## 3 Results
### Performance of the Machine Learning Models  
Extreme Gradient Boosting and Random Forest Classifiers were trained using 70% of the datasets resulting from the features selection steps. The two classifiers were evaluated in terms of several metrics, and the results on the training dynamics are shown using the 10-Fold CV; the remaining 30% of the dataset was considered for internal validation purposes. Considering the ML models developed to predict the best clinical response (BCR_CT and BCR_PET), the results from 10-Fold CV were encouraging, with all AUC values exceeding 0.89. The best-performing model in this phase was the XGBoost classifier trained on BCR_PET-derived RFs, which achieved an AUC of 0.97 and CA = 0.91 (Table 2). In internal validation, all models continued to show satisfactory performance, with AUC values above 0.71. Notably, the RF BCR_PET model achieved an AUC of 0.88, a CA of 0.82, a TP rate of 68.80%, and a TN rate of 94.10%. The models demonstrated particularly strong discriminative power for the negative class (i.e., non-responders), as reflected by the high TN rate. The ROC curves obtained from the 10-Fold CV and internal validation are shown in Figure 3.  
In the tumor-grade classification task, the CT-based models achieved AUCs of 0.78 with RF and 0.83 with XGBoost during 10-Fold CV. Both models reached a CA of 76% and exhibited comparable performance in terms of precision (0.77 vs. 0.76), sensitivity (0.76 for both), and specificity (0.74 vs. 0.75). The PET-based models yielded AUCs of 0.77 with RF and 0.79 with XGBoost, with XGBoost outperforming Random Forest in accuracy (78% vs. 70%) and demonstrating slight improvements in precision, sensitivity, and specificity (Table 3). In the internal validation cohort, XGBoost maintained its superiority in the CT setting, achieving an AUC of 0.80 compared to 0.70 for Random Forest, along with higher classification accuracy (73% vs. 55%), precision (0.89 vs. 0.67), and specificity (0.89 vs. 0.67). In the PET setting, both models demonstrated robust performance, with AUCs of 0.75 for RF and 0.78 for XGBoost. XGBoost offered modest gain in precision (0.81 vs. 0.78) and specificity (0.81 vs. 0.77), as shown in Table 4. The ROC curves obtained from the 10-Fold CV and internal validation are shown in Figure 4. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Training 10 FoldCV** & **AUC** & **CA** & **PRE** & **SEN** & **SPE** & **TP** & **TN** \\ \hline BCR\_CT Model & RF & 0.91 & 0.87 & 0.87 & 0.87 & 0.87 & 82.90\% & 90.20\% \\  & XGB & 0.89 & 0.88 & 0.88 & 0.88 & 0.88 & 85.00\% & 90.40\% \\ \hline BCR\_PET Model & RF & 0.96 & 0.91 & 0.91 & 0.91 & 0.92 & 93.60\% & 88.90\% \\  & XGB & 0.97 & 0.91 & 0.91 & 0.91 & 0.91 & 91.80\% & 90.70\% \\ \hline \hline \end{tabular}  
* AUC = Area Under the Curve; BCR = best clinical response; CA = classification accuracy; PRE = precision; RF = Random Forest; SEN = sensitivity; SPE = specificity; XGB = Extreme Gradient Boosting; TP = true positive; TN = true negative.  
\end{table} 
Table 2: Performances scores in 10-Fold CV for each BCR ML Model. Figure 3: Comparison of ROC curves for the CT and PET models obtained from 10-Fold cross-validation and internal validation. The ROC curves of the Random Forest (RF) and Extreme Gradient Boosting (XGB) learners in the prediction of best clinical response (BCR) are represented on the left (**A**) and on the right (**B**), respectively. The scores for each validated ML model are graphically represented in the radial plots shown in Figure 5. In addition, Figure 6 graphically compares the two best-performing models in the BCR prediction task, with RF learner for CT data and XGBoost learner for PET data. For tumor grade prediction, XGBoost emerged as the best-performing learner, with the PET-based model outperforming the CT-based model.