Du Siyao, Gao Si, Huang Guoliang, Li Shu, Chong Wei, Jia Ziyi, Hou Gang, Wáng Yì Xiáng J, Zhang Lina
Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China.
Department of Emergency Medicine, the First Affiliated Hospital of China Medical University, Shenyang 110001, China.
Quant Imaging Med Surg. 2020 Jun;10(6):1307-1317. doi: 10.21037/qims-20-531.
Many studies have described lung lesion computed tomography (CT) features of coronavirus disease 2019 (COVID-19) patients at the early and progressive stages. In this study, we aim to evaluate lung lesion CT radiological features along with quantitative analysis for the COVID-19 patients ready for discharge.
From February 10 to March 10, 2020, 125 COVID-19 patients (age: 16-67 years, 63 males) ready for discharge, with two consecutive negative reverse transcription-polymerase chain reaction (RT-PCR) and no clinical symptoms for more than 3 days, were included. The pre-discharge CT was performed on all patients 1-3 days after the second negative RT-PCR test, and the follow-up CTs were performed on 44 patients 2-13 days later. The imaging features and quantitative analysis were evaluated on both the pre-discharge and the follow-up CTs, by both radiologists and an artificial intelligence (AI) software.
On the pre-discharge CT, the most common CT findings included ground-glass opacity (GGO) (99/125, 79.2%) with bilateral mixed distribution, and fibrosis (56/125, 44.8%) with bilateral subpleural distribution. Enlarged mediastinal lymph nodes were also commonly observed (45/125, 36.0%). AI enabled quantitative analysis showed the right lower lobe was mostly involved, and lesions most commonly had CT value of -570 to -470 HU consistent with GGO. Follow-up CT showed GGO decrease in size and density (40/40, 100%) and fibrosis reduction (17/26, 65.4%). Compared with the pre-discharge CT results, quantitative analysis shows the lung lesion volume regressed significantly at follow-up.
For COVID-19 patients ready for discharge, GGO and fibrosis are the main CT features and they further regress at follow-up.
许多研究描述了2019冠状病毒病（COVID-19）患者在早期和进展期的肺部病变计算机断层扫描（CT）特征。在本研究中，我们旨在评估准备出院的COVID-19患者的肺部病变CT影像学特征，并进行定量分析。
纳入2020年2月10日至3月10日准备出院的125例COVID-19患者（年龄16 - 67岁，男性63例），这些患者连续两次逆转录聚合酶链反应（RT-PCR）检测结果为阴性，且无临床症状超过3天。所有患者在第二次RT-PCR检测阴性后1 - 3天进行出院前CT检查，44例患者在2 - 13天后进行随访CT检查。由放射科医生和人工智能（AI）软件对出院前和随访CT的影像特征及定量分析进行评估。
出院前CT检查中，最常见的CT表现包括双侧混合分布的磨玻璃影（GGO）（99/125，79.2%）以及双侧胸膜下分布的纤维化（56/125，44.8%）。纵隔淋巴结肿大也较为常见（45/125，36.0%）。AI辅助定量分析显示右下叶受累最多，病变的CT值最常见于-570至-470 HU，符合磨玻璃影表现。随访CT显示GGO大小和密度减小（40/40，100%），纤维化减轻（17/26，65.4%）。与出院前CT结果相比，定量分析显示随访时肺部病变体积显著缩小。
对于准备出院的COVID-19患者，磨玻璃影和纤维化是主要的CT特征，且在随访中进一步消退。
