tetano
Editor, Senior Moderator
Chin J Acad Radiol. 2020 May 11:1-6. doi: 10.1007/s42058-020-00034-2. [Epub ahead of print]
Lung involvement in patients with coronavirus disease-19 (COVID-19): a retrospective study based on quantitative CT findings.
Yu N[SUP]1[/SUP], Shen C[SUP]2[/SUP], Yu Y[SUP]1[/SUP], Dang M[SUP]3[/SUP], Cai S[SUP]4[/SUP], Guo Y[SUP]5[/SUP].
Author information
Abstract
Purpose:
To explore lung involvement in patients with coronavirus disease-19 (COVID-19) using quantitative computed tomography (QCT).
Methods:
A total of 52 patients with COVID-19 who were admitted to three hospitals in China from January 23, 2020 to February 1, 2020 were retrospectively analyzed using QCT. The accuracy of QCT segmentation was assessed. The relationship between the time from symptom onset to initial CT and QCT parameters acquired on the initial CT were explored.
Results:
First, the ability of QCT to detect and segment lesions was investigated and it was unveiled that results of segmentation of the majority of cases (42/52) were satisfactory and for 8 out of 52 patients, the images depicted lesions with miss-segmentation; besides, 2 out of 52 cases had negative finding on chest CT achieved by both radiologists and QCT. QCT-related parameters showed to have a relationship with the time from symptom onset to initial CT. In the early-stage (0-3 days), the percentage of lung involvement was 4%, with a mean density of - 462 ? 99 HU. The peak density of lesions appeared at the range of - 500 to - 700 HU on density histogram. In the intermediate-stage (4-6 days), the mean percentage of lung involvement noticeably increased compared with that in early stage (7%, p < 0.05). In late stage (7-14 days), the percentage of lung involvement decreased to 5%. The mean density of lesions was the highest (- 430 ? 80), and heterogeneity density distribution showed a dual-peak on density histogram.
Conclusion:
COVID-19 can be promptly detected by QCT. In addition, the QCT-related parameters can highly facilitate assessment of pulmonary involvement.
? Springer Nature Singapore Pte Ltd. 2020.
KEYWORDS:
COVID-19; Computed tomography; Lung
PMID:32395696PMCID:PMC7211979DOI:10.1007/s42058-020-00034-2
Lung involvement in patients with coronavirus disease-19 (COVID-19): a retrospective study based on quantitative CT findings.
Yu N[SUP]1[/SUP], Shen C[SUP]2[/SUP], Yu Y[SUP]1[/SUP], Dang M[SUP]3[/SUP], Cai S[SUP]4[/SUP], Guo Y[SUP]5[/SUP].
Author information
Abstract
Purpose:
To explore lung involvement in patients with coronavirus disease-19 (COVID-19) using quantitative computed tomography (QCT).
Methods:
A total of 52 patients with COVID-19 who were admitted to three hospitals in China from January 23, 2020 to February 1, 2020 were retrospectively analyzed using QCT. The accuracy of QCT segmentation was assessed. The relationship between the time from symptom onset to initial CT and QCT parameters acquired on the initial CT were explored.
Results:
First, the ability of QCT to detect and segment lesions was investigated and it was unveiled that results of segmentation of the majority of cases (42/52) were satisfactory and for 8 out of 52 patients, the images depicted lesions with miss-segmentation; besides, 2 out of 52 cases had negative finding on chest CT achieved by both radiologists and QCT. QCT-related parameters showed to have a relationship with the time from symptom onset to initial CT. In the early-stage (0-3 days), the percentage of lung involvement was 4%, with a mean density of - 462 ? 99 HU. The peak density of lesions appeared at the range of - 500 to - 700 HU on density histogram. In the intermediate-stage (4-6 days), the mean percentage of lung involvement noticeably increased compared with that in early stage (7%, p < 0.05). In late stage (7-14 days), the percentage of lung involvement decreased to 5%. The mean density of lesions was the highest (- 430 ? 80), and heterogeneity density distribution showed a dual-peak on density histogram.
Conclusion:
COVID-19 can be promptly detected by QCT. In addition, the QCT-related parameters can highly facilitate assessment of pulmonary involvement.
? Springer Nature Singapore Pte Ltd. 2020.
KEYWORDS:
COVID-19; Computed tomography; Lung
PMID:32395696PMCID:PMC7211979DOI:10.1007/s42058-020-00034-2