## 4 Discussion
The differences between studies with confirmed and discarded PVE show a trend of increased entropy in studies with confirmed PVE with respect to studies with discarded PVE (Figure 3). According to the definition provided by the IBSI standard [38], entropy refers to the uncertainty/randomness of the radiomic values and, therefore, to the tissue uptake heterogeneity. That is, a higher entropy could be explained by a greater dispersion of uptake values in the studied region, whereas a reduced entropy would mean a greater uptake homogeneity. This finding is therefore in line with the results described in previous studies, where a homogeneous uptake pattern was previously related to reactive inflammatory activity and not to infection [3; 8] whereas a heterogeneous uptake pattern with multiple foci is related to an infectious process. Previous studies have also demonstrated the usefulness of [18]FFDG PET/CT in the assessment of patients with suspected PVIE [2; 10; 11; 12; 13; 14]. However semi-quantitative metrics and SUV ratios are commonly assessed which may miss valuable insights into the patterns of homogeneity/heterogeneity in valvular uptake related to PVE diagnosis. Reproducibility and univariate statistical analyses allowed to define statistically significant radiomic potential predictors for PVE classification. Subsequently, a multivariate analysis of radiomic features based on the evaluation of different ML models was performed. In accordance with the outlined criteria for translating radiomics into clinically useful tests, as detailed in previous research [45], the present study thoroughly revisited these criteria in the study design. Moreover, the CLEAR checklist for radiomics research [46] has been completed (see Supplementary files). Nonetheless, limitations were encountered, and were primarily attributed to the restricted power sample size and challenges in fulfilling the criteria associated with model development. Therefore, given the limited patient cohort and limited statistical power, only an internal validation of these models was aimed within a proof-of-concept framework, without the explicit objective of validating a clinically feasible model. Therefore, this proof-of-concept multivariate analysis aims to conduct an initial assessment of the potential value of NAC [18]FFDG PET imaging in improving the accuracy of PVE diagnosis. The ML models evaluated were constructed using the radiomic biomarkers selected in the reproducibility and univariate analyses. The logistic regression model showed the best performance within the internal validation, reaching an AUC of 0.876, with a sensitivity of 0.864 and specificity of 0.864 (Table 2). The ML models show an acceptable performance for the detection of PVE. However, all the models demonstrated comparable performance outcomes, likely due to the restricted internal validation conducted with a limited patient cohort. Further investigations with an enlarged patient cohort will be necessary to enhance model disparities and assess their potential clinical relevance and context-specific adequacy. The study by [PERSON] et al. [34] evaluated the potential of radiomic analysis on AC [18]FFDG PET/CT imaging for the diagnosis of IE through predictive models for the risk stratification of patients. Clinical data, imaging data, and classification according to Duke criteria were collected in a total of 447 patients. Models that integrated further information (image-based information and clinical data) achieved higher performance (logistic regression model reached an AUC of 0.91). They established that multivariable models based on radiomics and clinical information had a positive but still limited contribution to the diagnosis of IE and stratification of patients into risk profiles. In the study by [PERSON] et al. [35], the usefulness of ML models based on radiomic features for the diagnosis of PVE in AC [18]FDG PET/CT imaging was evaluated. Clinical and imaging data were collected from a total of 108 patients.