您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (5): 38-45.doi: 10.6040/j.issn.1671-7554.0.2020.541

• 临床医学 • 上一篇    

105例新型冠状病毒肺炎胸部CT影像学特征——山东省多中心回顾性分析

程召平1,段艳华1,姚金坤2,李岩3,顾慧4,袁宪顺4,刘斌5,毕万利1,宋照亮5,聂佩6,陈月芹7,孙占国7,刘善平8,王鲁光9,唐忠仁10,魏相磊11,董亮12,王春亭13,王锡明4   

  1. 1.山东大学附属山东省医学影像学研究所, 山东 济南 250021;2.临沂市人民医院影像科, 山东 临沂 276100;3.烟台市奇山医院影像科, 山东 烟台 264001;4.山东第一医科大学附属省立医院医学影像科, 山东 济南 250021;5.枣庄市立医院影像科, 山东 枣庄 277102;6.青岛大学附属医院影像科, 山东 青岛 266000;7.济宁医学院附属医院影像科, 山东 济宁 272029;8.新泰市人民医院影像科, 山东 新泰 271200;9.聊城市东昌府人民医院影像科, 山东 聊城 252000;10.海阳市人民医院影像科, 山东 海阳 265100;11.临沂市中心医院影像科, 山东 临沂 276400;12.山东大学齐鲁医院呼吸与危重症医学科, 山东 济南 250012;13.山东第一医科大学附属省立医院重症医学科, 山东 济南 250021
  • 发布日期:2022-09-27
  • 通讯作者: 王锡明. E-mail: wxming369@163.com
  • 基金资助:
    山东省重点研发项目(2018GSF118060);山东省泰山学者专项经费(2015年);山东第一医科大学学术提升计划(2019年)

Chest CT features of 105 patients with COVID-19: a multicenter retrospective study in Shandong Province

CHENG Zhaoping1, DUAN Yanhua1, YAO Jinkun2, LI Yan3, GU Hui4, YUAN Xianshun4, LIU Bin5, BI Wanli1, SONG Zhaoliang5, NIE Pei6, CHEN Yueqin7, SUN Zhanguo7, LIU Shanping8, WANG Luguang9, TANG Zhongren10, WEI Xianglei11, DONG Liang12, WANG Chunting13, WANG Ximing4   

  1. 1. Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan 250021, Shandong, China;
    2. Department of Radiology, Linyi Peoples Hospital, Linyi 276100, Shandong, China;
    3. Department of Radiology, Yantai Qishan Hospital, Yantai 264001, Shandong, China;
    4. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China;
    5. Department of Radiology, Zaozhuang Municipal Hospital, Zaozhuang 277102, Shandong, China;
    6. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China;
    7. Department of Radiology, The Affiliated Hospital of Jining Medical College, Jining 272029, Shandong, China;
    8. Department of Radiology, Xintai Peoples Hospital, Xintai 271200, Shandong, China;
    9. Department of Radiology, Dongchangfu Peoples Hospital, Liaocheng 252000, Shandong, China;
    10. Department of Radiology, Haiyang Peoples Hospital, Haiyang 265100, Shandong, China;
    11. Department of Radiology, Linyi Central Hospital, Linyi 276400, Shandong, China;
    12. Department of Pulmonary and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China;
    13. Department of Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
  • Published:2022-09-27

PDF (PC)

