## 3 Results
### Performance of the Machine Learning Models
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Internal Validation** & **AUC** & **CA** & **PRE** & **SEN** & **SPE** & **TP** & **TN** \\ \hline BCR\_CT Model & RF & 0.75 & 0.67 & 0.79 & 0.58 & 0.79 & 78.60\% & 57.90\% \\  & XGB & 0.71 & 0.73 & 0.78 & 0.74 & 0.71 & 77.80\% & 66.70\% \\ \hline BCR\_PET Model & RF & 0.88 & 0.82 & 0.85 & 0.82 & 0.86 & 68.80\% & 94.10\% \\  & XGB & 0.87 & 0.76 & 0.82 & 0.76 & 0.83 & 61.10\% & 93.30\% \\ \hline GRADE\_CT Model & RF & 0.70 & 0.55 & 0.67 & 0.46 & 0.67 & 66.70\% & 46.20\% \\  & XGB & 0.80 & 0.73 & 0.89 & 0.62 & 0.89 & 88.90\% & 61.50\% \\ \hline GRADE\_PET Model & RF & 0.75 & 0.77 & 0.78 & 0.77 & 0.77 & 83.30\% & 70.00\% \\  & XGB & 0.78 & 0.77 & 0.81 & 0.77 & 0.81 & 90.00\% & 66.70\% \\ \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 4: Performances scores in the validation step for each ML model. Figure 4: 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 the tumor grade (GRADE) are represented on the left (**A**) and on the right (**B**), respectively. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Training 10-Fold CV** & **AUC** & **CA** & **PRE** & **SEN** & **SPE** & **TP** & **TN** \\ \hline GRADE\_CT Model & RF & 0.78 & 0.76 & 0.77 & 0.76 & 0.74 & 80.00\% & 73.50\% \\  & XGB & 0.83 & 0.76 & 0.76 & 0.76 & 0.75 & 77.30\% & 75.00\% \\ \hline GRADE\_PET Model & RF & 0.77 & 0.70 & 0.71 & 0.70 & 0.69 & 71.40\% & 69.70\% \\  & XGB & 0.79 & 0.78 & 0.78 & 0.78 & 0.77 & 78.30\% & 77.40\% \\ \hline \hline \end{tabular}  
* AUC = Area Under the Curve; 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 3: Performances scores in 10-Fold CV for each GRADE ML model. Performance metrics from the 10-Fold CV for each BCR and GRADE ML model are summarized in Tables 2 and 3, respectively, and visualized in Figure S3. Table 4 reports the internal validation results for all models. Figure 5: Comparison of model performance during internal validation: prediction of best clinical response (BCR) on the left and tumor grade (GRADE) on the right. For BCR prediction, the best CT model (BCR_CT XGB) is highlighted in red, and the best PET model (BCR_PET RF) is highlighted in green. For tumor grade prediction, the best CT model (GRADE_CT XGB) is shown in red, while the PET models GRADE_PET RT (green) and GRADE_PET XGB (yellow) demonstrate comparable performance. Figure 6: Comparison of the best model performance for CT and PET in the prediction of best clinical response (BCR) on the left and tumor grade (GRADE) on the right. In BCR prediction, the BCR_PET model outperforms the BCR_CT model. For tumor grade prediction, the performance of the CT and PET models is largely comparable.