Peijie Lyu1, Xing Liu1, Rui Zhang1, Lei Shi2, Jianbo Gao1. 1. From the The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province. 2. CT Research Collaboration, Siemens Healthineers Ltd, Guangzhou, Guangdong Province, China.
Abstract
OBJECTIVES: The aim of this study was to assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest computed tomography (CT) indicators and identify the CT characteristics of critical cases. MATERIALS AND METHODS: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A, n = 12), severe cases (group B, n = 15), and critical cases (group C, n = 24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher exact test, one-way analysis of variance, Kruskal-Wallis H test, and receiver operating characteristic analysis. RESULTS: Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern, and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A (69% sensitivity, 83% specificity, and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these 3 groups with high sensitivity (B + C vs A, 90%; C vs B, 92%), specificity (100%, 87%), and accuracy (92%, 90%). Critical cases had higher total severity score (>10) and higher total score for crazy-paving and consolidation (>4) than ordinary cases, and had higher mean lung density (>-779 HU) and full width at half maximum (>128 HU) but lower relative volume of normal lung density (≦50%) than ordinary/severe cases. In our critical cases, 8 patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and 2 of them had died. CONCLUSIONS: A rapid, accurate severity assessment of COVID-19 pneumonia based on chest CT would be feasible and could provide help for making management decisions, especially for the critical cases.
OBJECTIVES: The aim of this study was to assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest computed tomography (CT) indicators and identify the CT characteristics of critical cases. MATERIALS AND METHODS: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A, n = 12), severe cases (group B, n = 15), and critical cases (group C, n = 24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher exact test, one-way analysis of variance, Kruskal-Wallis H test, and receiver operating characteristic analysis. RESULTS: Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern, and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A (69% sensitivity, 83% specificity, and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these 3 groups with high sensitivity (B + C vs A, 90%; C vs B, 92%), specificity (100%, 87%), and accuracy (92%, 90%). Critical cases had higher total severity score (>10) and higher total score for crazy-paving and consolidation (>4) than ordinary cases, and had higher mean lung density (>-779 HU) and full width at half maximum (>128 HU) but lower relative volume of normal lung density (≦50%) than ordinary/severe cases. In our critical cases, 8 patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and 2 of them had died. CONCLUSIONS: A rapid, accurate severity assessment of COVID-19 pneumonia based on chest CT would be feasible and could provide help for making management decisions, especially for the critical cases.
Authors: Marie Laure Chabi; Ophélie Dana; Titouan Kennel; Alexia Gence-Breney; Hélène Salvator; Marie Christine Ballester; Marc Vasse; Anne Laure Brun; François Mellot; Philippe A Grenier Journal: Diagnostics (Basel) Date: 2021-05-14
Authors: Shahabedin Nabavi; Azar Ejmalian; Mohsen Ebrahimi Moghaddam; Ahmad Ali Abin; Alejandro F Frangi; Mohammad Mohammadi; Hamidreza Saligheh Rad Journal: Comput Biol Med Date: 2021-06-23 Impact factor: 6.698