F-FDG PET/CT and radiolabeled leucocyte scintigraphy single-photon emission computed tomography carry high performance in the diagnostic of LVAD infections. F-FDG PET/CT shows significantly higher sensitivity and could be proposed as first-line nuclear medicine procedure.

(18)F-FDG PET-CT is helpful in the work-up of suspected CIED infections. It is a potential tool to make the accurate diagnosis of CIED infection and to assess the extent of infection. The promising results in this indication need to be validated in a prospective multicenter study.

This study emphasizes the potential utility of FDG PET/CT scanning as a diagnostic tool for septic emboli in patients with pacing lead endocarditis. This promising diagnostic tool may be integrated in the diagnostic algorithm of patients with lead endocarditis because diagnosis of septic embolisms has a direct and significant impact on the therapeutic care pathway.

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In Multiple Myeloma (MM) patients, diffuse infiltration of bone marrow can be diagnosed on MRI and is associated with poorer prognosis. On 18-FDG PET/CT, the other important imaging modality in MM, the diagnosis of diffuse disease by visual analysis can be challenging.Radiomics allows the extraction of large amount of data from images to individualize disease specific diagnostic or prognostic patterns. We aimed to develop a radiomics-based model derived from PET and CT images, that could improve the diagnosis of multiple myeloma diffuse disease on 18-FDG PET/CT. MethodsWe prospectively performed PET/CT and Whole-Body MRI in 30 MM patients at initial diagnosis. MRI was the standard of reference for diffuse disease assessment. Twenty patients were assigned to the training set and 10 to the independent test set. Visual analysis of PET/CT was done by two nuclear medicine physicians separately, then by consensual reading.Spine volumes on CT and PET were automatically segmented and a total of 174 IBSI-compliant radiomics features from both volumes were extracted. Selection of best features in the training set was performed with Random Forest features importance combined with correlation analysis.Machine-learning algorithms were then trained on the selected features with cross-validation.Finally, the model was evaluated on an independent test set. ResultsOut of the 30 patients, 18 had established diffuse disease on MRI. After consensus of PET/CT images, the sensitivity, specificity and accuracy of visual analysis was 67%, 75% and 70%, respectively, with a moderate kappa coefficient of agreement of 0.6. Five radiomics features were selected. On the training set, Random Forest classifier reached the highest mean accuracy of 0.91 (95% CI 0.90-0.92) with an AUC of 0.90 (95% CI 0.89-0.91) for diffuse disease diagnosis. On the independent test set, the model achieved an accuracy of 80%.Conclusions Radiomics analysis of 18-FDG PET/CT images with machine-learning overcame the limitations of visual analysis, providing a highly accurate and more reliable diagnosis of diffuse bone marrow infiltration in multiple myeloma patients.