## 4 Discussion
The most relevant radiomic variables in the model used in the present study were related to the Gray-Level distribution and disorder/inhomogeneity in the image (i.e., entropy, complexity, 10 th intensity percentile). Some of these features were found in models developed by [28] and were also informative in a univariate analysis carried out by [33]. As expected, looking at the same univariate analysis as in the study of [33], the performance of a more complex model is consistently better than that of a single radiomic variable. The dimensionality (2D vs. 3D) of the images probably affected performance; in fact, the present models consistently outperformed those obtained using radiographic chest images as in the studies of [6; 28; 33]. The authors hypothesized that ageing of pulmonary tissue may affect several of the relevant radiomic features left after the LASSO feature reduction, as shown in Supplementary Materials Figure S3. Unfortunately, the current dataset used in the study did not allow discriminating the impact of lung tissue ageing, even when using the Neural Network approach. Figure S2 highlights how disorder and inhomogeneity in the grey levels are related to damage in the lungs as well as to the age of the patient. To the best of the authors' knowledge, this has not previously been highlighted. As a final consideration, it is important to note that the semi-automatic segmentation tool significantly reduced human costs in terms of manpower and time with respect to a manual approach. Moreover, the segmentation of a single patient may require from 10 to 60 min when performed manually against the 2-6 min necessary with an automatic tool, depending on computer and software specifics. It is noteworthy that manual segmentation, which is feasible only with small patient cohorts, may achieve a slightly better predictive performance [6; 8]. On the other hand, the time utilized by trained radiologists to manually segment all the chest CT images may be unavailable in a busy department, especially during pandemic events. Some of the limitations of the present study include the imbalanced nature of the majority of the clinical variables available as well as the reduced number of clinical features available. However, this may also represent one of the strong points of the study, since it showed that, even with a basic amount of information, it was still possible to obtain acceptable results. Another similar point is that of the length of time from the date of the CT scan to the outcome. It is a clear limitation since only the first CT was considered, hence concealing all the disease progression after the first scan. However, it showed that it was possible to have a quick and reliable evaluation of patients at admission, allowing better allocation of hospital resources. Some future prospects in this regard may include an additional analysis of the dataset in a delta-radiomics setting in which disease progression is also included in the patient evaluation by looking at the changes in radiomic features in successive CT scans. Another interesting prospect would be to additionally investigate the relationship between patient characteristics, such as age, and radiomic variables extracted from various organs.