68

摘要： 目的 通过山东省多中心研究探讨新型冠状病毒肺炎(COVID-19)胸部CT影像特征及其临床应用价值。 方法 回顾性分析2020年1至3月按照《新型冠状病毒肺炎诊疗方案(试行第6版)》经核酸检测确诊并治愈出院(或死亡)共计105例COVID-19患者的流行病学、临床以及胸部CT影像资料。其中普通型患者(普通组)92例,重型或危重型患者(重症组)13例。对胸部CT影像学基本特征及动态演变特征进行统计分析;并将普通组与重症组进行对照分析。 结果 所有患者均有明确流行病学史。初期临床症状主要为不明原因发热(85%,90/105)、咳嗽、咳痰(72%,75/105)。实验室检查异常表现为白细胞计数减低(11%,12/105)及淋巴细胞计数减低(38%,40/105)。胸部CT基本征象分析结果显示,早期双肺呈多发磨玻璃病变(GGO)(98%),平均受累肺叶3个(2,5),主要分布于肺中、外带胸膜下;进展期大部分GGO实变(96%)、伴小叶间隔增厚(64%),部分呈铺路石征(42%),反应性胸膜增厚(23%),胸腔积液(3例),气胸(1例);恢复期显示纤维条纹征(49%)。另一重要特点为双肺外带表现明显的空气-细支气管征(41%)和微血管扩张征(40%)。普通组与重症组对比结果显示,重症组受累肺叶数目多于普通组(P<0.001);重症组受累半定量积分高于普通组(P<0.001)。重症组铺路石征(85% vs 34%,P=0.001)、小叶间隔增厚(92% vs 61%,P=0.029)及胸膜增厚(69% vs 16%,P<0.001)、纤维条纹征(85% vs 43%,P =0.007)相比更高。胸部CT动态影像分析结果显示,疾病初始阳性影像表现迟于临床症状,肺部CT 5 d(5, 6)开始呈阳性,CT病变进展时间5 d(5, 7),持续进展至11 d(10, 14)即CT病变峰值时间,CT病变转归时间12 d(11, 15)。病灶进展与转归时间的长短与疾病严重程度相关,核酸检测阴性后影像转归滞后。 结论 COVID-19胸部CT具有一定的特异性表现,主要为典型磨玻璃病变及其特征性动态演变,以及空气-细支气管征和微血管扩张征表现,结合半定量评估,可以为COVID-19早期诊断、临床分型及预后评估提供可靠依据。

关键词: 新型冠状病毒肺炎, 胸部, 体层摄影术,X线计算机

Abstract: Objective To explore the chest CT features of patients with coronavirus disease 19(COVID-19)and the clinical application value. Methods The epidemiological, clinical and chest CT data of 105 COVID-19 cases confirmed by nucleic acid test in accordance with Diagnosis and Treatment of Novel Coronavirus Pneumonia(trial version sixth)who were treated and cured(or died)during Jan. and Mar. 2020 were retrospectively analyzed. The patients included 92 common cases and 13 severe or critical cases, averaged 48±14(21-88)years old. The basic and dynamic characteristics of chest CT images were analyzed and compared between the two groups. Results All patients had a clear epidemiological history. The main initial clinical symptoms were fever(85%, 90/105), cough and expectoration(72%, 75/105). Both leucocyte count(11%, 12/105)and lymphocyte count(38%, 40/105)were decreased. Chest CT showed that in the early stage, multiple ground glass opacity(GGO, 98%)was observed in bilateral lungs involving 3 lobes(2, 5)in average, mainly distributed in the middle and outer zone of the lung under pleura; in the progressive stage, GGO consolidated(96%), with interlobular septal thickening(64%), paving stone sign(42%), reactive pleura thickening(23%), pleural effusion(3 cases), pneumothorax(1 case); in the recovery stage, fibrous streak sign was observed(49%). Another important feature was typical air bronchogram sign(ABS, 41%)and microvascular dilation sign(MVDS, 40%)in the extrapulmonary zone. Compared with the common cases, the severe cases had more lobes involved (P<0.001), higher semi-quantitative score (P<0.001), and higher incidences of paving stone sign(85% vs 34%, P=0.001), interlobular septal thickening(92% vs 61%, P=0.029), pleural thickening(69% vs 16%, P<0.001), and fibrous streak sign(85% vs 43%, P=0.007). Dynamic image analysis showed that in the initial stage, positive image manifested later than clinical symptoms: chest CT showed positive signs of the disease on day 5(5, 6), the disease progressed on day 5(5, 7)until day 11(10, 14), and showed possible outcome after another 12(11, 15)days. The span between progression and outcome was related to the severity of disease, and CT outcome was delayed after nucleic acid test was negative. Conclusion COVID-19 shows some specific manifestations on chest CT, characterized by GGO, dynamic evolution, ABS and MVDS. The above manifestations and semi-quantitative evaluation can provide reliable basis for the early diagnosis, clinical classification and prognosis evaluation of COVID-19.

