Analysis of 4 imaging features in patients with COVID-19

Jin, Jun; Gao, De-hong; Mo, Xin; Tan, Si-ping; Kou, Zhen-xia; Chen, Yi-bo; Cao, Jin-bo; Chen, Wen-jing; Zhang, Ya-ming; Li, Bing-qing; Huang, Kuan-long; Xu, Bing-ren; Tang, Xiao-li; Wang, Yu-li

BACKGROUND: The aim of this was to analyze 4 chest CT imaging features of patients with coronavirus disease 2019 (COVID-19) in Shenzhen, China so as to improve the diagnosis of COVID-19. METHODS: Chest CT of 34 patients with COVID-19 confirmed by the nucleic acid test (NAT) were retrospectively analyzed. Analyses were performed to investigate the pathological basis of four imaging features(“feather sign”,“dandelion sign”,“pomegranate sign”, and “rime sign”) and to summarize the follow-up results. RESULTS: There were 22 patients (65.2%) with typical “feather sign”and 18 (52.9%) with “dandelion sign”, while few patients had “pomegranate sign” and “rime sign”. The “feather sign” and “dandelion sign” were composed of stripe or round ground-glass opacity (GGO), thickened blood vessels, and small-thickened interlobular septa. The “pomegranate sign” was characterized as follows: the increased range of GGO, the significant thickening of the interlobular septum, complicated with a small amount of punctate alveolar hemorrhage. The “rime sign” was characterized by numerous alveolar edemas. Microscopically, the wall thickening, small vascular proliferation, luminal stenosis, and occlusion, accompanied by interstitial infiltration of inflammatory cells, as well as numerous pulmonary interstitial fibrosis and partial hyaline degeneration were observed. Repeated chest CT revealed the mediastinal lymphadenectasis in one patient. Re-examination of the NAT showed another positive anal swab in two patients. CONCLUSION: “Feather sign” and “dandelion sign” were typical chest CT features in patients withCOVID-19; “pomegranate sign” was an atypical feature, and “rime sign” was a severe feature. In clinical work, accurate identification of various chest CT signs can help to improve the diagnostic accuracy of COVID-19 and reduce the misdiagnosis or missed diagnosis rate.