Highlights:GGO with crazy-paving patterns or interlobular septa thickening were common signs Fewer lesions were identified in the younger and adolescent age groups Distribution of lesions in the lungs showed age-related differences ABSTRACT J o u r n a l P r e -p r o o f Purpose: We aimed to compare chest HRCT lung signs identified in scans of differently aged patients with COVID-19 infections. Methods: Case data of patients diagnosed with COVID-19 infection in Hangzhou City, Zhejiang Province in China were collected, and chest HRCT signs of infected patients in four age groups (<18 years, 18-44 years, 45-59 years, ≥60 years) were compared.Results: Small patchy, ground-glass opacity (GGO), and consolidations were the main HRCT signs in 98 patients with confirmed COVID-19 infections. Patients aged 45-59 years and aged ≥60 years had more bilateral lung, lung lobe, and lung field involvement, and greater lesion numbers than patients <18 years. GGO accompanied with the interlobular septa thickening or a crazy-paving pattern, consolidation, and air bronchogram sign were more common in patients aged 45-59 years, and ≥60 years, than in those aged <18 years, and aged 18-44 years. Conclusions:Chest HRCT manifestations in patients with COVID-19 are related to patient's age, and HRCT signs may be milder in younger patients.

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With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.