Key words: Coronavirus disease 19, Chest, Tomography, X-ray computed

中图分类号: 

  • R563.1
[1] 中国疾病预防控制中心新型冠状病毒肺炎应急响应机制流行病学组.新型冠状病毒肺炎流行病学特征分析[J]. 中华流行病学杂志, 2020. doi: 10.3760/cma.j.issn.0254-6450.2020.02.003.
[2] 中华医学会放射学分会. 新型冠状病毒肺炎的放射学诊断:中华医学会放射学分会专家推荐意见(第1版)[J]. 中华放射学杂志, 2020,54(2020-02-08). doi: 10.3760/cma.j.issn.1005-1201.2020.0001.
[3] 国家卫生健康委办公厅. 新型冠状病毒肺炎诊疗方案(试行第6版)[EB/OL]. [2020-02-20]. http://www.nhc.gov.cn/yzygj/s7653p/202002/8334a8326dd94d329df351d7da8aefc2/files/b218cfeb1bc54639af227f922bf6b817.pdf.
[4] 程召平, 李岩, 段艳华, 等. 新型冠状病毒肺炎轻症患者胸部高分辨率CT动态影像演变的初步研究[J]. 中华放射学杂志, 2020,54(2020-03-31). doi:10.3760/cma.j.issn.1005-1201.2020.0021. CHENG Zhaoping, LI Yan, DUAN Yanhua, et al. A preliminary study on the dynamic image evolution of chest high resolution CT in patients with mild COVID-19 [J]. Chinese Journal of Radiology, 2020,54(2020-03-31). doi:10.3760/cma.j.issn.1005-1201.2020.0021.
[5] Guan WJ, Ni ZY, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China [J]. N Engl J Med, 2020,382(18):1708-1720.
[6] Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019(COVID-19)in China: a report of 1014 cases [J]. Radiology, 2020,200642. doi: 10.1148/radiol.2020200642.
[7] Scott S, Fernando UK, Suhny A, et al. Radiological society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA [J]. Radiology: Cardiothoracic Imaging, 2020, 2(2): e2000152. doi.org/10.1148/ryct.2020200152
[8] Salehi S, Abedi A, Balakrishnan S, et al. Coronavirus disease 2019(COVID-19): a systematic review of imaging findings in 919 patients [J]. AJR Am J Roentgenol, 2020,14:1-7. doi: 10.2214/AJR.20.23034.
[9] Wang Y, Dong C, Hu Y, et al. Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: a longitudinal study [J]. Radiology, 2020,19:200843. doi:10.1148/radiol.2020200843.
[10] Pan F, Ye T, Sun P, et al. Time course of lung changes on chest CT during recovery from 2019 novel coronavirus(COVID-19)pneumonia [J]. Radiology, 2020,13:200370. doi:10.1148/radiol.2020200370.
[11] Yang R, Li X, Liu H, et al. Chest CT severity score:an imaging tool for assessing severe COVID-19 [J]. Radiology: Cardiothoracic Imaging, 2020,2(2):e2000047. doi.org/10.1148/ryct.2020200047.
