## 4 Discussion
The SVM model achieved the best performance in the classification of IE reaching an AUC of 0.79 in the model that included only radiomic features and 0.82 in the model that integrated clinical and imaging information. They established that FDG PET/CT image-based machine learning algorithms achieved acceptable performance in terms of sensitivity and specificity for IE classification. As previously stated, the present study is a re-evaluation of the analysis performed in [25]. In the previous work, SUV-based ratios yielded patterns of heterogeneity in the valvular uptake of AC [18]FFDG PET/CT images with a performance of the valvular heterogeneity index (VHI) and ring-to-center ratio (RCR) reaching an AUC of 0.727 and 0.808, respectively. Moreover, univariate analysis demonstrated statistically significant differences between the confirmed and discarded PVE groups for the RCR index (\(p=0.02\)). On the other hand, the present study demonstrates that multivariate models based on radiomic features extracted from [18]FDG PET/CT NAC images lead to a higher performance in PVE classification (AUC = 0.876). Therefore, the preliminary findings from both univariate analysis and the multivariate models proposed in this work suggest the potential of NAC [18]FFDG PET imaging to enhance diagnostic sensitivity for IE, particularly in PVE. These results would set the basis for future validation studies that incorporate radiomic technology and ML models for the early detection of PVE. Thus, the radiomic-based biomarkers approach may provide a better diagnostic accuracy in describing valvular uptake patterns to characterize the specific pathophysiology of PVE. The inclusion of ML models in the current diagnostic criteria would potentially increase their sensitivity and specificity. Some limitations of the present study include the retrospective and single-center nature of the investigation which may limit the generalizability of findings. Moreover, the study did not control other imaging acquisition parameters, such as the scanner type, reconstruction method, or multiple-reader segmentation reproducibility, all of which are known to impact radiomic feature values. Furthermore, the study only performed an internal validation of the multivariate models, lacking an independent test for validation. For this purpose, an estimation of the power and sample size to avoid the underdetermination or classification of the parameters [47; 48] was additionally performed using G*power software version 3.1 ([[https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower](https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower)]([https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower](https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower)), accessed on 3 March 2024) [49]. For power and sample size estimation, the parameters recommended in previous studies were employed [48]. Considering a minimum desired statistical power of 0.8, a large effect size of 0.8, an alpha value of 0.05 for the Mann-Whitney U test, and a 1:1 ratio between groups, the sample estimate obtained was a minimum of 54 patients, with 27 patients per group, to avoid the underdetermination or classification of the parameters [47]. Nevertheless, the present study utilized a limited sample of 20 imaging studies (22 observations after applying SMOTE). Thus, conducting more extensive prospective studies with a larger pool of patients is of great importance. This approach will enhance the comprehension of the clinical significance of our findings, validate their reliability, and assess the potential of ML models based on radiomic features for classifying PVE.