基于机器学习的CT定量指标与新型冠状病毒肺炎临床分型及肺损伤严重程度的相关性研究

Correlation study between quantitative characteristics of CT lung opacification based on machine learning and clinical subtypes and severity of lung injury of COVID-19

目的探讨胸部CT定量指标在新型冠状病毒肺炎(COVID-19)临床分型及肺损伤严重程度评价中的价值。方法回顾性分析华中科技大学同济医学院附属同济医院2020年1月1日至2020年4月1日COVID-19确诊的438例患者的临床及CT影像资料。临床分型为普通型146例、重型247例、危重型45例。使用人工智能(AI)深度学习定量分析所有患者胸部CT指标,包括全肺体积、全肺感染体积、磨玻璃密度体积(GGO体积,CT值<-300 HU)和实性密度体积(SO体积,CT值≥-300 HU)以及SO体积/GGO体积。采用Kruskal-Wallis检验对各临床分型之间定量参数的差异性进行统计学分析,采用多元有序logistic回归分析定量参数与临床分型之间的相关性。结果 438例COVID-19确诊患者中,重型及危重型患者的年龄较大(P<0.05),且危重型患者以男性为主(P<0.05)。各临床分型患者的临床表现均主要以发热为主,其次为咳嗽、乏力、胸闷、呼吸困难、消化道症状等。3种临床分型肺部病变的CT表现均以GGO为主;全肺感染体积、GGO体积、SO体积以及各自在全肺体积的比例均从普通型、重型到危重型患者逐渐增大(P<0.01);SO体积/GGO体积随临床分型严重程度增加逐渐增大[普通型为0.12(0.03,0.34),重型为0.29(0.11, 0.59),危重型为0.61(0.39,0.97),P<0.05]。多元有序logistic回归分析显示全肺感染体积(OR=1.009)、SO体积/GGO体积(OR=1.866)、GGO体积(OR=1.008)和SO体积(OR=1.016)对临床分型的严重程度产生显著的正向影响关系(P<0.01)。结论基于AI胸部CT定量指标(SO体积、GGO体积、SO体积/GGO体积)与COVID-19肺炎临床严重程度密切相关。

Objective To investigate the value of chest CT quantitative index in clinical classification and lung injury severity evaluation of COVID-19.Methods The current study retrospectively analyzed the clinical and CT data of 438 patients with COVID-19 between January 2020 and March 2020 in Tongji Hospital,Tongji Medical College,Huazhong University of Science&Technology.The clinical types included common type(n=146),severe type(n=247)and critical type(n=45).The chest CT indexes of all patients were quantitatively analyzed by artificial intelligence(AI)deep learning,including whole lung volume,CT lung opacification,ground glass opacification volume(GGO volume;CT value<-300 HU),solid opacification volume(SO volume;CT value≥-300 HU)and the ratio of volume to the whole lung volume,the ratio of SO volume to GGO volume(SO volume/GGO volume).Kruskal-Wallis test was used to conduct statistical analysis of the differences in quantitative parameters among clinical types,and multiple ordered logistic regression was used to analyze the correlation between quantitative parameters and clinical types.Results Among the 438 patients diagnosed with COVID-19,severe and critical patients were older(P<0.05),and most of the critical patients were male(P<0.05).The main clinical manifestations of all clinical types were fever,followed by cough,fatigue,chest tightness,dyspnea,gastrointestinal symptoms and so on.GGO volume was the main CT manifestation of all the three clinical subtypes.The whole-lung opacification volume,GGO volume,SO volume and their proportions in whole-lung volume significantly increased from common,severe to critical types(all P<0.05).SO volume/GGO volume increased with the severity of clinical type[common type 0.12(0.03,0.34),severe type 0.29(0.11,0.59),critical type 0.61(0.39,0.97)].Multiple ordered logistic regression analysis showed that whole-lung opacification volume(OR=1.009),SO volume/GGO volume(OR=1.866),GGO volume(OR=1.008)and SO volume(OR=1.016)had a significant positive effect on the severity of clinical typing(P<0.01).Conclusion Quantitative indicators of chest CT based on deep learning algorithm(SO volume,GGO volume,SO volume/GGO volume)are closely related to the clinical severity of COVID-19.