## 4 Discussion
In addition, a monotonic trend for the percentage of discriminative features as a function of volume (R\({}^{2}\)\(>\) 0.85) was obtained for GLCM, GLDM, and GLSZM classes for scanner A and for first-order and GLSZM classes for scanner B (Figure 4, bottom right). In the case of scanner C, a general worse performance across volumes and feature classes was obtained, with no apparent trends. Supplementary Figures S2-S10 report detailed results for each feature and separately for each couple of textures. Aggregating data by image filters might mask some additional and informative trends. Indeed, as shown in Supplementary Figure S13, the differences among the three textures might be suppressed or highlighted by specific filters. Hence, the inability to discriminate textures of some features might be wrongly attributed to VOI size when it might be an effect of the filter instead. For this reason, in Figure 5, we report the analysis of discriminative features stratified by scanner and feature class for unfiltered (original) images only. For all scanners, a very interesting result emerged about the discriminative reliability of almost all features belonging to first-order and GLCM classes, for all volumes, without an apparent association between the percentage of discriminative features and volume (Figure 5, bottom right). According to these results, these categories should be favored when analyzing original images, especially if small volumes are present in the data set under investigation. As for the remaining feature classes, with 1.5T scanners, GLRLM, GLSZM, and NGTDM categories appear to be discriminative with a quite high percentage of features (80-93%) but only above the 3.3 cm\({}^{3}\) volume threshold. Similar considerations can be derived for each filtered image by inspecting Supplementary Materials Figures S14-S22. As a general comment, focusing on 1.5T scanners only, for most filtered images, it can be observed that first-order and GLCM features confirm their discriminative superiority across all volumes, even the small ones. The GLDM and GLRLM features are generally less discriminative, and finally, GLSZM and NGTDM features can equal first-order and GLCM performance but in most cases for larger volumes only. The worst discriminative performance was found with the wavelet-HH filter and with the Laplacian of Gaussian (LoG) filter, in the latter case possibly due to the high sigma value considered (6 mm), resulting in a smoothing level that reduces the texture differences among inserts; different values of sigma might be investigated to improve the discriminative power of the features. Conversely, wavelet-LL filtered images, meant as an approximation of original images, which likely reduces noise and other artifacts affecting radiomic features, provided very high discriminative performance, not only confirming the results observed with first-order and GLCM features on original images but also increasing the performance of NGTDM features in the case of scanners A and B, and even of GLRLM and GLSZM features in the case of scanner B.  
Scanner C, the 3T scanner, provided, in general, worse discriminative performance and noisier results without evidence of specific trends. More in detail, it demonstrated an overall lower discriminability of radiomic features across sizes when compared with the 1.5T scanners (scanners A and B). This result is likely due to the higher magnetic field that leads to higher water-fat shift artifacts and possibly other artifacts (related to hardware, differences of SNR, parallel imaging not used in the current study, susceptibility, and bandwidth) that may reduce the discriminative ability of radiomic features between textures, as also seen in terms of repeatability. Figure 6 provides an example of the increased water-shift artifact in scanner C, particularly visible at the inserts' edges but potentially present also within the inserts, where many interfaces occur.