Radiomic-Based Biomarkers for the Evaluation of Prosthetic Heart Valve Infective Endocarditis in Non-Attenuation Correction [18F]FDG PET/CT Images

Abstract

Although there have been crucial advancements in the diagnostic and treatment approaches, the mortality rate of infective endocarditis is still an ongoing challenge in clinical practice. [18F]FDG PET/CT imaging has recently proven its potential role in the early identification of prosthetic valve endocarditis (PVE). Due to radiomics’ rising applicability, recent studies exhibit promising outcomes in the clinical setting. The aim of the present study is the evaluation of potential radiomic-based biomarkers of non-attenuation-corrected (NAC) [18F]FDG PET images for the diagnosis of PVE. An adequate pre-processing and segmentation of the prosthetic ring metabolic activity were performed. A reproducibility analysis prior to the image-based biomarkers’ identification was conducted in terms of the intraclass correlation coefficient (ICC) derived from the variations in the radiomic extraction configurations (bin number and voxel size). After the reliability analysis, statistical analysis was performed by means of the Mann–Whitney U Test to study the differences between the PVE groups. Only p values < 0.05 after the Benjamini Hochberg correction procedure for multiple comparisons were considered statistically significant. Eight ML classification models for PVE classification based on radiomic features were evaluated. Overall, 45.2% and 95.7% of the radiomic features showed a consistency ICC above 0.82, demonstrating great reproducibility against variations in the bin number and interpolation thickness, respectively. Variations in interpolation thickness demonstrated great reproducibility in absolute agreement with 80.0% robust features, proving a non-dependency relationship with radiomic values. In the present study, the utility of potential radiomic-based biomarkers in the diagnosis of PVE in NAC [18F]FDG PET/CT images has been evaluated. Future studies will be required to validate the use of this technology as a valuable tool to support the current PVE diagnostic criteria.