Assessing the progression of COVID-19 is crucial for the disease treatment and control. In the early stage, the lung stroma of COVID-19 patient was mostly invaded, which could be manifested by the thickening of interlobular septa, angioedema dilatation and GGO appearance. As the disease progresses, the alveolar structure was gradually affected by inflammation, while alveolar edema, exudation and bleeding might occur. On the CT image, lung consolidation and mixed GGO can be manifested. Parts of the lesions exhibited distal air bronchograms sign and thickening of the bronchial wall, while the remaining parts displayed other signs. Based on the clinical data published in recent literature, almost all patients with COVID-19 had characteristic CT features during the course of the disease, including angioedema dilatation sign, paving stone sign, etc[14].
The imaging manifestations of the influencing factors were successfully extracted by machine learning model during the course of COVID-19. Finally, the three objective variables, namely, fibrosis formation, distal air bronchogram sign and reversed halo sign, were incorporated into the model, which could serve as potential indicators to predict the disease outcomes. In the follow-up CT images, we found that 61.8% (42/68) of patients with exacerbation had distal air bronchogram sign. Among the alleviated cases, the CT features of fibrosis formation, reversed halo sign were observed to be 83.3% (60/72), 63.9% (46/72), respectively. The pathological mechanism of fibrosis formation is that the immune response of human body is intense or when the wall of small blood vessel is damaged by edema, the permeability of blood vessel wall is increased, the plasma and fibrin exudate, which can be interwoven into a net to limit the spread of pathogens and attenuate the lesion[15]. As for the occurrence of reversed halo, it represents a rare sign of a focal ground glass area surrounded by a complete ring of consolidation. Surgical pathology confirmed that the central GGO was actually alveolar septal inflammation and cellular debris, and the lesions surrounding alveoli tended to be mechanical inflammation, Some literature has suggested that the lesions turn out to be benign when their center part began to be absorbed [16–18]. The bright bronchogram seen in the area of diseased lung tissue is known as air bronchogram sign, which can be considered as strong evidence of inflammatory lesions. It has been reported that distal air bronchogram sign is helpful to distinguish the lung and pleura or mediastinal lesions,Alveolar lesions can be detected by air bronchogram sign, whereas thoracic reef and mediastinal lesions display no such signs[19–20]. So from this study, we observed the presence of more distal air bronchograms in the follow-up CT images of patients with exacerbation, suggesting that the lesion is further aggravated by expanding from the septal injury to the alveoli. fibrosis and reverse - halo signs are the prediction of benign outcome.
Unconditional logistic regression and Fisher's linear discriminant analysis are very important tasks in machine learning, which can be used to automatically derive the generalized description of a given dataset from known historical data, in order to predict future events [21–22].The results of the two models are relatively satisfactory, and consequently afford greater confidence in the assessment of COVID-19. In addition, the above CT features indicate that the lesion is in the critical period, and this trend change is helpful for clinicians to judge the therapeutic effect and predict the outcome of the disease. In the single factor analysis, the model variable, such as crazy paving pattern, is associated with the evolution of COVID-19. However, in the multiple factor analysis, it can be influenced by other factors with "false association" claims, and hence the "false association" should been adjusted in the analysis.
This study has several limitations. First, there was no long-term clinical follow-up and the CT examination data of discharged patients was lacking. Hence, the severity of pulmonary fibrosis at the time of its formation and later changes needs to be further observed. Second, severe cases were not included. The prognosis of severe patients can be affected by many factors.