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.