarding clinical features. CT images were evaluated by three observers independently. Three different SCT scoring methods and QCT analysis were performed. The two disease groups were compared in terms of SCT and QCT parameters. Intraclass correlation coefficient was used to investigate inter-rater reliability. The performance of SCT and QCT in the differentiation of mild disease and severe disease was evaluated using receiver operating characteristics (ROC) analysis.
Results: Inter-rater reliability was excellent for all SCT scores. SCT and QCT scores were significantly different between two disease groups (p<0.05). Five-point score showed the best performance regarding to area under curve (AUC) values. The cut-off value of >7 for 5-point score had 88.89% sensitivity and 82.76% specificity and cut-off value of >10.29% for QCT score (%) had 75.00% sensitivity and 98.04% specificity for differentiating the mild disease from severe disease.
Conclusion: QCT may play an important role in the management of COVID-19 pneumonia with its high specificity values.