Radiomics Analysis to Enhance Precise Identification of Epidermal Growth Factor Receptor Mutation Based on Positron Emission Tomography Images of Lung Cancer Patients

Abstract

How to recognize precisely epidermal growth factor receptor (EGFR) mutation in lung cancer patients owns great clinical requirement. In this study, 1575 radiomics features were extracted from PET images of 75 lung cancer patients based on contrast agents such as18F-MPG and18F-FDG. The Mann-Whitney U test was used for single factor analysis, the Least Absolute Shrinkage and Selection Operator (Lasso) Regression was used for feature screening, then the radiomics classification models were established by using support vector machines and ten-fold cross-validation, and were used to identify EGFR mutation in primary lung cancers and metastasis lung cancers, accuracy based on18F-MPG PET images are respectively 90% for primary lung cancers, and 89.66% for metastasis lung cancers, accuracy based on18F-FDG PET images are respectively 76% for primary lung cancers and 82.75% for metastasis lung cancers. The area under the curves (AUC) based on18F-MPG PET images are respectively 0.94877 for primary lung cancers, and 0.91775 for metastasis lung cancers, AUC based on18F-FDG PET images are respectively 0.87374 for primary lung cancers, and 0.82251 for metastasis lung cancers. In conclusion, both18F-MPG PET images and18F-FDG PET images combined with established classification models can identify EGFR mutation, but18F-MPG PET images have more precisely than18F-FDG PET images, own clinical translational prospects.