Computerized tomographic imaging features and changes in peripheral blood T lymphocyte subsets in patients with coronavirus disease 2019
The aim of study was to investigate the relationship between the CT imaging features, peripheral blood T lymphocyte subsets, and disease severity in COVID-19 patients, and to clarify their clinical value in the early identification of severe cases. Hospitalized patients with confirmed COVID-19 (severe and mild) were retrospectively analyzed. A total of 106 patients were enrolled and divided into severe group (n = 17) and mild group (n = 89) according to the treatment protocol. The basic information, CT imaging scores, and T lymphocyte subset markers (CD3 + , CD4 + , CD8 + ) were compared between the two groups. The average age of the severe group was significantly higher than that of the mild group ( P < 0.01). The proportion of males was higher in the severe group ( P < 0.05), and the prevalence of comorbidities such as hypertension and diabetes was significantly higher ( P < 0.01). Clinically, fever (81.13 %) and cough (77.36 %) were the most common symptoms, with the severe group showing significantly higher peak body temperature ( P <0.01) and incidence of dyspnea ( P < 0.01). Among laboratory indicators, the severe group had significantly lower lymphocyte counts ( P < 0.01) and significantly higher C-reactive protein levels ( P < 0.01), with a more pronounced reduction in CD3 + T lymphocytes ( P < 0.05). The ratio of CD4+/CD8+ increased, but the difference was not statistically significant ( P > 0.05). The semi-quantitative CT scores showed a higher proportion of lung involvement in the severe group, and the total CT score was significantly higher than in the mild group ( P < 0.05). advanced age, male gender, comorbidities, reduced CD3 + T lymphocytes, and elevated chest CT scores are associated with the progression of COVID-19. Early identification of these indicators, combined with imaging assessment and comprehensive treatment focused on respiratory support, can effectively reduce the conversion rate to severe disease and mortality.
Authors
- L. Chen (ORCID: https://orcid.org/0000-0003-4141-3461)
- Xiaozhen Xia
Institutions
- Wenzhou Medical University (CN)
- Dongyang People's Hospital (CN)
Publication Details
- Journal
- Journal of Radiation Research and Applied Sciences
- Published
- 2025-08-09
- DOI
- https://doi.org/10.1016/j.jrras.2025.101856
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00