CT影像组学标签预测肺腺癌表皮生长因子受体基因敏感突变

CT radiomics signatures for prediction of epidermal growth factor receptor sensitive mutation in lung adenocarcinoma

目的建立基于CT平扫图像的影像组学标签,探讨其用于预测肺腺癌表皮生长因子受体(EGFR)基因敏感突变的可行性。方法根据EGFR基因突变情况将80例肺腺癌患者分为EGFR敏感组(n=37)及EGFR不敏感组(n=43)。收集2组肺部病灶CT平扫主观影像征象,并提取影像组学特征;采用LASSO法进行特征挑选,以多因素Logistic回归分别建立主观影像征象模型、影像组学标签及融合模型,采用ROC曲线评估各模型预测EGFR基因敏感突变的效能。结果 EGFR敏感组与不敏感组间CT主观影像征象差异均无统计学意义(P均>0.05)。通过特征选择,确定CT影像组学标签由4个影像组学特征构成。主观影像征象模型(AUC=0.66)、影像组学标签(AUC=0.77)及融合模型(AUC=0.83)预测EGFR基因敏感突变的效能差异均有统计学意义(P均<0.05),以融合模型预测的效能最优。结论基于CT平扫图像建立的影像组学标签能够预测肺腺癌EGFR基因敏感突变。

Objective To establish radiomics signatures based on non-enhanced CT image features,and to evaluate their feasibility for prediction epidermal growth factor receptor(EGFR)sensitive mutation of lung adenocarcinoma.Methods Eighty lung adenocarcinoma patients were divided into EGFR sensitive group(n=37)and EGFR insensitive(n=43)group according to EGFR mutation status.Radiomics features and subjective image features were collected from non-enhanced CT images.LASSO regression model was used to select radiomics features.Subjective image features model,radiomics model and combined diagnostic model were developed with multiple factors Logistic models,respectively.The predictive performance of EGFR sensitive mutation of each model was evaluated with ROC curve.Results There was no significant difference of subjective CT image features between EGFR sensitive and insensitive group(all P>0.05).Through feature selection,4 radiomics features were enrolled.Subjective CT image features model(AUC=0.66),radiomics model(AUC=0.77)and combined diagnostic model(AUC=0.83)had statistically significant differences in the performance of predicting EGFR sensitive mutation(all P<0.05).The combined diagnostic model had the best predictive efficiency.Conclusion Radiomics signatures based on non-enhanced CT images can be used to predict EGFR sensitive mutation in lung adenocarcinoma.