A Comparison of CT Manifestations Between Coronavirus Disease 2019 (COVID-19) and Other Types of Viral Pneumonia.

BACKGROUND: Though imaging manifestations of COVID-19 and other types of viral pneumonia are similar, their clinical treatment methods differ. Accurate, non-invasive diagnostic methods using CT imaging can help develop an optimal therapeutic regimen for both conditions. OBJECTIVE: To compare the initial CT imaging features in COVID-19 with those in other types of viral pneumonia. METHODS: Clinical and imaging data of 51 patients with COVID-19 and 69 with other types of viral pneumonia were retrospectively studied. All significant imaging features (Youden index >0.3) were included for constituting the combined criteria for COVID-19 diagnosis, composed of two or more imaging features with a parallel model. McNemar's chi-square test or Fisher's exact test was used to compare the validity indices (sensitivity and specificity) among various criteria. RESULTS: Ground glass opacities (GGO) dominated density, peripheral distribution, unilateral lung, clear margin of lesion, rounded morphology, long axis parallel to the pleura, vascular thickening, and crazy-paving pattern were more common in COVID-19 (p <0.05). Consolidation-dominated density, both central and peripheral distributions, bilateral lung, indistinct margin of lesion, tree-inbud pattern, mediastinal or hilar lymphadenectasis, pleural effusion, and pleural thickening were more common in other types of viral pneumonia (p < 0.05). GGO-dominated density or long axis parallel to the pleura (with the highest sensitivity), and GGO-dominated density or long axis parallel to the pleura or vascular thickening (with the highest specificity) are well combined criteria of COVID-19. CONCLUSION: The initial CT imaging features are helpful for the differential diagnosis of COVID-19 and other types of viral pneumonia.

Inspiratory and Expiratory Chest High-resolution CT: Small-airway Disease Evaluation in Patients with COVID-19.

BACKGROUND: An outbreak of coronavirus disease 2019 (COVID-19) has occurred worldwide. However, the small-airway disease in patients with COVID-19 has not been explored. AIM: This study aimed to explore the small-airway disease in patients with COVID-19 using inspiratory and expiratory chest high-resolution computed tomography (CT). METHODS: This multicenter study included 108 patients with COVID-19. The patients were classified into five stages (0-IV) based on the CT images. The clinical and imaging data were compared among CT images in different stages. Patients were divided into three groups according to the time interval from the initial CT scan, and the clinical and air trapping data were compared among these groups. The correlation between clinical parameters and CT scores was evaluated. RESULTS: The clinical data, including age, frequency of breath shortness and dyspnea, neutrophil percentage, lymphocyte count, PaO2, PaCO2, SaO2, and time interval between the onset of illness and initial CT, showed significant differences among CT images in different stages. A significant difference in the CT score of air trapping was observed between stage I and stage III. A low negative correlation was found between the CT score of air trapping and the time interval between the onset of symptoms and initial CT. No significant difference was noted in the frequency and CT score of air trapping among different groups. CONCLUSION: Some patients with COVID-19 developed small-airway disease. Air trapping was more distinguished in the early stage of the disease and persisted during the 2-month follow-up. Longer-term follow-up studies are needed to confirm the findings.

Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study.

BACKGROUND: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. METHODS: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. RESULTS: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR,2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. CONCLUSION: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.