基于参数响应图参数的随机森林模型预测胸部疾病患者的肺功能

Prediction of pulmonary function test parameters by parameter response mapping parameters based on random forest regression model

目的探讨基于随机森林的参数响应图(PRM)定量参数对肺功能的预测价值。方法回顾分析2018年8月至2019年12月在上海长征医院接受胸部三大疾病筛查的受试者615例。根据肺功能指标[第1秒用力呼气容积与用力肺活量的比值(FEV_(1)/FVC)及第1秒用力呼气容积占预计值的百分比(FEV_(1)%)]分为正常组、高危组及慢性阻塞性肺疾病(COPD)组。小气道CT定量参数主要为PRM参数,包括全肺、左肺、右肺及5个肺叶的肺体积、功能小气道疾病体积(PRMV^(fSAD))、肺气肿体积(PRMV^(Emph))、正常部分肺体积(PRMV^(Normal))、未分类部分肺体积(PRMV^(Uncategorized))及后四者体积占全肺的百分比(%)。采用单因素方差分析或Kruskal-Wallis H检验3组间基本临床特征(年龄、性别、身高、体质量)、肺功能参数和小气道CT定量参数的差异;采用Spearman检验评价PRM参数与肺功能参数的相关性。最后构建基于PRM联合4个基本临床特征的随机森林回归模型,预测肺功能。结果3组间全肺PRM参数差异均有统计学意义(P<0.001)。CT定量参数PRMV^(Emph)、PRMV^(Emph)%、PRMV^(Normal)%与FEV_(1)/FVC呈中度相关(P<0.001),全肺体积、PRMV^(Normal)、PRMV^(Uncategorized)及PRMV^(Uncategorized)%与FVC呈强或中度正相关(P<0.001),余PRM参数与肺功能参数呈弱或极弱相关。基于以上参数建立预测FEV_(1)/FVC的随机森林模型和预测FEV_(1)%的随机森林模型。预测FEV_(1)/FVC的随机森林模型预测FEV_(1)/FVC与实际值在训练集中R2=0.864,验证集中R2=0.749;预测FEV_(1)%的随机森林模型预测FEV_(1)%与实际值在训练集中R2=0.888,验证集中R2=0.792。验证集中,随机森林FEV_(1)%预测模型对正常组及高危组分类的灵敏度为0.85(34/40),特异度为0.90(65/72),准确度为0.88(99/112);随机森林FEV_(1)/FVC预测模型对非COPD患者及COPD患者分类的灵敏度0.89(8/9),特异度1.00(112/112),准确度0.99(120/121);两个模型联合对COPD组内[慢性阻塞性肺疾病全球倡议(GOLD)Ⅰ、GOLDⅡ、GOLDⅢ+Ⅳ]分类的准确度为0.44。结论小气道CT定量参数PRM可区分正常人群、高危及COPD人群;基于PRM参数结合临床特征的联合回归预测模型,对正常组及高危组、非COPD及COPD组的预测效果良好,进而实现一次CT扫描能够完成对功能小气道和肺功能的一次性评估。

Objective To explore the predictive value of random forest regression model for pulmonary function test.Methods From August 2018 to December 2019,615 subjects who underwent screening for three major chest diseases in Shanghai Changzheng Hospital were analyzed retrospectively.According to the ratio of forced expiratory volume in the first second to forced vital capacity(FEV_(1)/FVC)and the percentage of forced expiratory volume in the first second to the predicted value(FEV_(1)%),the subjects were divided into normal group,high risk group and chronic obstructive pulmonary disease(COPD)group.The CT quantitative parameter of small airway was parameter response mapping(PRM)parameters,including lung volume,the volume of functional small airways disease(PRMV^(fSAD)),the volume of emphysema(PRMV^(Emph)),the volume of normal lung tissue(PRMV^(Normal)),the volume of uncategorized lung tissue(PRMV^(Uncategorized))and the percentage of the latter four volumes to the whole lung(%).ANOVA or Kruskal Wallis H was used to test the differences of basic clinical characteristics(age,sex,height,body mass),pulmonary function parameters and small airway CT quantitative parameters among the three groups;Spearman test was used to evaluate the correlation between PRM parameters and pulmonary function parameters.Finally,a random forest regression model based on PRM combined with four basic clinical characteristics was constructed to predict lung function.Results There were significant differences in the parameters of whole lung PRM among the three groups(P<0.001).Quantitative CT parameters PRMV^(Emph),PRMV^(Emph)%,and PRMV^(Normal)%showed a moderate correlation with FEV_(1)/FVC(P<0.001).Whole lung volume,PRMV^(Normal),PRMV^(Uncategorized) and PRMV^(Uncategorized)%were strongly or moderately positively correlated with FVC(P<0.001),other PRM parameters were weakly or very weakly correlated with pulmonary function parameters.Based on the above parameters,a random forest model for predicting FEV_(1)/FVC and a random forest model for predicting FEV_(1)%were established.The random forest model for predicting FEV_(1)/FVC predicted FEV_(1)/FVC and actual value was R2=0.864 in the training set and R2=0.749 in the validation set.The random forest model for predicting FEV_(1)%predicted FEV_(1)%and the actual value in the training set was R2=0.888,and the validation set was R2=0.792.The sensitivity,specificity and accuracy of predicting FEV_(1)%random forest model for the classification of normal group from high-risk group were 0.85(34/40),0.90(65/72)and 0.88(99/112),respectively;and the sensitivity,specificity and accuracy of predicting FEV_(1)/FVC random forest model for differentiating non COPD group from COPD group were 0.89(8/9),1.00(112/112)and 0.99(120/121),respectively.While the accuracy of two models combination for subclassification of COPD[global initiative for chronic obstructive lung disease(GOLD)Ⅰ,GOLDⅡand GOLDⅢ+Ⅳ]was only 0.44.Conclusions Small airway CT quantitative parameter PRM can distinguish the normal population,high-risk and COPD population.The comprehensive regression prediction model combined with clinical characteristics based on PRM parameter show good performance differentiating normal group from high risk group,and differentiating non-COPD group from COPD group.Therefore,one-stop CT scan can evaluate the functional small airway and PFT simultaneously.