CT影像组学联合临床特征在预测肺腺癌EGFR突变中的价值

目的:探究CT影像组学联合临床特征对肺腺癌EGFR突变状态的预测效能.方法:对125例肺腺癌病人进行回顾性研究,分成训练组(n=74)与验证组(n=51),基于CT成像提取影像组学特征;采用支持向量机(SVM)分类器,分别构建临床模型、影像组学模型以及联合模型;受试者工作特征曲线(ROC)及曲线下面积(AUC)用于评价...

Value of the CT radiomics combined with clinical features in the prediction of EGFR mutation in lung adenocarcinoma

ObjectiveTo explore the predictive efficacy of CT radiomics combined with clinical features in predicting EGFR mutation in lung adenocarcinoma.MethodsThe clinical data of 125 patients with lung adenocarcinoma were retrospectively analyzed, the patients were divided into the training group(n=74) and verification group(n=51).The radiomics features were extracted based on CT radiomics.The support vector machine(SVM) classifier was used to construct the clinical model, radiomics model and joint model, respectively.The receiver operating characteristic curve(ROC) and area under the curve(AUC) were used to evaluate the predictive efficacy of model.ResultsThe AUC of clinical model, radiomics model and joint model in training group were 0.749(0.653-0.843), 0.818(0.711-0.898) and 0.860(0.760-0.930), respectively.The AUC of clinical model, radiomics model and joint model in verification group were 0.753(0.612-0.863), 0.797(0.661-0.896) and 0.855(0.728-0.938), respectively.ConclusionsFor the prediction of EGFR mutation status in lung adenocarcinoma, the CT radiomics features are superior to clinical factors and CT signs.The radiomics combined with clinical factors and CT signs can further improve the prediction efficiency.