Correlation Between Quantitative Assessment of Chest Computed Tomography (CT) Imaging and Prognosis of COVID-19 Patients

Autor: Jian-bing Zhu, Jing Liu, G Chen, Min Li, Yi Zhu, Rongrong Liu, Wei Tang
Rok vydání: 2020
Předmět:
Zdroj: Medical Science Monitor : International Medical Journal of Experimental and Clinical Research
ISSN: 1643-3750
DOI: 10.12659/msm.925183
Popis: BACKGROUND The aim of our work was to evaluate the correlation between the quantitative parameters of the peak lesion to 25% improvement time (PIT25) and the prognosis of new coronavirus pneumonia (COVID-19) patients by analyzing the changes of chest CT imaging. MATERIAL AND METHODS This retrospective analysis included 68 patients with COVID-19 in the Fifth People's Hospital of Suzhou City. Three radiologists performed a blind evaluation of 4 chest CT images that included the initial scans, the lesion peak, the lesion decreased to 25% of the peak, and the final scan. The score of chest CT lesion, the imaging characteristics of the lesion, the time of the appearance of symptoms related to the CT examination, quantitative assessment of PIT25, and the absorption of the lesion in last CT image were analyzed. Patients were divided into an obvious absorption group and a non-obvious absorption group according to the reduction of the lesion area by greater than 50% or less than 50%. RESULTS In the peak time, the most common images of CT were ground-glass opacities (94.1%), consolidation (85.3%) and reticulation (88.2%), multifocal (97.1%), center and subpleural (54.4%), subpleural distribution (45.6%), and pleural thickening (79.4%). The PIT25 with the prognosis (r=0.53, p=0.00) was significantly relevant. PIT25 was 4.3±0.7 days for the obvious absorption group and 6.8±1.4 days for the non-obvious absorption group. CONCLUSIONS The features of CT image are specific at the peak. The quantitative parameter PIT25 could be used to predict the prognosis of the patients with COVID-19, as COVID-19 patients with a shorter PIT25 have a better prognosis and vice versa.
Databáze: OpenAIRE