## 4 Discussion  
Despite recent advances and the increased sensitivity and specificity of current diagnostic criteria, IE still accounts for high morbidity and mortality [4; 5; 6; 8]. [18]FDG PET/CT imaging has recently demonstrated its high value for the screening of IE [2; 6; 10; 11; 12; 13; 14; 15; 16; 17]. However, the false-positive rate remains limited in those cases where implantable devices or prosthetic valves are involved, which may produce artifacts in the non-attenuation corrected images [3; 8; 29; 37]. In fact, clinical guidelines recommend checking imaging findings also in NAC images in order to reduce the false-positive rate [3]. Likewise, the analysis of SUV-based metrics and uptake patterns based on the homogeneity/heterogeneity of radiopharmaceutical distribution has been shown to play a relevant role in the proper diagnosis of IE [2; 9; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Although these previous studies mainly propose SUV-based metrics to describe valvular uptake patterns, emerging evidence suggests that uptake heterogeneity through radiomic analysis may play a significant role in IE diagnosis. Nevertheless, despite the recent increasing applicability of radiomics in some clinical scenarios such as oncology [30; 31; 32; 33], its usage is still scarce and poorly extended in the case of IE. Nonetheless, some studies have recently demonstrated promising results for the early detection of IE [34; 35]. However, to the best of our knowledge, the present study is the first to evaluate metabolic uptake radiomic-based patterns in NAC [18]FDG PET images in the setting of prosthetic valve infective endocarditis (PVE) to support the current diagnostic criteria, following the clinical guidelines' recommendations for the diagnosis of PVE. In our study, a total of 22 imaging studies were evaluated, with a total of 93 imaging features per study. Additionally, a repeatability and reproducibility analysis of these radiomic features was evaluated on NAC [18]FDG PET/CT images. Reproducibility analysis showed that some radiomic features were consistent against changes in acquisition settings. Consistency ICCs above the defined 0.82 threshold demonstrated reproducibility against variations in the bin number and interpolation thickness, respectively. Moreover, a non-dependency relationship of radiomic values with respect to variation in interpolation thickness was evidenced. Furthermore, first order, GLCM, and GLRLM features demonstrated greater consistency against variations in the number of bins. Specifically, 94.4% (17 out of 18), 43.8% (7 out of 16), and 41.7% (10 out of 24) of the first order features, GLRLM, and GLCM remained consistent over variations in the number of bins. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **AUC** & **Accuracy** & **F1-Score** & **Precision** & **Recall** & **Specificity** \\ \hline Logistic Regression & 0.876 * & 0.864 * & 0.863 * & 0.867 * & 0.864 * & 0.864 * \\ \hline Neural Network & 0.876 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * \\ \hline SVM linear & 0.860 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * \\ \hline Decision tree & 0.769 * & 0.682 & 0.681 & 0.683 * & 0.682 & 0.682 \\ \hline Random Forest & 0.839 * & 0.818 * & 0.817 * & 0.829 * & 0.818 * & 0.818 * \\ \hline Naive Bayes & 0.855 * & 0.773 * & 0.768 * & 0.795 * & 0.773 * & 0.773 * \\ \hline Gradient Boosting & 0.851 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * \\ \hline kNN & 0.835 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * & 0.818 * \\ \hline \hline \end{tabular}
* Acceptable performance (>0.75).  
\end{table} 
Table 2: Performances of the internal validation for the ML models based on the selected radiomic features for Confirmed PVE and Discarded PVE classification. In the univariate analysis, five radiomic features showed statistically significant differences between the confirmed and discarded PVE (Table 1) in addition to good reproducibility of the results (ICC \(>\) 0.82).