Background: There are few reports on the chest computed tomography (CT) imaging features of children with coronavirus disease 2019 (COVID-19), and most reports involve small sample sizes. Objectives: To systematically analyze the chest CT imaging features of children with COVID-19 and provide references for clinical practice. Data sources: We searched PubMed, Web of Science, and Embase; data published by Johns Hopkins University; and Chinese databases CNKI, Wanfang, and Chongqing Weipu. Methods: Reports on chest CT imaging features of children with COVID-19 from January 1, 2020 to August 10, 2020, were analyzed retrospectively and a meta-analysis carried out using Stata12.0 software. Results: Thirty-seven articles (1747 children) were included in this study. The heterogeneity of meta-analysis results ranged from 0% to 90.5%. The overall rate of abnormal lung CT findings was 63.2% (95% confidence interval [CI]: 55.8%–70.6%), with a rate of 61.0% (95% CI: 50.8%–71.2%) in China and 67.8% (95% CI: 57.1%–78.4%) in the rest of the world in the subgroup analysis. The incidence of ground-glass opacities was 39.5% (95% CI: 30.7%–48.3%), multiple lung lobe lesions was 65.1% (95% CI: 55.1%–67.9%), and bilateral lung lesions was 61.5% (95% CI: 58.8%–72.2%). Other imaging features included nodules (25.7%), patchy shadows (36.8%), halo sign (24.8%), consolidation (24.1%), air bronchogram signs (11.2%), cord-like shadows (9.7%), crazy-paving pattern (6.1%), and pleural effusion (9.1%). Two articles reported 3 cases of white lung, another reported 2 cases of pneumothorax, and another 1 case of bullae. Conclusions: The lung CT results of children with COVID-19 are usually normal or slightly atypical. The lung lesions of COVID-19 pediatric patients mostly involve both lungs or multiple lobes, and the common manifestations are patchy shadows, ground-glass opacities, consolidation, partial air bronchogram signs, nodules, and halo signs; white lung, pleural effusion, and paving stone signs are rare. Therefore, chest CT has limited value as a screening tool for children with COVID-19 and can only be used as an auxiliary assessment tool.
Objectives: To systematically analyze the chest CT imaging features of children with COVID-19 and provide references for clinical practice. Methods: We searched PubMed, Web of Science, and Embase; data published by Johns Hopkins University; and Chinese databases CNKI, Wanfang, and Chongqing Weipu. Reports on chest CT imaging features of children with COVID-19 from January 1, 2020, to August 10, 2020, were analyzed retrospectively and a meta-analysis carried out using Stata12.0 software. Results: Thirty-seven articles (1747 children) were included in this study. The overall rate of abnormal lung CT findings was 63.2% (95% confidence interval [CI]: 55.8-70.6%), with a rate of 61.0% (95% CI: 50.8-71.2%) in China and 67.8% (95% CI: 57.1-78.4%) in the rest of the world in the subgroup analysis. The incidence of ground-glass opacities was 39.5% (95% CI: 30.7-48.3%), multiple lung lobe lesions 65.1% (95% CI: 55.1-67.9%), and bilateral lung lesions 61.5% (95% CI: 58.8-72.2%). Other imaging features included nodules (25.7%), patchy shadows (36.8%), halo sign (24.8%), consolidation (24.1%), air bronchogram signs (11.2%), cord-like shadows (9.7%), crazy-paving pattern (6.1%), and pleural effusion (9.1%). Two articles reported three cases of white lung, another reported two cases of pneumothorax, and another one case of bullae. CONCLUSION: The lung CT results of children with COVID-19 are usually normal or slightly atypica, with a low sensitivity and specificity compared with that in adults. The lung lesions of COVID-19 pediatric patients mostly involve both lungs or multiple lobes, and the common manifestations are patchy shadows, ground-glass opacities, consolidation, partial air bronchogram signs, nodules, and halo signs; white lung, pleural effusion, and paving stone signs are rare. CLINICAL IMPACT: Therefore, chest CT has limited value as a screening tool for children with COVID-19 and can only be used as an auxiliary assessment tool.
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