深度学习定量测量对新型冠状病毒感染预后的分析

Analysis of Prognosis of Coronavirus Disease 2019 Using Quantitative Measurement of Deep Learning

目的:分析基于深度学习定量测量新型冠状病毒感染(COVID-19)患者胸部CT炎性病灶的特征,预警重症的发生,提高对COVID-19预后的认识。方法:回顾性分析477例首次确诊为中型COVID-19患者的胸部CT,男276例,女201例,根据是否转为重症(重型/危重型)分为A组(未转为重症)、B组(转为重症),比较两组病例病灶分布、累及肺叶侧别、数目等CT基本征象及基于深度学习的病灶体积、体积占比和密度等的差异。结果:477例COVID-19患者均有流行病学史,年龄、性别在两组间的差异无统计学意义。B组全肺及各肺叶病灶体积及体积占比高于A组。A组病灶以右肺下叶为主,占比高于其它肺叶,达3.32%;其次为左肺下叶,占比为2.08%;左肺上叶病灶体积占比较其他肺叶低,仅为0.25%。A组部分患者右肺上叶、右肺中叶及左肺上叶无病灶。B组病灶呈双肺分布,各肺叶均有;其中以右肺下叶、左肺下叶分布为主,占比最高,分别为57.86%和54.76%;右肺中叶体积占比较其他肺叶低,为34.73%。各组病灶均以磨玻璃密度影为主。A组以密度为-570~-470 HU病灶为主,占比达13.89%;其次为-470~-370 HU,占比为11.07%;密度为30~70 HU实性病灶及密度为-70~30 HU较少,占比仅3.22%和4.75%。B组大部分呈磨玻璃密度影,以病灶密度为-570~-470 HU、-470~-370 HU与-370~-270 HU为主,占比分别为13.18%、12.58%、12.52%;密度为-570~-470 HU的病灶占比与A组差异无统计学意义,余各密度病灶体积及体积占比高于A组,呈病灶密度越高,B组占比较A组越高的趋势。结论:感染病灶体积较大、实性成分较多及多种CT征象并存常提示肺部炎症较重,容易进展为重症,基于深度学习的胸部CT定量测量有助于评估COVID-19预后及预警重症。

Objective:To analyze the differences of chest computed tomography(CT)inflammatory lesions in patients with coronavirus disease 2019(COVID-19),which were quantitatively measured based on deep learning,to warn the occurrence of severe cases and improve the understanding of the prognosis of COVID-19.Methods:The chest CT scans of 477 local patients with COVID-19 diagnosed for the first time at Inner Mongolia Autonomous Region People's Hospital were retrospectively analyzed.A total of 276 men and 201 women were divided into group A(not serious)and group B(serious)based on whether their diseases turned serious(severe/critical).Comparison was made between the two groups on the basic CT signs,such as lesion distribution,involved lobe side,number,differences in lesion volume,volume proportion,and density based on deep learning.Results:All 477 patients with COVID-19 had epidemiological history,and no statistical difference was noted in age and gender between the two groups.The volume and proportion of the lesions in the whole lung and each lobe of the lung in group B were higher than those in group A.The lesions in group A were mainly in the lower lobe of the right lung,accounting for 3.32%more than that in other lobes.The lower lobe of the left lung was the next,accounting for 2.08%.The volume of lesions in the upper lobe of the left lung was lower than that in other lobes,accounting for only 0.25%.No lesions were noted in the upper lobe of the right lung,middle lobe of the right lung,and upper lobe of the left lung in part of group A.In group B,the lesions were distributed in both lungs and in each lung lobe.The lower lobes of the right lung and left lung were predominant,accounting for 57.86%and 54.76%,respectively.The volume of the middle lobe of the right lung was 34.73%compared with the other lobes.The main lesions in each group were ground-glass density shadows,and the main lesions in group A were-570~-470 HU density,accounting for 13.89%,followed by-470~-370 HU,accounting for 11.07%.Only 3.22%and 4.75%of solid lesions with densities of 30~70 HU and-70~30 HU were found.Most of the lesions in group B were ground-glass density shadows,and the focal densities were mainly-570~-470 HU,-470~-370 HU,and-370~-270 HU,accounting for 13.18%,12.58%,and 12.52%,respectively.No statistical difference was noted between the proportion of lesions with a density of-570~-470 HU and that of group A;however,the volume and proportion of other lesions with different densities were higher than those of group A,showing a trend that the higher the density of the lesions,the higher the proportion of group B was compared with group A.Conclusion:Larger infection volume,more lesion solid components,and multiple CT signs often indicate more severe lung inflammation,which easily progresses to severe disease.Quantitative measurement of chest CT based on deep learning is helpful for the prognosis assessment of COVID-19 and the early warning of severe outcome.