BackgroundIn the last years, 18 F-fluorodeoxyglucose PET/computed tomography ( 18 F-FDG PET/CT) has demonstrated its utility for the evaluation of immunoglobulin G4 (IgG4)-related disease (IgG4RD). The studies are, however, really heterogeneous and different. The aim of this review is, therefore, to analyze the diagnostic performance of 18 F-FDG PET and PET/CT for the assessment of IgG4RD.Methods A wide literature search of the PubMed/ MEDLINE, Scopus, Embase and Cochrane library databases was made to find relevant published articles about the diagnostic performance of 18 F-FDG PET or PET/ CT for the evaluation of IgG4RD. ResultsThe comprehensive computer literature search revealed 779 articles. On reviewing the titles and abstracts, 756 articles were excluded because the reported data were not within the field of interest. Twenty-three studies were included in the review. ConclusionDespite some limitations that affect our review, 18 F-FDG PET or PET/CT demonstrated the ability to assess IgG4RD both at initial evaluation and after therapy. In general, no correlation between PET/CT parameters and IgG4 serum levels has been reported. A possible role for 18 F-FDG PET/CT to drive differential diagnosis with other disease is starting to emerge. Nucl

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The clinical and prognostic role of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) in the study of patients affected by differentiated thyroid carcinoma (DTC) with positive serum thyroglobulin (Tg) level and negative [131I] whole-body scan ([131I]WBS) has already been demonstrated. However, the potential prognostic role of semi-quantitative PET metabolic volume features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), has not yet been clearly investigated. The aim of this retrospective study was to investigate whether the main metabolic PET/CT parameters may predict the prognosis. We retrospectively included 122 patients with a positive 2-[18F]FDG PET/CT for DTC disease after a negative [131I]WBS with Tg > 10 ng/mL. The maximum and mean standardized uptake value (SUVmax and SUVmean), MTV and TLG of the hypermetabolic lesion, total MTV (tMTV) and total TLG (tTLG) were measured for each scan. Progression-free survival (PFS) and overall survival (OS) curves were plotted according to the Kaplan–Meier analysis. After a median follow up of 53 months, relapse/progression of disease occurred in 87 patients and death in 42. The median PFS and OS were 19 months (range 1–132 months) and 46 months (range 1–145 months). tMTV and tTLG were the only independent prognostic factors for OS. No variables were significantly correlated with PFS. The best thresholds derived in our sample were 6.6 cm3 for MTV and 119.4 for TLG. In patients with negative WBS and Tg > 10 ng/mL, 2-[18F]FDG PET/CT metabolic volume parameters (tMTV and tTLG) may help to predict OS.

The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role of radiomics features (RaF) and machine learning (ML) in the prediction of the histological classification of stage I and II non-small-cell lung cancer (NSCLC) at baseline [18F]FDG PET/CT. A total of 227 patients were retrospectively included and, after volumetric segmentation, RaF were extracted. All of the features were tested for significant differences between the two scanners and considering both the scanners together, and their performances in predicting the histology of NSCLC were analyzed by testing of different ML approaches: Logistic Regressor (LR), k-Nearest Neighbors (kNN), Decision Tree (DT) and Random Forest (RF). In general, the models with best performances for all the scanners were kNN and LR and moreover the kNN model had better performances compared to the other. The impact of the PET/CT scanner used for the acquisition of the scans on the performances of RaF was evident: mean area under the curve (AUC) values for scanner 2 were lower compared to scanner 1 and both the scanner considered together. In conclusion, our study enabled the selection of some [18F]FDG PET/CT RaF and ML models that are able to predict with good performances the histological subtype of NSCLC. Furthermore, the type of PET/CT scanner may influence these performances.