The value of predicting EGFR mutation status in invasive lung adenocarcinoma based on enhanced CT imaging omics

WANG Jie; WANG Zhenyu; MA Liangyong; ZHANG Jinhua; LIN Yufen; LIN Jizheng; ZHANG Juntao

Abstract：Objective To develop and validate a radiomics nomogram for the prediction of the epidermal growth factor recep-tor(EGFR)mutation status in invasive lung adenocarcinoma.Methods 231 patients with pathologicallyconfirmed invasive lung adenocarcinoma with EGFR mutation data were retrospectively analyzed and divided into training set(n=162)and test set(n=69)at a ratio of 7:3.The minimum redundancy maximum relevance(mRMR)and the least absolute shrinkage and selection operator(LASSO)were used to select best radiomics features for constructing radiomics model to indentify the mutation status of EGFR,Univariate and multivariate logistic regression analysis were performed to screen the clinical,pathological and CT fea-tures associated with mutation status of EGFR for constructing clinical model and radiomics nomogram based on clinical,patho-logical,CT features and radiomics features.Receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to evaluate the prediction efficiency of models and the AUC differences were compared by DeLong test.And calibration curve and decision curve were used to analyze to assess the clinical value of radiomics nomogram.Results 19 radiomics fea-tures were selected to build radiomics model and the AUC value of radiomics model were 0.79 and 0.76.The clinical model was composed of sex,pathological stage and vessel convergence sign and the AUC value of clinical model was 0.75 and 0.75.The AUC value of radiomics nomogram was 0.82 and 0.80.DeLong test showed that in training set,the AUC value of radiomics nomo-gram had a better performance than clinical model(P<0.05),there was no significant difference in the prediction performance of radiomics model and clinical model,and in test dataset,there was no significant difference in the prediction performance of three models.Radiomics nomogram had a better performance goodness of fit and DCA showed radiomics nomogram had a better performance than clinical model.Conclusion Radiomics based on enhanced CT had a better performance in predicting the EGFR mutation status in invasive lung adenocarcinoma.