## 4 Discussion  
In this study, we assessed \(EGFR\) mutation status, using radiomic features extracted from the entire lung volume containing the nodule on CT images. Six ML algorithms were trained and used in conjunction with pairwise correlation, PCA, and QR decomposition feature selection methods. These filtering techniques were employed since using too many features in the classification algorithm can lead to overfitting, in which noise or irrelevant features may exert undue influence on classification decisions. In addition, a baseline without feature selection method was also implemented to compare results obtained with and without feature selection. The results of this study suggest that the linear learning models--SVM with linear kernel, Elastic Net, and Logistic Regression--perform well with quantitative imaging features as their predictors, whereas the SVM classifier based on the RBF kernel performs poorly. Additionally, the commonly used classifiers, Random Forest and XGBoost, show acceptable performance results. Furthermore, we found that the model less used in radiomics and radiogenomics, but commonly used in genomics--Elastic Net--was one of the best performing classifiers, as shown in a previous study that investigated the ability of various ML classifiers to accurately predict lung cancer nodule status [47]. The present work also indicates that the Linear SVM classifier in conjunction with PCA feature selection with 70% variance should be considered in future \(EGFR\) mutation status classification studies. In our opinion, it is crucial to highlight the fact that linear models obtained the best results in the present study, as it may change the direction and extend the use of simpler and more interpretable models in seemingly complex problems. We also show that, in general, feature selection methods that reduce the number of features prior to model training appear to improve predictive performance compared to previous radiomic analyses [47; 48; 49; 50]. In the radiogenomics field, a large number of features tend not to provide additional information because they are highly correlated and are linear combinations of others. However, in ensemble learning models, such as Random Forest and XGBoost, which perform automatic feature selection, additional feature filtering does not seem to have a significant impact on model performance. Considering the feature selection methods, it was not possible to identify the one that gives better results for all classifiers since the models are defined with different mathematical principles and optimize different parameters; as a consequence, different classifiers provide better results when combined with different numbers of radiomic features. Nevertheless, based on the best results per feature selection method, we recommend considering the PCA method with 70% and 75% of variance over the pairwise correlation filter and QR decomposition, as these two methods seem to remove very important features for model predictions. Considering approaches that employ only radiomic features, the outcomes of this work show that radiomic characteristics from the entire lung containing the nodule provided better results in \(EGFR\) mutation status assessment compared to traditional nodule-based approaches and to other methodologies that consider other lung structures [4; 5; 14; 51]. However, direct performance comparisons between models trained and tested with data from different datasets would not bring a fair discussion point to this study. Nevertheless, the results of the current study indicate that the CT features with the highest correlation with \(EGFR\) mutation are from the lung that has the nodule and these are therefore the main contributors to the model decision.