id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-310150-j1mvr9r9	Wei, Wei	Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics	2020-07-01		.txt	text/plain	3102	225	53	title: Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics These features were then used to construct a radiomics texture model to discriminate the severe patients using multivariate logistic regression method. (4) The Spearman correlation analysis showed that most textural and clinical features had above-moderate correlations with disease severity (> 0.4). Both the clinical model and radiomics signature showed good performance in discriminating patients with common and severe COVID-19. Both the clinical and radiomics models showed good stability, indicating that the texture analysis was valuable for discriminating common and severe COVID-19 patients, and that the results were not due to overfitting. The strong correlation was found between inflammatory score and partial wavelet transform features and region size matrix GLSZM features (> 0.7), indicating that these image features are closely related to disease severity and can be used for clinical type classification of the COVID-19 patients.	./cache/cord-310150-j1mvr9r9.txt	./txt/cord-310150-j1mvr9r9.txt