Background: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases.
Objective: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results.
Methods: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups.
Results: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups.
Conclusions: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.
Keywords: COVID-19; early detection; quantitative CT parameters; temporal changes.