medRxiv preprintSevere acute respiratory syndrome coronavirus 2 (SARS-Cov-2) quickly became a major epidemic threat in the whole China. We analysed SARS-Cov-2 infected cases from Tibetan Autonomous Prefecture, and noted divergent characteristics of these Tibetans infected cases compared to Han Chinese, characterizing by a considerable proportion of asymptomatic carriers (21.7%), and few symptomatic patients with initial symptom of fever (7.7%). Here, we did a descriptive study on clinical characteristics of 18 asymptomatic individuals with SARS-CoV-2 infection. The median age of these asymptomatic carriers was 31 years and one third of them were students, aged under 20 years. Notably, some of asymptomatic carriers had recognizable changes in radiological and laboratory indexes. Our finding indicates a potentially big number of SARS-CoV-2 asymptomatic carriers in prevalent area, highlighting a necessity of screening individuals with close contact of infected patients, for a better control on the spread of SARS-CoV-2 infection.

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Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/ severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, ^ ORCID: Zekun Jiang,