|
|
|
| Quantification of CT images in 83 cases of COVID-19 |
| LIN Chunmiao*, QIN Tong, LU Yuyang, YU Lexi
|
| *Departments of Radiology, Zhejiang Provincial People's Hospital（Affiliated People's Hospital,Hangzhou Medical College）, Hangzhou, Zhejiang 310014, China |
|
|
|
|
Abstract Objective To quantitatively analyze the chest computerized tomography ( CT ) images of coronavirus disease 2019 ( COVID-19 ) cases by automatic artificial intelligence ( AI ) system, so as to provide the basis for the prediction of severe cases and early clinical intervention. Methods Eighty-three confirmed cases of COVID-19 from January 23 to February 14, 2020 in Wuchang Hospital of Wuhan were selected and the clinical data were collected. According to the diagnosis and treatment Plan of COVID-19 (seventh trial), the patients were divided into an ordinary group and a severe group. The parameters of chest CT images were quantified by the automatic AI system, and the CT imaging features of two groups were compared. Results There were 46 cases in the ordinary group and 37 cases in the severe group, with the age of ( 62.68 ±13.69 ) years and ( 50.52 ±12.45 ) years, respectively. The percentages of total pulmonary lesions, the lesion volume of bilateral lungs, the lesion volume of right lower lung, the left lung volume and the right lung volume from -300 to -200 Hu [median (inter-quartile range)] were 19.80% ( 21.69% ), 622.87 ( 1 145.73 ) cm3, 205.73 ( 246.95 ) cm3, 26.50 (21.20) cm3 and 38.02 (48.78) cm3 in the severe group, which were significantly different from 9.78% ( 13.24% ), 333.55 ( 401.77 ) cm3, 126.02 (164.21) cm3, 21.43 (13.11) cm3 and 26.92 ( 18.04 ) cm3 in the ordinary group ( P<0.05 ). The volume of pulmonary lesions reached the peak from 10 to 16 days after infection. Conclusion The lung lesions in severe cases of COVID-19 are large, especially in the right lower lung, and need to be closely monitored from 10 to 16 days after infection for early warning of severe cases.
|
|
Received: 14 December 2020
Revised: 15 March 2021
Published: 04 June 2021
|
|
|
|
|
|
[1] HUANG C,WANG Y,LI X,et al.Clinical features of patients infected with 2019 novel coronavirus in Wuhan,China[J]. Lancet,2020,395(10223):497-506.
[2] XU Z,SHI L,WANG Y,et al.Pathological findings of COVID-19 associated with acute respiratory distress syndrome[J] .Lancet Respir Med,2020,8(4):420-422.
[3] LI K, WU J, WU F,et al.The clinical and chest CT features associated with severe and critical COVID-19 pneumonia[J] .Invest Radiol,2020,55(6):327-331.
[4] 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,2020,295(3):715-721.
[5] YOON S H,LEE K H,KIM J Y,et al.Chest radiographic and CT findings of the 2019 novel coronavirus disease(COVID-19):analysis of nine patients treated in Korea[J] .Korean J Radiol,2020,21(4):494-500.
[6] 汪锴,康嗣如,田荣华,等. 新型冠状病毒肺炎胸部CT影像学特征分析[J] .中国临床医学,2020,27(1):27-31.
[7] 吕志彬,关春爽,闫铄,等. 人工智能在CT预测新型冠状病毒肺炎转归中的价值[J] .首都医科大学学报,2020,41(3):340-344.
[8] 中华人民共和国国家卫生健康委员会办公厅,国家中医药管理局办公室. 新型冠状病毒肺炎诊疗方案(试行第七版)[EB/OL].(2020-03-04)[2021-03-15] .http://www.nhc.gov.cn/cms-search/downFiles/f9ea38ce2c2d4352bf61ab0feada439f.pdf.
[9] BERNHEIM A,MEI X,HUANG M,et al. Chest CT findings in coronavirus disease-19(COVID-19):relationship to duration of infection [J/OL] .Radiology,2020,295(3)(2020-02-20)[2021-03-15] .https://doi.org/10.1148/radiol.2020200463.
[10] WANG D,HU B,HU C,et al.Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan,China[J] .JAMA,2020,323(11):1061-1069.
[11] CUI S,SHU Z,SHAO Y,et al. Age-associated changes in aging lungs:a study with auto-segmentation and radiomics based on CT images [J/OL] .Res Sq(2020-02-20)[2021-03-15].https://www.researchsquare.com/article/rs-13785/v1. DOI:10.21203/rs. 2.23216/v1.
[12] 黄璐,韩瑞,于朋鑫,等. 新型冠状病毒肺炎不同临床分型间CT和临床表现的相关性研究 [J/OL] .中华放射学杂志,2020[2021-03-15]. http://rs.yiigle.com/yufabiao/1180145.htm. DOI:10.3760/cma.j.issn.1005-1201.2020.0003.
[13] RUUSKANEN O,LAHTI E,JENNINGS L C,et al.Viral pneumoniat[J]. Lancet,2011,377(9773):1264-1275.
[14] LUO W,YU H,GOU J,et al.Clinical pathology of critical patient with novel coronavirus pneumonia(COVID-19):pulmonary fibrosis and vascular changes including microthrombosis formation firstly found [J/OL] .Clin Infect Dis(2020-03-09)[2021-03-15].https://www.researchgate.net/publication/339939319_Clinical_Pathology_of_Critical_Patient_with_Novel_Coronavirus_Pneumonia_COVID-19_Pulmonary_Fibrosis_and_Vascular_Changes_including_Microthrombosis_Formation_firstly_Found. DOI:10.1097/TP.0000000000003412.
[15] SHEN C,YU N,CAI S,et al.Quantitative computed tomography analysis for stratifying the severity of coronavirus disease 2019[J] .J Pharm Anal,2020,10(2):123-129.
[16] DENG L S,YUAN J,DING L,et al.Comparison of patients hospitalized with COVID-19,H7N9 and H1N1[J] .Infect Dis Poverty,2020,9(1):163-171.
[17] SCHOLTEN E T,JACOBS C,VAN GINNEKEN B,et al.Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation[J] .Eur Radiol,2015,25(2):488-496.
[18] TIAN S,HU W,NIU L,et al.Pulmonary pathology of early phase 2019 novel coronavirus(COVID-19)pneumonia in two patients with lung cancer[J] .J Thorac Oncol,2020,15(5):700-704.
[19] NISHIYAMA A,KAWATA N,YOKOTA H,et al. A predictive factor for patients with acute respiratory distress syndrome:CT lung volumetry of the well-aerated region as an automated method [J/OL] .Eur J Radiol,2020,122(2019-11-14)[2021-03-15]. https://www.ejradiology.com/article/S0720-048X(19)30398-5/fulltext. DOI:10.1016/j.ejrad.2019.108748.
[20] COLOMBI D,BODINI F C,PETRINI M,et al.Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia[J] .Radiology,2020,296(2):E86-E96. |
|
|
|
