## 3 Results  
The number of radiomic features removed and retained after the implementation of the nine feature selection methods is shown in Table 1.  
The hyperparameters that achieved the best performance on the test set for each classifier and feature selection method are shown in Tables S7-S12 of the supplementary material. Furthermore, in the supplementary material, the AUC, precision, sensitivity, and specificity metrics, for each classifier-feature selection method combination, can be found in Tables S13-S18.  
Figure 3 gives the mean AUC (average value of all 50 results) for each classifier across the various feature selection methods in a heatmap form, whereas Table 2 summarizes the best performance results obtained in the present study for each one of the six classifiers. SVM with linear kernel, Elastic Net, and Logistic Regression classifiers had the best overall predictive performance and showed the best results when combined with the PCA feature selection method with 70% of variance, presenting AUC values greater than 0.7. It can thus be concluded that these classifiers performed well in predicting the _EGFR_ mutation status. In fact, the highest AUC was obtained with the Linear SVM classifier and the PCA 70% (AUC = 0.737 \(\pm\) 0.018). Furthermore, the implementation of the Logistic Regression classifier with PCA 70% resulted in the highest precision (Precision = 0.682 \(\pm\) 0.099) and sensitivity (Sensitivity = 0.699 \(\pm\) 0.039) values. On the other hand, the XGBoost classifier with PCA 70% achieved the highest specificity result (Specificity = 0.767 \(\pm\) 0.017). Among all feature selection methods, PCA yielded the highest AUC results on average in five of the six classifiers studied, whereas QR decomposition held lower AUC values. The standard deviations of all results are fairly similar, which shows that no metric had a significantly higher or lower variance than the others.  
\begin{table}
\begin{tabular}{c c c} \hline
**Feature Selection** & **Number of Removed** & **Number of Retained** \\
**Method** & **Features** & **Features** \\ \hline Pairwise correlation filter & 1223 & 93 \\ PCA 65\% & 1312 & 4 \\ PCA 70\% & 1311 & 5 \\ PCA 75\% & 1310 & 6 \\ PCA 80\% & 1309 & 7 \\ PCA 85\% & 1306 & 10 \\ PCA 90\% & 1301 & 15 \\ PCA 95\% & 1290 & 26 \\ QR decomposition & 988 & 328 \\ \hline \end{tabular}
\end{table}
Table 1: Number of removed and retained features after the implementation of the pairwise correlation filter, Principal component analysis (PCA) with feature set variance ranging from 65% to 95%, and QR decomposition.  
Figure 3: Heatmap with the AUC of each classifier/feature selection combination. Dark colors stand for the best results, while light colors represent the worst outcomes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Classifier** & **Feature Selection** & **AUC** & **Precision** & **Sensitivity** & **Specificity** \\ \hline SVM (linear kernel) & PCA 70\% & 0.737 \(\pm\) 0.018 & 0.644 \(\pm\) 0.012 & 0.615 \(\pm\) 0.010 & 0.685 \(\pm\) 0.095 \\ Elastic Net & PCA 70\% & 0.733 \(\pm\) 0.011 & 0.585 \(\pm\) 0.048 & 0.611 \(\pm\) 0.033 & 0.715 \(\pm\) 0.013 \\ Logistic Regression & PCA 70\% & 0.725 \(\pm\) 0.012 & 0.682 \(\pm\) 0.099 & 0.699 \(\pm\) 0.039 & 0.743 \(\pm\) 0.079 \\ XGBoost & PCA 70\% & 0.697 \(\pm\) 0.032 & 0.640 \(\pm\) 0.036 & 0.632 \(\pm\) 0.040 & 0.767 \(\pm\) 0.017 \\ Random Forest & No filter & 0.696 \(\pm\) 0.011 & 0.683 \(\pm\) 0.090 & 0.688 \(\pm\) 0.029 & 0.721 \(\pm\) 0.012 \\ SVM (RBF kernel) & PCA 75\% & 0.583 \(\pm\) 0.014 & 0.601 \(\pm\) 0.008 & 0.530 \(\pm\) 0.017 & 0.623 \(\pm\) 0.009 \\ \hline \end{tabular}
\end{table}
Table 2: Performance results of each classifier with the best AUC. For each combination of classifier and feature selection method, the evaluation metrics AUC, precision, sensitivity, and specificity are presented as mean \(\pm\) standard deviation. AUC—Area Under the Curve; SVM—Support Vector Machine; XGBoost—Extreme Gradient Boosting; RBF—Radial Basis Function.