## 3 Results  
According to the results of the reproducibility analysis (Figures 1 and 2), 95.7% (89 out of 93) of the features remain consistent when varying the voxel size (median ICC values were 0.945 [IQR 0.764-0.997] and 0.987 [IQR 0.948-0.998] for absolute agreement and consistency, respectively) whether only 45.2% (42 out of 93) of the features remain consistent for variations in the discretization bin number (median ICC values were 0.250 [IQR 0.116-0.512] and 0.793 [IQR 0.459-0.984] for absolute agreement and consistency, respectively). In compliance with IBSI recommendations and given the non-dependence relationship of the radiomic features and voxel size configuration, an isotropic volume resampling strategy with a 1.0 \(\times\) 1.0 \(\times\) 1.0 mm\({}^{3}\) voxel size was employed. Conversely, some further discrepancies could be noted within the radiomic features' values when altering the discretization bin number parameter. Therefore, the discretization bin number was set to 64 bins in compliance with IBSI standards [38].  
The statistically significant differences between the diagnostic PVE groups in the radiomic predictors which also maintain an ICC above the established threshold for consistency (ICC > 0.82) are shown in Table 1. Notably, highly correlated features (\(\rho\geq 0.9\)) were also discarded to mitigate multicollinearity issues (see Supplemen  
Figure 1: Absolute agreement (**a**) and consistency (**b**) ICC values of the radiomic features for the variation of the bin number (Experiment 1) and voxel size (Experiment 2) configurations.  
Figure 2: Consistency ICC values of the radiomic features groups for (**a**) the variation of the bin number (Experiment 1) and (**b**) the variation in voxel size (Experiment 2) configurations.  
These features were acquired using the FBN method (64 bins) for gray level discretization and a voxel size of 1.0 \(\times\) 1.0 \(\times\) 1.0 mm\({}^{3}\). The statistically significant differences between PVE groups were described in a total of 2 GLRLM, 1 first order, 1 GLRM, and 1 NGTDM features. The differences between the subgroups are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Feature** & **Discarded PVE** & **Confirmed PVE** & \(p\)**-Value** & **ICC [95\% CI]** \\ \hline First order & Entropy & \(5.00\pm 0.07\) & \(5.39\pm 0.03\) & 0.003 * & 0.999 [0.999-1.000] \\ \hline GLDM & DependenceEntropy & \(8.18\pm 0.02\) & \(8.49\pm 0.02\) & 0.003 * & 0.873 [0.767-0.942] \\ \hline \multirow{2}{*}{GLRLM} & GrayLevelNonUniformity & \(648.90\pm 0.29\) & \(452.78\pm 0.38\) & 0.025 & 0.910 [0.835-0.959] \\ \cline{2-6}  & RunEntropy & \(5.80\pm 0.02\) & \(6.69\pm 0.03\) & 0.003 * & 0.892 [0.802-0.951] \\ \hline NGTDM & Coarseness & \(0.0014\pm 0.001\) & 0.002 \(\pm\) 0.35 & 0.028 & 0.962 [0.930-0.988] \\ \hline \end{tabular}
* Significant after Benjamini Hochberg multiple testing correction. PVE: prosthetic valve endocardiis; CI: confidence interval.  
\end{table}
Table 1: Radiomic features with statistically significant differences between discarded and confirmed PVE.  
Figure 3: Boxplots of the statistically significant radiomic features after Benjamini Hochberg multiple testing correction. Log values are calculated to show the differences between the diagnostic groups (Discarded PVE vs. Confirmed PVE). **(a)** First order Entropy; **(b)** GLDM Dependence Entropy; **(c)** GLRLM RunEntropy.  
ML models based on radiomic features with significant differences between confirmed and discarded PVE in the Mann-Whitney U test are evaluated (Table 2). Logistic regression showed an AUC of 0.876 with an overall sensitivity (recall) and specificity of 0.864 and 0.864, demonstrating a potential great capability of PVE classification. Similar performances were obtained for models based on all radiomic variables (Supplementary Materials Table S3).