Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID‐19 pneumonia
Abstract Introduction Computed tomography (CT) can be effective for the early screening and diagnosis of COVID‐19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission). Methods We included all COVID‐19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3‐10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground‐glass opacity, GGO) and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity. Results The ratios of some representative features differed between the remission stage and the progression phase, such as round‐shape lesion (8.0% vs 34.4%), GGO (65.0% vs 87.5%), consolidation (62.0% vs 31.3%), large cable sign (59.0% vs 9.4%) and crazy‐paving sign (20.0% vs 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign and crazy‐paving sign were the most significant factors, based on a variable named “prediction of progression or remission” (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875). Conclusion CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy‐paving sign, may increase the recognition of the intrapulmonary development of COVID‐19..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:75 |
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| Enthalten in: |
International Journal of Clinical Practice - 75(2021), 4 |
| Beteiligte Personen: |
Liao, Jie‐lan [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
Copyright © 2021 John Wiley & Sons Ltd |
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| Umfang: |
8 |
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| doi: |
10.1111/ijcp.13760 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract Introduction Computed tomography (CT) can be effective for the early screening and diagnosis of COVID‐19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission). Methods We included all COVID‐19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3‐10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground‐glass opacity, GGO) and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity. Results The ratios of some representative features differed between the remission stage and the progression phase, such as round‐shape lesion (8.0% vs 34.4%), GGO (65.0% vs 87.5%), consolidation (62.0% vs 31.3%), large cable sign (59.0% vs 9.4%) and crazy‐paving sign (20.0% vs 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign and crazy‐paving sign were the most significant factors, based on a variable named “prediction of progression or remission” (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875). Conclusion CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy‐paving sign, may increase the recognition of the intrapulmonary development of COVID‐19. | ||
| 700 | 1 | |a Chen, Yu |4 aut | |
| 700 | 1 | |a Huang, Chong‐Quan |4 aut | |
| 700 | 1 | |a He, Gui‐qing |4 aut | |
| 700 | 1 | |a Du, Ji‐Cheng |4 aut | |
| 700 | 1 | |a Chen, Que‐Lu |4 aut | |
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