Abstract

Abstract

Abstract

Abstract
Epidermal Growth Factor Receptor (EGFR) is one of the most frequently mutated genes in lung cancer. Its mutation status characterization is crucial for personalized treatment in Non-Small-Cell Lung Cancer (NSCLC). Biopsy is the gold standard for characterizing the EGFR mutation status. However, it is an invasive time-consuming method and is often burdensome or even impractical for some patients. Therefore, it is of utmost importance to identify alternative non-invasive methods for classifying this mutation. Computed Tomography (CT) images represent a non-invasive, safer and faster method to directly characterize lung cancer. This study developed a comprehensive radiomic approach for EGFR mutation classification using CT images, in which two preprocessing strategies were compared and five machine learning algorithms were evaluated across different datasets. We analyzed two independent datasets individually and combined, implementing lung containing nodule versus bounding box around nodule preprocessing approaches. Radiomic features were extracted using PyRadiomics and selected through Principal Component Analysis (PCA) (65-95% variance thresholds) and pairwise correlation filtering. The results demonstrated that the lung with nodule strategy achieved better and more consistent performance compared to the bounding box around the nodule method. The best performance (AUC=0.780) was achieved using Random Forest with correlation filtering. The results suggest that radiomics may be a potential support tool for EGFR classification when biopsy is not feasible or recommended. This would enable safer and more efficient personalized treatment. Nevertheless, the results underscore the need for larger, diverse datasets to improve model robustness for characterizing such complex and variable information before clinical integration. © 2025 IEEE.