## 4 Discussion
Compared with [9], which followed a similar approach to our own, the AUCs are in concordance when only considering the use of RFs as input variables. Furthermore, [PERSON] et al. [9] implemented a combined approach with clinical laboratory tests, as well as comorbidities and demographic information. The abovementioned approach resulted in improvements in the predictive ability of their model developed on the exclusive use RFs. Compared to [9; 10], which had almost 80/90 total patients, the number of subjects analyzed in our study is between three and four times larger. Both studies [9; 10] used a Lung Intelligence kit (General Electric Healthcare, Milwaukee, WI, USA) and required the expertise of a radiologist. Furthermore, both studies limited analysis to a single CT image reconstruction, which is not necessarily representative of standard hospital procedures where the diagnostic question may lead to different types of reconstruction. The AUC values reported in our study refer to RFs extracted from two validation datasets (i.e., Part CT and Med-CT) generated using the same raw data with two different reconstruction approaches applied by radiologists in a real-world setting. In this context, our model results were predictive of OS, although without the use of additional clinical information, which could be missing in a busy department during a pandemic. The findings of this study, obtained from chest CT scans and analyzed with a multivariate approach, are based on more comprehensive and nuanced information than what can be found with multiple univariate analyses of chest XRs, as conducted in [7; 21]. Another substantial difference with [21] is that the segmentation was automatically performed with a manual validation through a dice coefficient, which could not be done in the present work due to the semi-automatic nature of the segmentation. Despite looking at different endpoints (i.e., survival instead of onset of severe/critical illness), our results move in the direction suggested in [22], using radiomics as a fundamental step of the analysis. Finally, both [9; 18] used an approach similar to the one reported in this study. [PERSON] et al. [18] analyzed a similarly sized cohort with the additional distinction of early/late CT scan acquisition with respect to symptom onset as declared by the patient. Dividing these two populations, they reported testing AUCs of 0.862 and 0.977. However, in their study they also had access to demographic information, as well as comorbidities of the patients, which improved the predictive ability of their model. The same considerations hold for [9] which also had available laboratory findings to describe each patient. In both [9; 18], the reported maximum follow-up was shorter than the one reported in this study. This study also presents some limitations, the first of which is the reliance on the application of a semi-automatic segmentation tool obtained with commercial CE market software, which does not allow much versatility. The second limitation is that we used RFs extracted from the lungs. This approach agrees with the observation by [PERSON] et al. [10], who reported that analyzing the lesion with radiomic features instead of the entire lung does not improve their model predictive abilities. Hence, semi-automatic segmentation may have introduced some biases in the VOIs, potentially affecting the quality of RFs. A fully manual segmentation of the volume at risk can provide a more accurate delineation of VOI, although such tasks are rarely feasible in daily clinical practice. For this reason, AI-based approaches are in use for automatic or semi-automatic segmentation of VOIs in order to develop predictive models of the outcome, e.g., for external beam radiotherapy [23]. The features extracted with the software SOPHIA DDM have been shown to be largely IBSI compliant [14].