Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity
Open accessAbstract
Purpose To assess the relationships among pulmonary vascular enlargement, computed tomography (CT) findings quantified with software, and coronavirus disease (COVID-19) severity.Materials and methods Ultra-high-resolution (UHR) CT images of 87 patients (50 males, 37 females; median age, 63 years) with COVID-19 confirmed using real-time polymerase chain reaction were analyzed. The maximum subsegmental vascular diameter was measured on CT. Total CT lung volume (CTLV total) and lesion extent (ratio of lesion volume to CTLV total) of ground-glass opacities, reticulation, and consolidation were measured using software. Maximum pulmonary vascular diameter and lesion extent were analyzed using Spearman's correlation analysis. Logistic regression analysis was performed on CT results to predict disease severity. We also assessed changes in these measures on follow-up scans in 16 patients.Results All 23 patients with severe and critical illness had vascular enlargement (> 4 mm). Pulmonary vascular enlargement (odds ratio 3.05, p = 0.018) and CT lesion extent (odds ratio 1.07, p = 0.002) were independent predictors of disease severity after adjustment for age and comorbidities. On follow-up CT, vascular diameter and CT lesion volume decreased (p = 0.001, p = 0.002; respectively), but CTLV total did not change significantly.Conclusion Subsegmental vascular enlargement is a notable finding to predict acute COVID-19 disease severity.
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The COVID-19 pandemic increased the number of COVID-19-related studies from Japan, with a significant increase in such research focusing on AI.
“Furthermore, owing to the coronavirus disease 2019 (COVID-19) pandemic, the number of COVID-19-related studies from Japan has increased [54–61], with a significant increase in such research focusing on AI [62–64].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [55]
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- Authors
- Aoki, R., Iwasawa, T., Hagiwara, E., Komatsu, S., Utsunomiya, D., & Ogura, T.
- Affiliations
- Kanagawa Cardiovascular and Respiratory Center (JP)
- Corresponding author
- Iwasawa, Tae
- Venue
- Japanese Journal of Radiology
- Published
- 2021-01-27
- Volume
- 39
- Issue
- 5
- Pages
- 451-458
- ISSN
- 1867-1071 (ISSN-L)
- 1867-108X
- PMC
- PMC7838849
- OpenAlex
- W3125915845
- Cited by
- 26
- References
- 29
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 8 · 2022: 6 · 2021: 6
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Japanese Journal of Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Keywords
- COVID-19 pandemic, disease severity prediction, ultra-high-resolution CT, ground-glass opacities
- MeSH terms
- COVID-19, SARS-CoV-2, Adult, Aged, Aged, 80 and over, Female, Humans, Lung, Male, Middle Aged, Retrospective Studies, Severity of Illness Index, Tomography, X-Ray Computed, Young Adult
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