Prediction of EGFR Mutation Status in Non-small Cell Lung Cancer Based on Multiparametric MRI Radiomics

Background: To establish and validate a predictive model based on magnetic resonance imaging (MRI) radiomics features combined with clinicopathological factors to predict the mutation status of epidermal growth factor receptor (EGFR) in non-small cell lung cancer（NSCLC). Patients and methods: A total of 91 NSCLC patient (72 in the training cohort and 19 in the validation cohort) were included in this study. A total of 1708 radiomics features were extracted from the MRI (T2w and CET1w) sequences. The variance threshold method combined with the univariate selection method, least absolute shrinkage and selection operator (LASSO) regression were used to screen important radiomics features, calculate radiomics scores, and construct a radiomics model. The combination of radiomics scores (Rad scores) and independent predictive factors was based on multivariate logistic regression analysis to construct a radiomics nomogram to predict EGFR mutation status. The predictive performance and clinical practicality of the model were evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Result: EGFR mutations were identified in 30.8 % (28/91) of patients. Thirteen important radiomic features were selected from 1708 radiomics features. The radiomics model effectively classified EGFR mutants and wild-type, with AUCs of 0.846 and 0.808 for the training and validation cohorts, respectively, and had a higher diagnostic efficiency, with AUCs of 0.880 and 0.859, respectively. The calibration curve showed that the model had a good predictive performance, and the decision curve indicated that the radiomic nomogram had high clinical benefits. Conclusion: The predictive model based on MRI radiomics has good diagnostic efficacy for EGFR mutation status in NSCLC and can provide guidance for individualized targeted therapies.