## 4 Discussion
Indeed, in all scenarios, the prognostic role of RFs was confirmed with comparable model performance (Table 3, Supplementary Tables S4-S6, and Table 4) when switching among two segmentation methods and two different criteria for the selection of the lesion for RFs calculation. A partial explanation for these results can be found in the fact that, for many patients (91/112 patients with segmentation Method-1, and 98/112 patients with segmentation Method-2), the target lesion was at same time the one exhibiting both the highest uptake (SUV\({}_{\text{max}}\) lesion) and the largest volume (VOL\({}_{\text{max}}\) lesion). However, in spite of this, the observed reproducibility of model performance is an important result in support of the hypothesis that prognostic information is contained in PET images, it can be captured by radiomic analysis, and its assessment is robust since it is not strictly related to the use of a specific image analysis methodology. Robustness of radiomic analysis and the generalizability of its results are currently the most important issues to be overcome in order to make radiomics useful for clinical practice. Demonstratingthat a radiomic finding is robust to methodology, as is the case of the present study, is an important step in indicating that the hypothesis deserves to be further investigated on independent populations. Such kinds of analysis are highly encouraged by researchers in the field but are still quite uncommon [27]. Of note, our results are in agreement with the previous literature. More in detail, in the main analysis, the RFs contributing mostly to the prediction of both PFS and OS were texture features, as evidenced in Figure 1. Notably, most features were in common between the radiomic score for PFS and OS, supporting the hypothesis that relevant and novel prognostic information can be derived by the RFs selected by the elastic net Cox model. The feature Coarseness calculated from the neighboring gray level dependence matrix (NGLDM) was among the top three features for both clinical endpoints, and is related to the rate of the spatial variation of radiopharmaceutical uptake, quantifying the prevalence of regions with sudden or low uptake variation. The features low gray-level zone emphasis (LGZE) and short-zone low gray-level emphasis (SZLGE) calculated from the gray level zone length matrix (GLZLM) were among the top two features for PFS and OS respectively, and in our database they exhibited similar behaviour (linear correlation R\({}^{2}\) = 0.90). These RFs are both related to the presence and distribution of zones with low intensity, in particular small zones with homogeneous low uptake. The top feature in the radiomic score calculated for PFS was the feature short-run low gray-level emphasis (SRLGE) calculated from the gray level run length matrix (GLRLM), which, however, played a minor role as a predictor of OS. Similar to the previous GLZLM features, it is related to the distribution of short runs (consecutive voxels along a defined direction) with homogeneous low uptake. Previously, [PERSON] et al. [14] showed that GLZLM features (related to the distribution of long homogeneous zones) were predictive biomarkers associated with disease free survival, whereas a first-order feature, kurtosis, was associated with OS. Similarly, [PERSON] et al. [28] found a significant association of GLZLM features extracted from the largest lesion with 2-year event-free survival. The same research group [29] showed that a first-order metric still related to heterogeneity, skewness-H, was the only independent predictor of PFS. [PERSON] et al. [30] found a significant association also using multivariate analysis of the feature RLN, extracted from GLRLM matrix, with both PFS and OS.