## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model Name** & **Training AUC** & **Testing AUC** & **Sens** & **Spec** \\ \hline \multirow{4}{*}{**Random Forest classifier**} & Clinical & 0.98 \(\pm\) 0.01 & 0.63 \(\pm\) 0.09 & 44\% & 83\% \\ \cline{2-6}  & Radiomic & 1.00 \(\pm\) 0.01 & 0.64 \(\pm\) 0.08 & 41\% & 86\% \\ \cline{2-6}  & Radiological & 0.93 \(\pm\) 0.01 & 0.49 \(\pm\)0.07 & 19\% & 79\% \\ \cline{2-6}  & All & 1.00 \(\pm\) 0.01 & 0.67 \(\pm\) 0.11 & 44\% & 88\% \\ \hline \multirow{4}{*}{**Fully connected Neural Network**} & Clinical & 0.82 \(\pm\) 0.11 & 0.82 \(\pm\) 0.01 & 76\% & 75\% \\ \cline{2-6}  & Radiomic & 0.83 \(\pm\) 0.09 & 0.75 \(\pm\) 0.01 & 77\% & 64\% \\ \cline{2-6}  & Radiological & 0.69 \(\pm\) 0.09 & 0.62 \(\pm\) 0.02 & 63\% & 56\% \\ \cline{2-6}  & All & 0.91 \(\pm\) 0.11 & 0.81 \(\pm\) 0.01 & 69\% & 83\% \\ \hline \multirow{4}{*}{**Lasso regularized classifier**} & Clinical & 0.84 \(\pm\) 0.01 & 0.82 \(\pm\) 0.11 & 69\% & 83\% \\ \cline{2-6}  & Radiomic & 0.81 \(\pm\) 0.01 & 0.75 \(\pm\) 0.10 & 64\% & 78\% \\ \cline{2-6}  & Radiological & 0.67 \(\pm\) 0.01 & 0.61 \(\pm\) 0.09 & 70\% & 51\% \\ \cline{2-6}  & All & 0.88 \(\pm\) 0.01 & 0.82 \(\pm\) 0.10 & 85\% & 68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table with all the AUCs obtained using the different models during both training and testing. Sensitivity (Sens) and Specificity (Spec) are relative to the testing phase. \begin{table}
\begin{tabular}{c l} \hline \hline
**Model Name** & **Relevant Features (Coefficient)** \\ \hline \multirow{2}{*}{**Clinical**} & Age (years) (0.116771), Respiratory Rate (0.082292), Sex (\(-\)0.037591), \\  & Fever (\(-\)0.022923) \\ \hline \multirow{4}{*}{**Radiomic**} & 10 th intensity percentile (\(-\)0.125094), Intensity-based interquartile range (0.103349), Complexity (\(-\)0.102924), Cluster prominence (\(-\)0.064690), Area density-aligned bounding box (\(-\)0.039374), Entropy (0.033002), Number of compartments (GMM) (\(-\)0.032441), Asphericity (0.028517), Local intensity peak (0.028478), Global intensity peak (\(-\)0.024832), Intensity range (0.012509), Fat surface (0.007267) \\ \hline \multirow{4}{*}{**Radiological**} & Ground-glass opacity (\(-\)0.043875), Lung consolidation (0.038143), X-ray Tube Current (\(-\)0.017264), kVp (0.004995) \\ \hline \multirow{4}{*}{**All**} & Age (years) (0.092963), Intensity-based interquartile range (0.057260), \\  & Respiratory Rate (0.049603), Ground-glass opacity (\(-\)0.031423), Sex (\(-\)0.028895), \\ \cline{1-1}  & Complexity (\(-\)0.028606), Lung consolidation (0.017272), Fever (\(-\)0.016933), \\ \cline{1-1}  & X-ray Tube Current (\(-\)0.016908), Area density—aligned bounding box (\(-\)0.009676), Cluster prominence (\(-\)0.006663), Fat surface (0.004984), Number of compartments (GMM) (\(-\)0.001448), Local intensity peak (0.000195) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Set of variables chosen via Lasso regression with respective weights of the linear combination in the tested version of the model. Arranged in descending order of absolute value; in all cases, the Intercept = 0.178899. Figure S2 shows an example of how age and SARS-CoV-2 disease affect CT image appearance and grey level inhomogeneities, consequently impacting the values of the radiomic features. One such example is entropy, which did not remain in the final model, being related to patient age. In particular, the entropy values obtained from the images were 8.29, 9.96, 8.22, and 9.97 for the patients illustrated in panels A, B, C, and D, respectively. A and C were both under 70 years of age while [PERSON] and D were both older. [PERSON] and [PERSON] were successfully discharged from the hospital while [PERSON] and D died from SARS-CoV-2 disease. These findings suggested investigating the impact of ageing on several relevant radiomic features, as shown in Supplementary Materials Figure S3 (entropy/complexity).