摘要
目的分析基于2019新型冠状病毒肺炎(COVID-19)临床分型的胸部CT表现,提高对COVID-19的影像认识。方法选取48例临床确诊COVID-19患者的胸部CT资料,基于临床分型分为普通型组、重型组、危重型组,比较3组病例胸部CT病灶分布的差异性。结果48例COVID-19患者均有流行病学史,三组患者性别差异无统计学意义,随着患者年龄的增加,病变程度影像学上呈加重趋势。左肺上叶、右肺上叶、右肺中叶病灶分布在重型及危重型占比高于普通型(P<0.05),左肺下叶、右肺下叶、仅左肺、仅右肺三型分布差异无统计学意义。结论普通型组、重型组、危重型组胸部CT病灶以肺叶外带磨玻璃阴影为主,其分布、形态特征有助于COVID-19诊断及病情动态评估。
Objective To analyze the chest CT manifestations of corona virus disease 2019(COVID-19)according to clinical classification,so as to improve the image recognition of COVID-19.Methods We retrospectively analyzed the chest CT data of 48 COVID-19 patients.Based on clinical classification,the patients were divided into the general type group,heavy group and the critical group,and the differences in the distribution of chest CT lesions in three groups were compared.Results All the 48 COVID-19 patients had a history of epidemiology,and there was no statistical difference in gender among the three groups.As the age of the patients increased,the disease degree showed an aggravating trend on imaging.The proportion lesions of the upper lobe of the left lung,the upper lobe of the right lung and the middle lobe of the right lung in heavy and critical type were higher than those in the general type(P<0.05).There was no statistical difference in the distribution of three types in the lower lobe of left lung,the lower lobe of right lung,left lung and the right lung.Conclusion In the general group,the heavy group and the critical group,the chest CT lesions were mainly in the pulmonary lobe with external ground glass opacity,and their distribution and morphological characteristics were helpful for the diagnosis and dynamic assessment of COVID-19.
作者
赵建华
刘瑞
贺燕林
张晓琴
柴军
ZHAO Jianhua;LIU Rui;HE Yanlin;ZHANG Xiaoqin;CHAI Jun(Department of Imaging Medical,Inner Mongolia Autonomous Region People’s Hospital,Huhhot 010017,P.R.China)
出处
《医学影像学杂志》
2020年第10期1817-1820,共4页
Journal of Medical Imaging
基金
内蒙古自治区应用技术研究与开发资金项目。内蒙古自治区自然科学基金(编号:2017MS08514)
内蒙古自治区人民医院院内基金项目(编号:2019YN03)。
关键词
新型冠状病毒肺炎
临床分型
病灶分布
体层摄影术
X线计算机
Corona virus disease 2019
Clinical classification
Distribution of lesions
Tomography,X-ray computed