[12] Huang L, Han R, Ai T, et al. Serial quantitative chest CT assessment of COVID-19: deep-learning approach [J]. Radiology: Cardiothoracic Imaging, 2020. doi.org/10.1148/ryct.2020200075.
[13] 刘茜, 王荣帅, 屈国强, 等. 新型冠状病毒肺炎死亡尸体系统解剖大体观察报告[J]. 法医学杂志, 2020, 36(1): 21-23. LIU Qian, WANG Rongshuai, QU Guoqiang, et al. General anatomy report of novel coronavirus pneumonia death corpse [J]. Journal of Forensic Medicine, 2020, 36(1): 21-23.
[14] Luo W, Yu H, Gou J, et al. Clinical Pathology of Critical Patient with Novel Coronavirus Pneumonia(COVID-19)[J]. Preprints, 2020. https://www.preprints.org/manuscript/202002.0407/v4.
[1] 董亮,崔文超,周青,张龙云,周炜,张欣,赵超. 比较超声与增强CT经皮穿刺混杂密度胸部病变[J]. 山东大学学报 (医学版), 2022, 60(5): 98-103.
[2] 曹义海. 血管生成在疾病治疗中的应用与展望[J]. 山东大学学报 (医学版), 2021, 59(9): 9-14.
[3] 杨璇,李岩志,马伟,贾崇奇. 基于两样本孟德尔随机化的肺功能与新型冠状病毒肺炎病死风险的因果关系[J]. 山东大学学报 (医学版), 2021, 59(7): 104-111.
[4] 周溪,黄霂晗,任玉洁,邱洋. 新型冠状病毒感染与天然免疫及炎症反应[J]. 山东大学学报 (医学版), 2021, 59(5): 15-21.
[5] 于莹,张功,刘晶,颜世童,韩涛,黄海量. 基于网络药理学和分子对接方法探析黄芪预防新型冠状病毒肺炎的潜在作用机制[J]. 山东大学学报 (医学版), 2021, 59(4): 6-16.
[6] 任敏敏,王广梅,张丽,杨瑶瑶,封丹珺. 335名抗疫一线护理人员心理弹性对共情疲劳的影响[J]. 山东大学学报 (医学版), 2021, 59(2): 88-94.
[7] 袁勇贵,李磊,沈仲夏,陈刚,吴义高,岳莹莹. 新型冠状病毒肺炎疫情下精神障碍诊疗的防控策略[J]. 山东大学学报 (医学版), 2020, 58(4): 1-6.
[8] 常彩云,于秋燕,赵小冬,王芳,李伟,阮师漫,耿兴义. 济南市首例新型冠状病毒肺炎病例及其相关家庭聚集性疫情分析[J]. 山东大学学报 (医学版), 2020, 58(4): 7-11.
[9] 杨丽,李战,刘晓雪,焦海涛,周林,刘庆皆,刘铁诚,耿兴义. 济南市新型冠状病毒肺炎密切接触者隔离医学观察情况分析与评价[J]. 山东大学学报 (医学版), 2020, 58(4): 12-16.
[10] 乔宇,崔亮亮,李帅,王峰,阮师漫,景一鸣,刘翀. 智能问答机器人系统研发及应用研究——以济南市新型冠状病毒肺炎疫情处置应对为例[J]. 山东大学学报 (医学版), 2020, 58(4): 17-22.
[11] 李新蕊,耿兴义,赵小冬,刘岚铮,王蔚茹,崔亮亮,李战,常彩云,阮师漫. 济南市47例新型冠状病毒肺炎疫情流行综合分析[J]. 山东大学学报 (医学版), 2020, 58(4): 23-27.
[12] 赵怀龙,吕燕,赵红,赵宝添,韩莹,杨国樑,王春荣,关恒云,刘辉,刘岚铮. 济南市47例新型冠状病毒肺炎患者取样部位对核酸检测结果的影响[J]. 山东大学学报 (医学版), 2020, 58(4): 28-31.
[13] 李新蕊,耿兴义,王蔚茹,赵小冬,刘岚铮,张晓菲,吕翠霞,常彩云,李战,崔亮亮,阮师漫. 37例新型冠状病毒肺炎聚集性思考[J]. 山东大学学报 (医学版), 2020, 58(4): 32-40.
[14] 杨柳,李战,许华茹,常彩云,刘仲,李传彬,孙湛,景睿,刘铁诚,耿兴义,周敬文. 济南市10例儿童新型冠状病毒肺炎确诊病例流行病学和临床特征[J]. 山东大学学报 (医学版), 2020, 58(4): 36-39.
[15] 景睿,刘铁诚,王春荣,胡艳霞,武晶晶,张文茜,耿兴义,阮师漫. 济南市某商城新型冠状病毒肺炎聚集性疫情调查[J]. 山东大学学报 (医学版), 2020, 58(4): 40-43.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发