Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study

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 [Formula: see text] 7 days); Stage-2 (8 [Formula: see text] 14 days); Stage-3 (15 [Formula: see text] 21days); Stage-4 ([Formula: see text] 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, (([Formula: see text] , [Formula: see text] 750), [[Formula: see text] 750, [Formula: see text] 300), [[Formula: see text] 300, 50) and [50, [Formula: see text])), 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 [[Formula: see text] 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. H higher proportion of HU [[Formula: see text] 750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.