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A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19...

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Autores principales: Chen, Xiaofeng, Tang, Yanyan, Mo, Yongkang, Li, Shengkai, Lin, Daiying, Yang, Zhijian, Yang, Zhiqi, Sun, Hongfu, Qiu, Jinming, Liao, Yuting, Xiao, Jianning, Chen, Xiangguang, Wu, Xianheng, Wu, Renhua, Dai, Zhuozhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160614/
https://www.ncbi.nlm.nih.gov/pubmed/32300971
http://dx.doi.org/10.1007/s00330-020-06829-2
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author Chen, Xiaofeng
Tang, Yanyan
Mo, Yongkang
Li, Shengkai
Lin, Daiying
Yang, Zhijian
Yang, Zhiqi
Sun, Hongfu
Qiu, Jinming
Liao, Yuting
Xiao, Jianning
Chen, Xiangguang
Wu, Xianheng
Wu, Renhua
Dai, Zhuozhi
author_facet Chen, Xiaofeng
Tang, Yanyan
Mo, Yongkang
Li, Shengkai
Lin, Daiying
Yang, Zhijian
Yang, Zhiqi
Sun, Hongfu
Qiu, Jinming
Liao, Yuting
Xiao, Jianning
Chen, Xiangguang
Wu, Xianheng
Wu, Renhua
Dai, Zhuozhi
author_sort Chen, Xiaofeng
collection PubMed
description OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. METHODS: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student’s t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. RESULTS: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1–3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. CONCLUSIONS: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. KEY POINTS: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-71606142020-04-16 A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study Chen, Xiaofeng Tang, Yanyan Mo, Yongkang Li, Shengkai Lin, Daiying Yang, Zhijian Yang, Zhiqi Sun, Hongfu Qiu, Jinming Liao, Yuting Xiao, Jianning Chen, Xiangguang Wu, Xianheng Wu, Renhua Dai, Zhuozhi Eur Radiol Chest OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. METHODS: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student’s t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. RESULTS: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1–3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. CONCLUSIONS: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. KEY POINTS: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-04-16 2020 /pmc/articles/PMC7160614/ /pubmed/32300971 http://dx.doi.org/10.1007/s00330-020-06829-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Chest
Chen, Xiaofeng
Tang, Yanyan
Mo, Yongkang
Li, Shengkai
Lin, Daiying
Yang, Zhijian
Yang, Zhiqi
Sun, Hongfu
Qiu, Jinming
Liao, Yuting
Xiao, Jianning
Chen, Xiangguang
Wu, Xianheng
Wu, Renhua
Dai, Zhuozhi
A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
title A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
title_full A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
title_fullStr A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
title_full_unstemmed A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
title_short A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
title_sort diagnostic model for coronavirus disease 2019 (covid-19) based on radiological semantic and clinical features: a multi-center study
topic Chest
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160614/
https://www.ncbi.nlm.nih.gov/pubmed/32300971
http://dx.doi.org/10.1007/s00330-020-06829-2
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