## 4 Discussion
In addition, it appears evident, especially in the case of texture 2, that scanner C images are more blurry, which might have reduced the difference among textures, resulting in a lower ability of radiomic features to distinguish them. Hence, in the case of 3T scanners, additional investigation is required, especially with further adjustment of the bandwidth, which could reduce the chemical shift observed and improve the discriminability of radiomic features. The results discussed so far refer to the specific settings used in this study for the calculation of features; future studies might investigate a filter-dependent optimization of such parameters, aiming to improve the ability of features to discriminate different textures at different volumes. A similar study with both 1.5T and 3T scanners was performed by [PERSON] et al. [12], who acquired images of phantom embedding inserts with different textures. Radiomic  
Figure 6: Comparison between water-fat shift artifacts between scanner B (**left**) and scanner C (**right**). Arrows in blue indicate the overlap region of the artifact for different inserts in scanner B. Arrows in red indicate the overlap region of the artifact for different inserts in scanner C. Window width/window level for the image of scanner B: 1970/1165; window width/window level for the image of scanner C: 2208/1104. features were extracted for each insert considering a fixed VOI volume (6 cm\({}^{3}\)). The authors report that texture features that are able to differentiate phantom textures were significantly influenced by field strength, in line with our findings. However, they did not report whether the number of discriminative features was higher in the case of 1.5T or 3T images, not allowing a straightforward comparison with our data. It must be highlighted, however, that to explain the different behavior of 1.5T and 3T scanners, not only the field strength has to be taken into account, but also the coil properties, which might be different between the two scanners, and the sequence parameters, which can be varied and optimized in different ways. For example, [PERSON] et al. [13] found comparable texture misclassification with 1.5T images and 3T images acquired with a high temporal resolution and low spatial resolution sequence, whereas misclassification at 3T was notably reduced when using a high spatial resolution sequence. In our study, 3T images were acquired with the same acquisition parameters used on the 1.5T scanner from the same vendor. In general terms, these data are a first attempt to quantify to what extent the ability of radiomic features to discriminate different textures depends on the VOI size, the textures to be discriminated, the magnetic field strength, the radiomic feature, and the image filter type, in case of T\({}_{2}\)-weighted imaging of pelvic region. Despite not being directly generalizable to every clinical scenario, they are important for offering the first guide in the absence of more adequate data and, most importantly, for setting the basis and methodology for further investigation. To allow other researchers to adapt this analysis to their clinical scenario, the source code and data files used in this study are publicly available in the Github repository: [[https://github.com/ReliabilityRadiomicsIEOFC/Radiomics_Size_Dependency](https://github.com/ReliabilityRadiomicsIEOFC/Radiomics_Size_Dependency)]([https://github.com/ReliabilityRadiomicsIEOFC/Radiomics_Size_Dependency](https://github.com/ReliabilityRadiomicsIEOFC/Radiomics_Size_Dependency)), created on 7 March 2022. Aiming at a clinical application of radiomics, especially in the case of the classical radiomic approach with the calculation of handcrafted radiomic features, it should be of paramount importance to assess these dependencies before designing a new radiomic study, along with other methodological investigations such as radiomic feature repeatability and reproducibility upon image acquisition and reconstruction parameters.