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Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology.

ABSTRACT: In this study, we ascertained the chest CT data of 60 patients admitted to 3 hospitals in Chongqing with confirmed COVID-19. We conducted anatomical and pathological analyses to elucidate the possible reasons for the distribution, morphology, and characteristics of COVID-19 in chest CT. We also shared a semiquantitative scoring of affected lung segments, which was recommended by our local medical association. This scoring system was applied to quantify the severity of the disease. The most frequent imaging findings of COVID-19 were subpleural ground glass opacities and consolidation; there was a significant difference in semiquantitative scores between the early, progressive, and severe stages of the disease. We conclude that the chest CT findings of COVID-19 showed certain characteristics because of the anatomical features of the human body and pathological changes caused by the virus. Therefore, chest CT is a valuable tool for facilitating the diagnosis of COVID-19 and semiquantitative scoring of affected lung segments may further elucidate diagnosis and assessment of disease severity. This will assist healthcare workers in diagnosing COVID-19 and assessing disease severity, facilitate the selection of appropriate treatment options, which is important for reducing the spread of the virus, saving lives, and controlling the pandemic.

SUBMITTER: Zhang H

PROVIDER: S-EPMC7473568 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology.

Zhang Hao H Jiang Xu-Jing XJ Liu Xiao-Hua XH Ma Hong H Zhang Ya-Hong YH Rao Yue Y Li Lin L Xu Hai-Yan HY Lyu Fa-Jin FJ

PloS one 20200904 9

In this study, we ascertained the chest CT data of 60 patients admitted to 3 hospitals in Chongqing with confirmed COVID-19. We conducted anatomical and pathological analyses to elucidate the possible reasons for the distribution, morphology, and characteristics of COVID-19 in chest CT. We also shared a semiquantitative scoring of affected lung segments, which was recommended by our local medical association. This scoring system was applied to quantify the severity of the disease. The most frequ ...[more]

PMID: 32886711

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Project description:PurposeTo compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time.Methods and materialsThis retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated.ResultsTwo hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different (< 0.001): CT-SS > 3L-CT-SS > chest CT score > TSS.ConclusionAll chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation.Key points• All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. • Chest CT scoring system and TSS had the highest specificity. • Both TSS and m-TSS consumed the least time compared to the other three scoring systems.

Project description:PurposeTo study the extent of pulmonary involvement in coronavirus 19 (COVID-19) with quantitative CT and to assess the impact of disease burden on opacity visibility on chest radiographs.Materials and methodsThis retrospective study included 20 pairs of CT scans and same-day chest radiographs from 17 patients with COVID-19, along with 20 chest radiographs of controls. All pulmonary opacities were semiautomatically segmented on CT images, producing an anteroposterior projection image to match the corresponding frontal chest radiograph. The quantitative CT lung opacification mass (QCTmass) was defined as (opacity attenuation value + 1000 HU)/1000 × 1.065 (g/mL) × combined volume (cm3) of the individual opacities. Eight thoracic radiologists reviewed the 40 radiographs, and a receiver operating characteristic curve analysis was performed for the detection of lung opacities. Logistic regression analysis was performed to identify factors affecting opacity visibility on chest radiographs.ResultsThe mean QCTmass per patient was 72.4 g ± 120.8 (range, 0.7-420.7 g), and opacities occupied 3.2% ± 5.8 (range, 0.1%-19.8%) and 13.9% ± 18.0 (range, 0.5%-57.8%) of the lung area on the CT images and projected images, respectively. The radiographs had a median sensitivity of 25% and specificity of 90% among radiologists. Nineteen of 186 opacities were visible on chest radiographs, and a median area of 55.8% of the projected images was identifiable on radiographs. Logistic regression analysis showed that QCTmass (P < .001) and combined opacity volume (P < .001) significantly affected opacity visibility on radiographs.ConclusionQCTmass varied among patients with COVID-19. Chest radiographs had high specificity for detecting lung opacities in COVID-19 but a low sensitivity. QCTmass and combined opacity volume were significant determinants of opacity visibility on radiographs.Earlier incorrect version appeared online. This article was corrected on April 6, 2020 and December 14, 2020.Supplemental material is available for this article.© RSNA, 2020.

Project description:PurposeWe noted incidental findings on chest computed tomography (CT) imaging of expiratory central airway collapse (ECAC) in dyspneic patients after military deployment to southwest Asia (mainly Iraq and Afghanistan). We developed a standardized chest CT protocol with dynamic expiration to enhance diagnostic reliability and investigated demographic, clinical, and deployment characteristics possibly associated with ECAC.Materials and methodsWe calculated ECAC in 62 consecutive post-9/11 deployers with dyspnea who underwent multi-detector chest CT acquisition. ECAC was defined as ≥70% reduction in the cross-sectional tracheal area at dynamic expiration. We compared demographics (age, smoking, body mass index), comorbid conditions (gastroesophageal reflux, obstructive sleep apnea [OSA]), and clinical findings (air trapping, forced expiratory volume in 1 second percent predicted) in deployers with and without ECAC. We examined associations between ECAC and forced expiratory volume in 1 second percent predicted, air trapping, OSA, deployment duration, and blast exposure.ResultsAmong 62 consecutive deployers with persistent dyspnea, 37% had ECAC. Three had severe (>85%) collapse. Those with ECAC were older (mean age 46 vs. 40 y, P=0.02), but no other demographic or clinical characteristics were statistically different among the groups. Although not statistically significant, ECAC odds were 1.5 times higher (95% confidence interval: 0.9, 2.5) for each additional year of southwest Asia deployment. Deployers with ECAC had 1.6 times greater odds (95% confidence interval: 0.5, 4.8) of OSA.ConclusionsFindings suggest that ECAC is common in symptomatic southwest Asia deployers. Chest high-resolution CT with dynamic expiration may provide an insight into the causes of dyspnea in this population, although risk factors for ECAC remain to be determined. A standardized semiquantitative approach to CT-based assessment of ECAC should improve reliable diagnosis in dyspneic patients.

Dataset Information

Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology.

Publications

Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology.

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