Prostate-specific membrane antigen-PET/CT may result in stage migration in prostate cancer: performances, quantitative analysis, and potential criticism in the clinical practice.

AIM: The early detection of prostate cancer (PCa) metastatic disease with PET imaging leads to stage migration and change of disease management. We aimed to assess the impact on clinical management deriving from prostate-specific membrane antigen (PSMA) imaging with a digital PET/CT during the routine application in the staging and restaging process of PCa. MATERIAL AND METHODS: Eighty consecutive PCa patients underwent 18F-PSMA-1007. Digital PET/CT were retrospectively evaluated and discussed with oncologists to evaluate the impact on clinical management. Performances analysis, correlation among variables also considering semiquantitative parameters have been conducted. RESULTS: In the whole group of 80 patients at staging (N = 31) and restaging (N = 49), the detection rate of PSMA PET was 85% for all lesions. At staging, the performance analysis resulted in sensitivity 77.6%, specificity 89.5%, negative predictive value (NPV) 77.6%, positive predictive value (PPV) 89.5%, accuracy 85.7%, and area under curve (AUC) 0.87%. The performance of restaging PET in the group of patients with PSA values <1 ng/ml resulted in the following values: sensitivity 66.7%, specificity 92.9%, NPV 85.7%, PPV 81.3%, accuracy 82.6%, and AUC 0.79. Semiquantitative analysis revealed a mean value of SUVmax, metabolic tumor volume, and total lesion PSMA expression with differences in patients with high risk compared to low intermediate. At restaging PET, semiquantitative values of patients with total prostate specific antigen (tPSA) ≤ 1 ng/ml were significantly less than those of the tPSA > 1 ng/ml. A significant impact on clinical management was reported in 46/80 patients (57.5%) based on PSMA PET findings at staging and restaging. CONCLUSION: Although PSMA-PET provides optimal performances, its current role in redefining a better staging should be translated in the current clinical scenario about potential improvement in clinical/survival outcomes.

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model.

BACKGROUND/AIM: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. MATERIALS AND METHODS: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401-610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. RESULTS: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients' dataset (Siemens and GE scans). CONCLUSIONS: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.

Subcutaneous Uptake on [18F]Florbetaben PET/CT: a Case Report of Possible Amyloid-Beta Immune-Reactivity After COVID-19 Vaccination.

Introduction: Large-scale worldwide COVID-19 vaccination programs are being rapidly deployed, and high-risk patients with comorbidity are now receiving the first doses of the vaccine. Physicians should be, therefore, aware of new pitfalls associated with the current pandemic vaccination program, also in the case of [18F]Florbetaben PET/CT.Case PresentationWe described the first image of [18F]Florbetaben PET/CT in the evaluation of a 70-year-old male with suspicious Alzheimer disease and unclear history of heart disease. We detailed the diagnostic imaging PET/CT workup with different findings. Conclusion: In this case, [18F]Florbetaben PET/CT can demonstrate potential beta-amyloid immune-reactivity and deposition associated with the current COVID-19 pandemic vaccination programs.

Mutational screening of VSX1, SPARC, SOD1, LOX, and TIMP3 in keratoconus.

PURPOSE: To evaluate the involvement of Visual System Homeobox 1 (VSX1), Secreted Protein Acidic and Rich in Cysteine (SPARC), Superoxide Dismutase 1 (SOD1), Lysyl Oxidase (LOX), and Tissue Inhibitor of Metalloproteinase 3 (TIMP3) in sporadic and familial keratoconus. METHODS: Mutational analysis of the five genes was performed by sequencing and fragment analysis in a large cohort of 302 Italian patients, with a diagnosis of keratoconus based on clinical examination and corneal topography. The variants identified in VSX1 and SPARC were also assessed in the available relatives of the probands. RESULTS: A novel mutation p.G239R and previously reported mutations were found in VSX1. Novel and already reported variants were identified in SPARC and SOD1, whose pathogenic significance has not been established. No pathogenic variants have been identified in LOX and TIMP3. CONCLUSIONS: Molecular analysis of the five genes in a cohort of 225 sporadic and 77 familial keratoconus cases confirms the possible pathogenic role of VSX1 though in a small number of patients; a possible involvement of LOX and TIMP3 could be excluded; and the role played by SOD1 and SPARC in determining the disease as not been definitively clarified. Further studies are required to identify other important genetic factors involved in the pathogenesis and progression of the disease that in the authors' opinion, and according with several authors, should be considered as a complex disease.