## 1 Introduction  
1 Discrimination of Tumor Texture Based on MRI Radionic Features: Is There a Volume Threshold? A Phantom Study  
1.1 Discrimination of Tumor Texture Based on MRI Radionic Features: Is There a Volume Threshold? A Phantom Study  
[PERSON] 1,2,4,6, [PERSON] 3,7, [PERSON] 2,4, [PERSON] 1,5, [PERSON] 3, [PERSON] 1,6,7, [PERSON] 7, [PERSON] 8,9, [PERSON] 10,4,8 and [PERSON] 10  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 3: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 4: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 5: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 6: [[https://www.mdpi](https://www.mdpi)]([https://www.mdpi](https://www.mdpi))  
for first-order and GLSZM classes on the second 1.5T scanner. Poorer results in terms of features' discriminative ability were found for the 3T scanner. Focusing on original images only, the analysis of discriminative features stratified by feature class showed that the first-order and GLCM were robust to VOI size variations (>85% discriminative features for all sizes), while for the 1.5T scanners, the GLSZM and NGTDM feature classes showed a percentage of discriminative features >80% only for volumes no smaller than 3.3 cm\({}^{3}\), and equal or larger than 7.4 cm\({}^{3}\) for the GLRLM. As for the 3T scanner, only the GLSZM showed a percentage of discriminative features >80% for all volume sizes above 3.3 cm\({}^{3}\). Analogous considerations were obtained for each filter, providing useful indications for feature selection in this clinical case. Similar studies should be replicated with suitably adapted phantoms to derive useful data for other clinical scenarios and imaging modalities. radiomics; MRI; texture analysis; volume dependency +
Footnote †: journal: Scientific Journal  
## 1 Introduction  
Radiomics is the practice of converting standard-of-care medical images into minable high-dimensional data (hereinafter referred to as _radiomic features_) in order to support clinical decisions [1]. It is based on the hypothesis that quantitative imaging of the tumor anatomy can yield information on its functional and progression mechanisms by investigating the spatial variations in the tumoral tissues and their surroundings [2; 3] through texture analysis. As a matter of fact, when a tumor originates, many complex mechanisms occur, at the cellular level, possibly resulting in intra-tumoral heterogeneity, which might be captured by medical images. Tumors with higher heterogeneity are thought to be associated with a worse prognosis [1]. Most radiomic features were initially developed to characterize two-dimensional rectangular aerial photographs, which shared the same pixel dimensions and therefore did not require normalization to differences in the size of the region of interest [4]. In medical radiomics, these descriptors are extracted from volumes-of-interest (VOIs) encompassing the tissue under investigation, typically a tumor, which will vary in terms of area and volume. Several studies assessed the dependency of some radiomic features with volume and suggested corrections to remove such dependencies [4; 5]. Identifying the dependencies of radiomic features with the size of the VOI is of major importance for their use as imaging biomarkers, as it allows the disentanglement of the texture component from the size component and, therefore, improves reliability [5].