Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study.

Background: Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic. Aims: We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus. Methods: Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables. Results: RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (ß = 0.097, p < 0.05) and pleural effusion (ß = 10.631, p < 0.05) on COVID-19 pneumonia patients. Conclusion: The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

Correlation of laboratory parameters and Chest CT findings in young adults with COVID-19 and comparison of imaging findings with children.

PURPOSE: We aimed to compare COVID-19 imaging findings of young adults (19-35 years of age) with those of children (0-18 years) and to correlate imaging findings of young adults with their laboratory tests. MATERIALS AND METHODS: This retrospective study included Real Time-Polymerase Chain Reaction (RT-PCR) confirmed 130 young adults (mean age: 28.39 ± 4.77; 65 male, 65 female) and 36 children (mean age: 12.41 ± 4.51; 17 male, 19 female), between March and June 2020. COVID-19 related imaging findings on chest CT were examined in young adults and compared with children by the Mann-Whitney U, and Chi-square or Fisher's exact test. Laboratory examinations of young adults were assessed in terms of correlation with radiological findings by the Spearman's correlation analysis. RESULTS: Bilateral multiple distributions (p = 0.014), subpleural involvement, and pleural thickening (p = 0.004), GGOs with internal consolidations were more frequent in adults (p = 0.009). Infiltrations were significantly larger than 20 mm in young adults (p = 0.011). The rates of feeding vessel sign, vascular enlargement, and halo sign were significantly higher in young adults (p < 0.003). Highly significant positive correlations were found between radiological and biochemical parameters. CONCLUSION: Distribution, size, and pattern of COVID-19 related imaging findings differed in children and young adults. Radiological findings were correlated with biochemical parameters but not with blood count results of young adults.

Covid-19: Correlation of Early Chest Computed Tomography Findings With the Course of Disease.

OBJECTIVE: To investigate the role of chest computed tomography (CT) examinations acquired early after initial onset of symptoms in predicting disease course in coronavirus disease 2019. METHODS: Two hundred sixty-two patients were categorized according to intensive care unit (ICU) admission, survival, length of hospital stay, and reverse transcriptase-polymerase chain reaction positivity. Mean time interval between the onset of symptoms and CT scan was 5.2 ± 2.3 days. Groups were compared using Student t test, Mann-Whitney U, and Fisher exact tests. RESULTS: In the ICU (+) and died groups, crazy paving (64% and 57.1%), bronchus distortion (68% and 66.7%), bronchiectasis-bronchiolectasis (80% and 76.2%), air trapping (52% and 52.4%) and mediastinal-hilar lymph node enlargement (52% and 52.4%) were significantly more encountered (P < 0,05). These findings were correlated with longer hospital stays (P < 0.05). There were no differences between reverse transcriptase-polymerase chain reaction-positive and -negative patients except bronchiectasis-bronchiolectasis. CONCLUSION: Computed tomography examinations performed early after the onset of symptoms may help in predicting disease course and planning of resources, such as ICU beds.