## 4 Discussion and Conclusions  
The aim of this work was to acquire further insights into the dependence of radiomic features' robustness on the quantization level used during feature extraction, and the reproducibility of features in relation to segmented ROIs of breast masses. In particular, first of all, in order to quantify the robustness and to provide a score--for each radiomic feature and for each category (i.e., GLCM, GLRLM, GLSZM, GLDM, NGTDM)--a correlation analysis based on the ICC was carried out to identify the features that are the least dependent on the level of quantization (i.e., the 'binCount' parameter). After determining the best value for 'binCount' and considering the subset containing only the robust features, an additional analysis was carried out in order to assess the reproducibility by comparing, relying upon the ICC, three different segmentations: two that were independently obtained through manual delineation by two radiologists, and one that was obtained using a validated automated segmentation approach [21, 33] based on spatial FCM clustering. This second analysis led to the quantification of not only a robustness index for each feature, but also to the definition and quantification of an overall robustness index for each category of features. Among the GLCM features, _Contrast_, _DifferenceAverage_, _DifferenceVariance_, _Imc1_, _Imc2_, and _MaximumProbability_ seem to have a poor robustness, related to the segmentation method. Even if, in the comparison of manual1 vs. manual2, their robustness index is good (surely due to a higher accordance between the two manual segmentations), in the comparison with the automated method, the _Imc1_ is lower. This denotes a high dependence on the ROI. The category has a medium-to-high overall robustness index (0.48-0.8). Among the GLRLM features, _LongRunHighGrayLevelEmphasis_ obtains the worst score in the comparison between automated and manual segmentations, followed by _GrayLevelNonUniformity_, _RunLengthNonUniformity_, and _RunVariance_. On the other hand, the other features present a very good score in all comparisons, and the overall category score is high (0.6-0.8). Nearly all GLSZM features have a medium/low index that brings the category a very low overall index, varying in the range 0.36-0.53, depending on the segmentation. GLDM almost all have a medium other score, except for _LargeDependentHighGrayLevelEmphasis_ and _SmallDependenceHighGrayLevelEmphasis_, showing an overall score in the range 0.42-0.67. Finally, all features belonging to the NGTDM category show very low robustness indexes and, consequently, the category obtains an overall score in the range 0.16-0.4. Considering the dependence on the quantization level, taking into account only the _intersection subset_ among all the values of 'binCount' could be the best selection strategy. Among radiomic categories, GLCM, GLRLM, and GLDM showed the best overall robustness against variations due to the segmentation method. A fair comparison against analogous literature works is not possible, because each one does not necessarily refer to the same disease and, consequently, does not use the same \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Auto vs. Man1** & **Auto vs. Man2** & **Man1 vs. Man2** \\ \hline GLCM & 0.48 & 0.45 & 0.8 \\ GLRLM & 0.6 & 0.63 & 0.8 \\ GLSZM & 0.42 & 0.36 & 0.53 \\ GLDM & 0.42 & 0.42 & 0.67 \\ NGTDM & 0.36 & 0.16 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall feature category robustness across the three segmentation raters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Auto vs. Man1** & **Auto vs. Man2** & **Man1 vs. Man2** \\ \hline GLCM & 50\% & 50\% & 100\% \\ GLRLM & 50\% & 50\% & 100\% \\ GLSZM & 33.3\% & 0\% & 44.4\% \\ GLDM & 33.3\% & 33.3\% & 100\% \\ NGTDM & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of features with robustness index \(\geq\) 3.  
data.