CT影像组学特征预测晚期肺腺癌EGFR基因突变的价值研究

目的:探究影像组学特征对于晚期肺腺癌EGFR基因突变状态的预测价值.方法:回顾性分析2013年04月至2019年09月期间青岛大学附属医院收治的339例晚期肺腺癌患者,其中237例作为训练组,102例作为验证组,从患者胸部CT动脉期及静脉期图像中各提取396个影像组学特征.依次应用mRMR和LASSO在训练组进行特征筛...

CT based radiomic features for prediction of epidermal growth factor receptor mutation in advanced lung adenocarcinoma

Objective:To predict epidermal growth factor receptor(EGFR)mutation status of advanced lung adenocarcinoma using quantitative radiomic features.Methods:The study included 339 patients with advanced lung adenocarcinoma admitted in Affiliated Hospital of Qingdao University from April 2013 to September 2019.There were 237 patients in the training group and 102 patients in the validation group.396 radiomic features were extracted from the arterial phase and venous phase image respectively.The minimum redundancy maximum relevance(mRMR)method as well as the least absolute shrinkage and selection operator(LASSO)method were used to select the features.A multivariable logistic regression model was built using the radiomic features which were highly correlated with EGFR gene mutation.The receiver operating curve(ROC)was used for the evaluation of model performance in training group and validation group.Results:LASSO was conducted to choose the most predictive subset which include 15 features to construct the arterial phase radiomic model.Area under the curve(AUC)for arterial phase radiomic model was 0.75 in the training group and 0.70 in the validation group.LASSO was conducted to choose 5features to construct the venous phase radiomic model.AUC for venousphase radiomic model was 0.69 in the training group and 0.68 in the validation group.396 features extracted from the arterial phase image with the features obtained from the venous phase image were analysed.Then LASSO was conducted to choose 10 features to construct the combined radiomic model.AUC for combined radiomic model was 0.76 in the training group and 0.74 in the validation group.Conclusion:Radiomic features were predictive for EGFR mutation status in advanced lung adenocarcinoma.The arterial phase radiomic model showed better performance than venous phase radiomic model.The combined radiomic model showed best performance in predicting the EGFR mutation status.