## 4 Discussion  
In this work, CT images acquired on a population of 435 patients affected by SARS-CoV-2 were analyzed to assess the robustness in the ability of a radiomic-based LASSO-Cox model in the prediction of patient OS when faced with different reconstruction algorithms. In the literature, similar models have rarely been validated on large datasets obtained in different conditions from those employed during the acquisition of the training set (e.g., Med-CT vs. Par-CT or training images). Despite these unfavorable conditions, our validation datasets represent a real-world setting representative of clinical practice during the SARS-CoV-2 pandemic. To the best of our knowledge, this represents the largest patient cohort analyzed with this methodology and the only one where a comparison between different reconstruction methods is made. Table 2 summarizes similar studies reported in literature [7; 9; 18; 19] compared to this study. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Patients** & **Follow-up** & **Segmentation** & **Predictors** & **Modelling** & **Outcome** & **Performance** \\  & **Validation** & **Length** & **Type (Tool)** & **Predictors** & **Modelling** & **Outcome** & **Performance** \\ \hline \multirow{3}{*}{[7]} & 167 & \multirow{3}{*}{NS} & Manual & \multirow{3}{*}{RFs from lesion} & \multirow{3}{*}{Adaboost} & \multirow{3}{*}{Death} & \multirow{3}{*}{AUC = 0.71} \\  & [NS/NS/NA] & & & & & & \\ \cline{3-8}  & & & & CXR & & & \\ \hline \multirow{3}{*}{[9]} & 96 & \multirow{3}{*}{62} & Semi-automatic & \multirow{3}{*}{Demographics, Laboratory tests and RFs} & Lasso-Cox & \multirow{3}{*}{OS, death} & \multirow{3}{*}{AUCtest = 0.871} \\  & [66/30/NA] & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{3}{*}{[18]} & EarlyCT 317 & \multirow{3}{*}{\(\sim\)30} & Automatic & \multirow{3}{*}{Demographics, Comorbidities, RFs} & Lasso-Cox & \multirow{3}{*}{Poor} & AUCtest,early = 0.816 \\  & [139/36/NA] & & & & & & \\ \hline \multirow{3}{*}{[19]} & 152 & \multirow{3}{*}{NS} & Manual by & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{XGBoost} & \multirow{3}{*}{Death} & \multirow{3}{*}{AUCcombined = 0.95} \\  & [106/46/NA] & & & & & & \\ \cline{3-8}  & & & & & & \\ \hline \multirow{3}{*}{This study} & 435 & \multirow{3}{*}{948} & Semi-automatic & \multirow{3}{*}{RFs} & Lasso-Cox & \multirow{3}{*}{OS, death} & AUCPart-CT = 0.764 \\  & [167/72/196] & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{3}{*}{[19]} & 152 & \multirow{3}{*}{NS} & Manual by & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{XGBoost} & \multirow{3}{*}{Death} & AUCcombined = 0.95 \\  & & & & & & \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{3}{*}{This study} & 435 & \multirow{3}{*}{948} & Semi-automatic & \multirow{3}{*}{RFs} & [PERSON] & \multirow{3}{*}{OS, death} & AUCPart-CT = 0.764 \\  & [167/72/196] & & & & & & \\ \cline{3-8}  & & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of similar studies. In previous studies [7; 8; 19; 20], multiple attempts have been made to predict poor prognosis in patients, be it by predicting the outcome of death, the need for artificial ventilation, or admission to the Intensive Care Unit (ICU). In all these previous cases, the studies did not perform a time-to-event analysis like the one shown in the present study. Table 2 summarizes studies [7; 8; 19; 20] investigating RFs extracted from CT images as predictors of death, OS, or poor outcomes. The AUC values of the developed models ranged from 0.71 to 0.976. This variability can be explained by the fact that the ROC curves are related to a binary classification of patients based on survival without taking time into account. The model developed in the present study is optimized to separate patients according to survival rates, whereas binary classification models, such as those developed in [7; 19; 20], are optimized to predict only the event of mortality.