## 3 Results
### LASSO-Cox for Feature Selection  
The features with non-zero coefficients in the 100 LASSO-Cox-based models predicting the OS, i.e., non-trivially contributing to the models, are reported in Supplementary Material, Table S1. Sensitivity analysis of the occurrence cut-off, conducted on the training dataset, revealed that the \(p\)-values obtained from the long-rank test comparing patients with the predicted score higher or lower than the median predicted value (mtraining) increased with the occurrence cut-off. At the same time, as expected, the number of RFs decreased, as reported in Figure 3a. The difference in BICs between the adjacent models to the one with the smallest BIC was between two and six, indicating 'positive' evidence in favor of the identified optimal model. The calculated LASSO-Cox-based model using the high occurrence RFs was able to predict the OS of the validation datasets using the mtraining as cut-off and the same RFs, but extracted from Par-CT and Med-CT images. The best BIC value was associated with an occurrence rate of 44-45% selecting five prognostic RFs (i.e., area density enclosing ellipsoid, cluster shade, intensity histogram quartile coefficient of dispersion, minimum value, normalized zone distance non-uniformity). Figure 3: _Cont._  
Figure 2: Axial CT images of the same representative patient using the same slice height, extracted from the validation databases, i.e., (**a**) Par-CT and (**b**) Med-CT. The purple and red zones are those identified by SOPHiA DDM as COVID-19 damage (hazy) and high density (Vascular tree), respectively. The Kaplan-Meyer curves generated from the LASSO-Cox model were obtained with an occurrence rate higher than 45% in all the datasets and using mtraining as the cut-off are shown in Figure 4a-c for the training, Par-CT, and Med-CT validation sets, respectively. The behavior reported in Figure 4a-c was observed in the models obtained with all the cut-offs, except for those with an occurrence cut-off higher than 49%, since they retained only two RFs to be included in the LASSO-Cox models. Figure 4: _Cont._  
Figure 3: (**a**) The number of RFs and the BIC value versus the occurrence cut-off (expressed in %) in the 100 calculated LASSO-Cox models. (**b**) The \(p\)-values of Kaplan-Meier curves comparing patients in the training and verification datasets (i.e., Med-CT and Par-CT) with a score higher or lower than the cut-off determined on the training dataset (mtraining). The red horizontal line indicates \(p\) = 0.001. Table 1 reports the values of the coefficients associated to each of the RFs indentified in the final LASSO-Cox model. In addition, Table 1 reports the ICCs obtained from the consistency analysis. Based on Hazard Ratio values (Table 1), higher values of \"intensity histogram quartile coefficient of dispersion\" and \"normalized zone distance non-uniformity\" were associated with a higher risk of death. In contrast, \"area density enclosing ellipsoid\", \"cluster shade\", and \"min value\" were associated with reduced risk. Figure 4: The Kaplan-Meier curves of OS versus time, expressed in days, in (**a**) training, and the two validation datasets, i.e., (**b**) Par-CT and (**c**) Med-CT. Patients are grouped using the LASSO-Cox-based score higher or lower that the median value calculated using the training dataset. Figure 5a-c reports the box plots of \"area density enclosing ellipsoid\", \"cluster shade\" and \"intensity histogram quartile coefficient of dispersion\", respectively. These figures show similar behaviors of RFs in alive and dead patients, irrespective of the two reconstruction approaches. Moreover, for Med-CT, the feature \"min value\" (Figure 5d) collapsed to almost the single value of \(-1020\) HU, likely due to the voxel dimension in the reconstructed images. This justifies the very low agreement found in the ICC analysis. Notably, the predictive value of the LASSO-Cox-based model was statistically significant among groups (i.e., remains informative).