## 4 Discussion
However, the extraction only produced the category of original features without allowing the use of filters (i.e., wavelet filters), which proved largely informative in [10] and may explain the reduced, although statistically significant, separation in the Kaplan-Meyer curves presented in this study. Unfortunately, not having access to radiomic analysis after filtering makes the comparison of the features building the multivariate models quite difficult due to the lack of overlap between the findings. Regarding the prognostic features, one of the prognostic features was the \"minimum value\", which can be directly obtained after segmentation of the image and corresponds to the lowest intensity of HU within the VOI. Another prognostic feature was the \"intensity histogram quartile dispersion coefficient\" which measures the dispersion of the discretized intensity distribution and represents a more robust alternative to the intensity histogram coefficient of variance. Another feature retained in the final LASSO-Cox model was the \"area density enclosing ellipsoid\", listed as a morphological feature that represents the ratio between the area of VOI and of the enclosing ellipsoid. The model retained the RF \"cluster shade\", which quantifies the skewness and uniformity in the gray-level co-occurrence matrix (GLCM)-based features. By design, the higher the computed cluster shade, the greater the asymmetry of the gray-level distribution. The last prognostic feature was the \"normalized zone distance non-uniformity\", listed in the gray-level distance zone-based features. This feature is a normalized version of the zone distance non-uniformity feature, which measures the distribution of zone counts over the different zone distances. This parameter is low when zone counts are equally distributed along zone distances. Finally, this study did not use clinical information, laboratory findings, or patient co-morbidities which, as has been shown in the literature, might lead to further improvements in the performances of the predictive models [8; 9; 10]. This is because we would then focus analysis on image-extracted RFs, since our objective was to assess the predictive value of RFs extracted from high-resolution CT and look at differences in the model application due to image two types of image reconstruction, in which clinical information may only have an indirect effect. Overall, our method seems to be robust and able in identifying COVID-19 patients with good and poor prognosis, irrespective of reconstructions adopted in the clinical practice in our institute. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app122312065/s1](https://www.mdpi.com/article/10.3390/app122312065/s1)]([https://www.mdpi.com/article/10.3390/app122312065/s1](https://www.mdpi.com/article/10.3390/app122312065/s1)), Table S1: Occurrence rate of RFs (\(\backsim\)10%) generated out of 100 LASSO-Cox models using the training datasets. Conceptualization, L.S. ([PERSON]); Methodology, M.F.M. and S.S.; Validation, L.S. ([PERSON]); Formal analysis, G.P., M.S. and L.S. ([PERSON]); Investigation, G.P., M.S. and F.C. ; Data curation, L.S. ([PERSON]) and S.S.; Writing--original draft, G.P., L.S. ([PERSON]), M.F.M. and S.S.; Writing--review & editing, F.C. and L.S. ([PERSON]); Visualization, F.C. and R.G. ; Supervision, R.G. and L.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript. The SOPHIA DDM software licence was funded by the S. Orsola Polytinic Foundation (Fondazione Policlinico S. Orsola) of Bologna, Italy. 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).