## 5 Conclusions  
The present study pointed out that semi-automatic segmentation tools allowed the extraction of the radiomic features, which allowed the construction of predictive ML models, having a performance not reaching those obtained using clinical variables but more accurate than the models based on radiological findings. The models developed could provide valuable support to clinicians and radiologists in discerning CT-based RFs representative of the extension and severity of areas affected by SARS-CoV-2.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12094493/s1](https://www.mdpi.com/article/10.3390/app12094493/s1)]([https://www.mdpi.com/article/10.3390/app12094493/s1](https://www.mdpi.com/article/10.3390/app12094493/s1)), Figure S1: Performance of the RFC on different input features: (A) Clinical, (B) Radiological, (C) Radiomic, or (D) All available features; Figure S2: CT axial images from different patients. In first column (A,C), the patients are both under 70 years of age while, in the second column (B,D), both are older. Similarly, patients in the same row (A,B and C,D) were alive and deceased at the end of the follow-up, respectively; Figure S3: Behavior of relevant radiomic features (A) Entropy and (B) Complexity as a function of Age. The patients are also represented as a Misclassification group, using the marker in the plot, and by outcome, by color, as Deceased (Red) or Alive (Blue); Figure S4. Histogram of different misclassifications individually made by the clinical model (Orange), the radiomic model (Green), or both simultaneously (Blue) according to the FcNN. The pink-colored histogram represents the distribution of the simultaneously correct predictions for both the clinical and the radiomic models. False positives are cases in which the model predicts patient mortality and the patient survives; false negatives are cases in which the model predicts that the patient survives, but the patient dies. In all cases, all the bins are normalized with the size of the population in the corresponding age range; Table S1: Description of follow-up lengths for various groups of patients in the study.  
Conceptualization, L.S. ([PERSON]), F.C. ([PERSON]), P.V., R.G. and L.S. ([PERSON]); methodology and formal analysis, L.S. ([PERSON]), M.S., G.P., E.G., N.C. and L.S. ([PERSON]); investigation, L.S. ([PERSON]), F.C. ([PERSON]), P.V., R.G., E.G., N.C. and L.S. ([PERSON]); resources, R.G.; data curation, F.C. ([PERSON]), M.B., G.V., N.B., M.F.M. and F.C. ([PERSON]); writing--original draft preparation, L.S. ([PERSON]), E.G., M.F.M., G.P., M.S. and L.S. ([PERSON]); writing--review and editing, all authors; supervision, F.C. ([PERSON]), E.G. and L.S. ([PERSON]); funding acquisition, L.S. ([PERSON]) and R.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the S. Orsola Polyclinic Foundation (Fondazione Policlinico S. Orsola) of Bologna, Italy. The APC was funded by the Alma Mater Studiorum University of Bologna, Specialization in Radiology Bologna, Italy.  
**Institutional Review Board Statement:** This study regarding the prognostic value of radiomic features was conducted and included all the patients suitable for analysis, according to the guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board (or Ethics Committee) of IRCCS University Hospital of Bologna (protocol code No. EM949-2020_507/2020/Oss/AOUBo, approved on: 16 September 2020).  
**Informed Consent Statement:** Written informed consent was obtained from all patients before publishing this paper.  
**Data Availability Statement:** Data will be available after reasonable request to the corresponding author.  
**Acknowledgments:** We would like acknowledge Eng. [PERSON] from the S. Orsola Polyclinic Foundation of Bologna, Italy.  
**Conflicts of Interest:** The authors declare no conflict of interest.