About: Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics

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About: Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics
Creator	Wei, Wei Cheng, Qi Hu, Xiao-Wen Ya-Qiong Ge, & Zhao, Ying-Ming
Source	Medline; PMC
abstract	OBJECTIVE: To explore the value of CT texture analysis (CTTA) for determining coronavirus disease 2019 (COVID-19) severity. METHODS: The clinical and CT data of 81 patients with COVID-19 were retrospectively analyzed. The texture features were extracted using LK2.1. The two-sample t test or Mann–Whitney U test was used to find the significant features. Minimum redundancy and maximum relevance (MRMR) method was performed to find the features with maximum correlation and minimum redundancy. These features were then used to construct a radiomics texture model to discriminate the severe patients using multivariate logistic regression method. Besides, a clinical model was also built. ROC analyses were conducted to evaluate the performance of two models. The correlations of clinical features and textural features were analyzed using the Spearman correlation analysis. RESULTS: Of the total cases included, 60 were common and 21 were severe. (1) For textural features, 20 radiomics features selected by MRMR showed good performance in discriminating the two groups (AUC > 70%). (2) For clinical features, chi-square tests or Mann–Whitney U tests identified 16 clinical features as significant, and 12 were discriminative (p < 0.05) between two groups analyzed by univariate logistic analysis. Of these, 10 had an AUC > 70%. (3) Prediction models for textural features and clinical features were established, and both showed high predictive accuracy. The AUC values of textural features and clinical features were 0.93 (0.86–1.00) and 0.95 (0.95–0.99), respectively. (4) The Spearman correlation analysis showed that most textural and clinical features had above-moderate correlations with disease severity (> 0.4). CONCLUSION: Texture analysis can provide reliable and objective information for differential diagnosis of COVID-19. KEY POINTS: • CT texture analysis can well differentiate common and severe COVID-19 patients. • Some textural features showed above-moderate correlations with clinical factors. • CT texture analysis can provide useful information to judge the severity of COVID-19. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-07012-3) contains supplementary material, which is available to authorized users.
has issue date	2020-07-01(xsd:dateTime)
bibo:doi	10.1007/s00330-020-07012-3
bibo:pmid	32613287
has license	no-cc
sha1sum (hex)	66196f2d4a8db73daa6e1b8e23528d3eb1325a27
schema:url	https://doi.org/10.1007/s00330-020-07012-3
resource representing a document's title	Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics
has PubMed Central identifier	PMC7327490
has PubMed identifier	32613287
schema:publication	Eur Radiol
resource representing a document's body	covid:66196f2d4a8db73daa6e1b8e23528d3eb1325a27#body_text
is schema:about of	named entity 'clinical' named entity 'models' named entity 'features' named entity 'features' named entity 'Identification' named entity 'REDUNDANCY' named entity 'SAMPLE' named entity 'FEATURES' named entity 'RESULTS' named entity 'COMMON' named entity 'VALUE' named entity 'CORRELATION' named entity 'METHOD' named entity 'cases' named entity 'features' named entity 'AUC' named entity 'COVID-19' named entity 'COVID-19' named entity 'features' named entity 'Results' named entity 'coronavirus disease 2019' named entity 'total' named entity 'construct' named entity 'radiomics' named entity 'CTTA' named entity 'clinical features' named entity 'clinical features' named entity 'COVID-19' named entity 'GGO' named entity 'lesion' named entity 'radiomics' named entity 'Histogram' named entity 'lymphocytes' named entity 'CT images' named entity 'likelihood ratio test' named entity 'inflammation' named entity 'gray-level co-occurrence matrix' named entity 'AUC values' named entity 'COVID' named entity 'clinical features' named entity 'sensitivity, specificity' named entity 'clinical diagnosis' named entity 'institutional review board' named entity 'inflammation' named entity 'lesion' named entity 'differential diagnosis' named entity 'emphysema' named entity 'CD3' named entity 'GGO' named entity 'institutional review board' named entity 'radiomics' named entity 'radiomics' named entity 'COVID' named entity 'GGO' named entity 'CD3' named entity 'Wuhan' named entity 'lymphocyte' named entity '0.80' named entity 'CTTA' named entity 'correlation' named entity 'lung' named entity 'COVID' named entity 'radiologist' named entity 'COVID' named entity 'cross-validation' named entity 'lung' named entity 'sensitivity, and specificity' named entity 'radiomics' named entity 'COVID-19' named entity 'lymphocyte count' named entity 'CD4' named entity 'radiomics' named entity 'tube voltage' named entity 'logistic regression' named entity 'radiomics' named entity 'prognosis' named entity 'sputum'

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