## 4 Discussion  
Radiomics and Deep Learning (DL) approaches for medical tomographic images, first established in radiology, have reached the nuclear medicine community as part of the effort to catch reproducible data usually missed by even a skilled naked eye. Standardized Uptake Value, Metabolic Tumor Volume, and Total Lesion Glycolysis are semiquantitative data now routinely employed for diagnosis and monitoring [18F]FDG avid disease. When using non-FDG tracers, semiquantitative variables can be provided by radiomics. A discrete number of radiomic-based papers looking for radiomic signatures in PET oncologic images have been published [3; 18], but, in order to validate the use of radiomic features in PET image interpretation, intra- and inter-institutional variability in acquisition protocols and PET tomographs, both affecting RFs in their repeatability and reproducibility, still has to be overcome [19]. In fact, the standardization of images collected in several NM departments is mandatory for the validation of a radiomic model. Our retrospective analysis enrolled 29 patients, which is the main limitation of this analysis, though the patients were homogeneous in the clinical indication for their [18F]Fluciclovine scans, carried out on the same tomograph, with fixed activity and time of acquisition. Also, the same nuclear medicine consultant and medical physicist reviewed the [18F]Fluciclovine images and manually extracted RFs. With the acquisition protocol being so strict, the implementation of study populations from other Nuclear Medicine facilities could be feasible if the PET scanners used have similar characteristics. The aim of our study was to find a radiomic signature in [18F]fluciclovine PET/CT scans that could possibly be useful in establishing Pca relapse alone or combined with clinical variables, namely PSA levels at relapse. However, when going through the data, the radiomic model was not integrated with the clinical data because, as an inclusion criterion, each patients' PSA already meant disease recurrence had taken place. Furthermore, the patients enrolled in this study had been treated by either radical prostatectomy or radiotherapy, with a relatively different cut-off significant for biochemical relapse,  
Figure 4: ROC curves of the models with training set and validation set for each model of logic linear regression. Model II, with IntensityHistogramSkewness and GLSZM_SmallZoneEmphasis, displayed the best area under the curve (AUC) values, with an AUC of 0.82 on the training set and 0.95 on the validation set. while PSA kinetics, which are more accurate [20; 21], were not available when scheduling [18F]Flucicolvine scans. Indeed, our radiomic training set detected [18F]Flucicolvine uptake sites with a higher probability to be specific for recurrence. Initially, 114 RFs were extracted, but only IntensityHistogramSkewness and GLSZM_SmallZoneEmphasis.IBSI.5 QRC were robust and significant in predicting the clinical outcome. Roughly, skewness is a single-pixel or single-voxel feature that describes the shape of the intensity distribution of data, reflecting the asymmetry of the data distribution curve. GLSZM_SmallZoneEmphasis counts groups (so-called zones) of interconnected neighboring pixels or voxels with the same gray level. Other than these two features depicting Model II, IntensityHistogramSkewness and INTENSITY.HISTOGRAM_MinimumHistogramGradientGreyLevel.SUVbw.IBSI.RHQZ and IntensityHistogramSkewness and GLSZM_SmallZoneHighGreyLevelEmphasis.IBSI.HW1V in model I and III, respectively, had AUCs that were not so dissimilar from those of model II, highlighting that skewness and the zones of gray-level patterns can describe a radiomic signature in clinical settings. These results, however, refer to our preliminary experience and need further validation with an increased sample size and reduced \"small size\" bias.