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Background: The coronavirus disease 2019 (COVID-19) is an emerging pandemic of global public health concern. We aimed to summarize the characteristics of COVID-19 patients in the early stage of the pandemic and explore the risk factors of disease progression.Methods: We conducted a systematic review with meta-analysis, searching three databases for studies published between January 1, 2020, and March 18, 2020. We used random-effects models to calculate the 95% confidence intervals of pooled estimated prevalence and the odds ratio between the severe and nonsevere cases.Results: Ninety studies involving 16,526 COVID-19 patients were included. Hypertension (19.1%) and diabetes (9.5%) were the most common comorbidities. The most prevalent clinical symptoms were fever (78.4%), cough (58.5%), and fatigue (26.4%). Increased serum ferritin (74.2%), high C-reactive protein (73.3%), and high erythrocyte sedimentation rate (ESR) (72.2%) were the most frequently reported laboratory abnormalities. Most patients had bilateral lung involvement (82.2%) and showed peripheral (66.9%) and subpleural (62.1%) distribution, with multifocal involvement (73.1%). And the most common CT features were vascular enlargement (64.3%), ground-glass opacity (GGO) (60.7%), and thickened interlobular septa (55.1%). Respiratory failure was the most common complication (30.7%) and the overall case-fatality rate (CFR) was 4.2%. Moreover, male, history of smoking, and comorbidities might influence the prognosis. Most clinical symptoms such as fever, high fever, cough, sputum production, fatigue, shortness of breath, dyspnoea, and abdominal pain were linked to the severity of disease. Some specific laboratory indicators implied the deterioration of disease, such as leucocytosis, lymphopenia, platelet, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, creatinine, creatine kinase (CK), lactic dehydrogenase (LDH), C-reactive protein, procalcitonin (PCT), and D-dimer. Besides, the risk of bilateral pneumonia, consolidation, pleural effusion, and enlarged mediastinal nodes was higher in severe cases.Conclusions: Most COVID-19 patients have fever and cough with lymphopenia and increased inflammatory indices, and the main CT feature is GGO involved bilateral lung. Patients with comorbidities and worse clinical symptoms, laboratory characteristics, and CT findings tend to have poor disease progression. respectively. According to the prior studies, the SARS-CoV-2, a betacoronavirus, is both similar and different to the SARS-CoV and MERS-CoV (3). As reported, the mortality of SARS-CoV and MERS-CoV is higher than that of SARS-CoV-2. But the pandemic caused by COVID-19 has exceeded the previous two diseases (4).It is important to find out the epidemiological, clinical, laboratory and image features of patients with SARS-CoV-2 infection and risk factors of severe illness for better prevention and treatment of this disease. Large quantities of researches have made effort to understand this subject, but most were case series, cross-sectional ...

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Background In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). Methods The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). Results The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell’s concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. Conclusions CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

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