Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia
| Autor: | Kevin Li Chun Hsieh, Hsin Y. Liu, Gong Yau Lan, Hsin Yi Lai, Yuarn Jang Lee, Jen Chung Wu, Han Chuan Chuang |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19) COVID19 Short Communication Lesion volume Computed tomography medicine.disease_cause Lesion Prognosticator Outcome predictor medicine Humans Lung Coronavirus Retrospective Studies medicine.diagnostic_test business.industry SARS-CoV-2 Outbreak COVID-19 General Medicine medicine.disease Pneumonia Radiology medicine.symptom business Tomography X-Ray Computed |
| Zdroj: | Journal of the Formosan Medical Association |
| ISSN: | 0929-6646 |
| Popis: | In 2019, a large outbreak of a novel coronavirus disease (COVID-19) occurred in China. The purpose of this study is to quantitatively analyze the evolution of chest computed tomography (CT) imaging features in COVID-19. Nine patients with positive real-time reverse-transcriptase polymerase chain reaction results were included in this study. Totally 19 CT scans were analyzed. Lesion density, lesion volume, and lesion load were higher in the severe group than in the mild group. A significantly positive correlation was noted between major laboratory prognosticators with lesion volume and load. Lesion load at the first week of disease was significantly higher in severe group (p = 0.03). Our study revealed that several CT features were significantly different between severely and mildly infected forms of COVID-19 pneumonia. The CT lesion load value at the first week of infection may be applied as an outcome predictor of the disease. |
| Databáze: | OpenAIRE |
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