Visual and quantitative assessment of COVID-19 pneumonia on chest CT: The relationship with disease severity and clinical findings.

Kaya, Furkan; Konya, Petek Şarlak; Demirel, Emin; Demirtürk, Neşe; Orhan, Semiha; Ufuk, Furkan

BACKGROUND Lungs are the primary organ of involvement of COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. AIM We aimed to evaluate the visual and quantitative pneumonia severity on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. METHODS We retrospectively evaluated adult COVID-19 patients who underwent chest CT, clinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between variables. The Pearson correlation test analyzed the correlation between variables. The diagnostic performance of the variables was assessed using receiver operating characteristic (ROC) analysis was used. RESULTS The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with disease severity (p<0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r=730,p<0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. CONCLUSION The VSQS and QCT scores can help manage the COVID-19 and predict the duration of hospitalization.