## 2 Materials and Methods
### Image Processing and Feature Extraction  
All image studies were processed using our in-house tool Cardiology Software Suite (in-house version) for Image Analysis (CASSIA) [25]. A thorough description of the image processing methodology is detailed in [25]. However, a few modifications are incorporated for the processing of NAC images. Briefly, the co-registration of NAC PET and corresponding CT images is performed, followed by the adequate segmentation of the heart valve metabolic activity to analyze the metabolic patterns along the valve regions. The segmentation of the valve is performed by defining a toroidal volume of interest (VOI) that isolates the metabolically active region of the prosthetic valve ring.  
Once the valvular VOI is segmented, radiomic features are extracted. The adequate computation of radiomic features may require preprocessing of the segmented VOI. According to IBSI recommendations [38], the discretization of image gray values as well as interpolation to achieve an isotropic volume must be performed prior to feature extraction. In compliance with IBSI indications, the fixed bin number (FBN) discretization algorithm is the preferred method for arbitrary units as those in the NAC [18]FFDG PET images (no re-segmentation range may be defined) [38].  
In addition, some discretization and interpolation parameters such as the bin number or voxel resampling size may alter the reproducibility of the radiomic features. To this purpose, this study assessed the reproducibility of the extracted radiomic indices for the variations in these extraction parameters, in terms of the intraclass correlation coefficient (ICC), as a methodological step prior to statistical analysis for the identification of potential image-based biomarkers. The bin number (4, 8, 16, 32, and 64 bins) and voxel size (\(1\times 1\times 1\) mm\({}^{3}\), \(1\times 1\times 2\) mm\({}^{3}\), \(1\times 1\times 3\) mm\({}^{3}\), and \(1\times 1\times 4\) mm\({}^{3}\)) configurations were defined in compliance with the Image Biomarker Standardization Initiative (IBSI) criteria [38] and image acquisition settings of the study population. An up-sampling approach is preferred to minimize image aliasing artifacts [38]. As stated in previous work [39], an ICC(3,1) for a two-way mixed-effects model was computed to quantify the reproducibility of the radiomic features related to variations in the bin number (Experiment 1) and voxel size (Experiment 2) parameters. A threshold of 0.82 (computed as the mean endpoint between intervals defined as good [ICC 0.75-0.9] or excellent [ICC \(>\) 0.9] feature performance [40; 41; 42]) for ICC was used to group radiomic features into sets of features showing reproducible and non-reproducible values in response to variations in the feature calculation parameter configurations.  
After the reproducibility analysis, the optimal feature extraction settings of parameters are defined and radiomics data are obtained according to the selected robust extraction parameters configuration.  
Finally, radiomic features were extracted from the defined volume of interest encompassing the valvular region in the original non-filtered NAC [18]FFDG PET images using the open-source python package Pyradiomics [43] with the default extraction parameters, unless otherwise stated above. A total of 93 features were obtained that are categorized as follows: 18 First Order Statistics, 24 Gray Level Co-occurrence Matrix (GLCM), 16 Gray Level Run Length Matrix (GLRLM), 16 Gray Level Size Zone Matrix (GLSZM), 5 Neighboring Gray Tone Difference Matrix (NGTDM), and 14 Gray Level Dependence Matrix (GLDM).