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AimIn recent years, the clinical availability of scanners for integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) has enabled the practical potential of multimodal, combined metabolic-receptor, anatomical, and functional imaging to be explored. The present systematic review and meta-analysis summarize the diagnostic information provided by PET/MRI in patients with prostate cancer (PCa).Materials and methodsA literature search was conducted in three different databases. The terms used were “choline” or “prostate-specific membrane antigen - PSMA” AND “prostate cancer” or “prostate” AND “PET/MRI” or “PET MRI” or “PET-MRI” or “positron emission tomography/magnetic resonance imaging.” All relevant records identified were combined, and the full texts were retrieved. Reports were excluded if (1) they did not consider hybrid PET/MRI; or (2) the sample size was < 10 patients; or (3) the raw data were not enough to enable the completion of a 2 × 2 contingency table.ResultsFifty articles were eligible for systematic review, and 23 for meta-analysis. The pooled data concerned 2104 patients. Initial disease staging was the main indication for PET/MRI in 24 studies. Radiolabeled PSMA was the tracer most frequently used. In primary tumors, the pooled sensitivity for the patient-based analysis was 94.9%. At restaging, the pooled detection rate was 80.9% and was higher for radiolabeled PSMA than for choline (81.8% and 77.3%, respectively).ConclusionsPET/MRI proved highly sensitive in detecting primary PCa, with a high detection rate for recurrent disease, particularly when radiolabeled PSMA was used.

The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [ 18 F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [ 18 F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [ 18 F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.

•18F-FDG PET SUVR is moderately related to fMRI FC graph metrics across brain regions.•The SUVR-STR coupling is stronger and more linear for FC hubs.•Connector hubs have higher SUVR than provincial hubs.•There is a link between network integration and energy consumption. The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes’ resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

Gliomas alter brain function and integrity, but these disruptions are often studied separately. This study utilised a novel approach that integrated functional, structural and microstructural connectivity information to investigate glioma-induced brain network changes and their clinical implications. It focused on the impact of gliomas on key brain networks, with a particular emphasis on the relationship between tumour topology and its effect on homotopic areal-level parcellation. The investigation was grounded in a unique clinical dataset comprising functional and diffusion images of forty-one newly diagnosed glioma patients. Connectivity matrices (functional, structural, and microstructural) were generated using homotopic parcellations and combined into an integration connectivity matrix. A linear regression model compared patient data to pseudo-healthy references. This identified affected regions as those falling in the left tail of the distribution across patients and parcellations. The study revealed that lateralized gliomas affect networks in both hemispheres, with left hemisphere lesions primarily altering homotopic homolateral and contralateral networks in healthy tissues. Abnormalities were more easily detected in regions distant from the lesion using functional connectivity rather than structural measures. The approach highlighted the heterogeneity of functional and structural alterations and emphasised that a comprehensive understanding of glioma abnormalities requires integrating multiple connectivity modalities.

Resting state fMRI has been used in many studies to investigate the impact of brain tumours on functional connectivity (FC). However, these studies have so far assumed that FC is stationary, disregarding the fact that the brain fluctuates over dynamic states. Here we utilised resting state fMRI data from 33 patients with high-grade gliomas and 33 healthy controls to examine the dynamic interplay between resting-state networks and to gain insights into the impact of brain tumours on functional dynamics. By employing Hidden Markov Models, we demonstrated that functional dynamics persist even in the presence of a high-grade glioma, and that patients exhibited a global decrease of connections strength, as well as of network segregation. Furthermore, through a multivariate analysis, we demonstrated that patients’ cognitive scores are highly predictive of pathological dynamics, thus supporting our hypothesis that functional dynamics could serve as valuable biomarkers for better understanding the traits of high-grade gliomas.

•Functional gradients can capture signal dispersion in the Default Mode Network (DMN).•Functional gradients are predictive of memory abilities in longitudinal settings.•Amyloid status influences the association between gradients dispersion and memory.•Temporal cortices are the only DMN structures showing opposite alteration patterns.•Functional alterations predict future structural damage and tau accumulation. Functional gradients offer a novel, synthetic and interpretable view of functional connectivity. By integrating gradients, graph theory, morpho-volumetric indices, amyloid status, and regional tau burden, we aim to understand how cognitive decline relates to functional and structural brain changes. We analyzed 279 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with available functional, structural, and proteomic data at baseline and at 2-year follow-up, alongside detailed assessments of verbal memory abilities (encoding, retrieval and recall). Results at baseline revealed distinct patterns based on amyloid status and clinical severity. In individuals with positive amyloid status, increased functional dispersion of the Default Mode Network (DMN) correlated with poorer memory, while in individuals with negative status higher dispersion was associated with better performance. Notably, middle temporal regions exhibited connectivity profiles opposite to other DMN areas. We also observed that regions where gradients’ dispersion predicted impaired memory performance showed preserved structural integrity but had a more widespread connectivity profile. Baseline dispersion predicted future memory decline, showing consistent patterns across positive and negative amyloid groups—higher dispersion was linked to faster memory decline, except in tau-rich regions like the temporal cortex, where it was advantageous. Furthermore, higher dispersion was associated with future tau buildup and morphological alterations. This study extends a previous investigation and evaluates the utility of a multimodal framework in tracking disrupted DMN activity as an early marker of reduced coherence in communication among brain regions, possibly having a predictive value for future progression of cognitive decline.

Objectives To test the performance of a 3D convolutional neural network (CNN) in analysing brain [ 18 F]DOPA PET/CT in order to identify patients with nigro-striatal neurodegeneration. We evaluated the robustness of the 3D CNN by testing it against a manual regional analysis of the striata by using a striatal-to-occipital ratio (SOR). Methods We analyzed patients who had undergone [ 18 F]DOPA PET/CT from 2016 to 2018. Two examiners interpreted PET/CT images as positive or negative. Only patients with at least 2 years of follow-up and an ascertained neurological diagnosis were included. A 3D CNN was developed to evaluate [ 18 F]DOPA PET/CT and refine the diagnosis of movement disorder. This system required training and testing, which were carried out on 2/3 and 1/3 of patients, respectively. A regional analysis was also conducted by drawing region of interest on T1-weighted 3D MRI scans, on which the [ 18 F]DOPA PET images were first co-registered. Results Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5 th percentile of the negative cases’ distribution. Conclusions 3D CNNs are able to interpret [ 18 F]DOPA PET/CT properly, revealing patients affected by Parkinson’s disease. Key Points • [ 18 F]DOPA PET/CT is a sensitive diagnostic tool to identify patients with nigro-striatal neurodegeneration. • A semiquantitative evaluation of the images allows a more confident interpretation of the PET findings. • 3D convolutional neural network allows an accurate interpretation of 18F-DOPA PET/CT images, revealing patients affected by Parkinson’s disease.

Background Hyperparathyroidism-jaw tumor (HPT-JT) syndrome is a rare autosomal dominant disorder caused by CDC73 gene mutations, predisposing individuals to primary hyperparathyroidism (pHPT), cemento-ossifying fibromas, and other neoplastic conditions. [18F]Fluorocholine PET/CT has emerged as a tool for localizing hyperfunctioning parathyroid glands in pHPT, but its application in HPT-JT syndrome remains unreported. Case presentation We describe the case of a 15-year-old male presenting with severe hypercalcemia, increased PTH serum levels, and a history of cemento-ossifying fibroma removal. Standard imaging, including [99mTc]Tc-MIBI scintigraphy, was inconclusive. [18F]Fluorocholine PET/CT successfully identified a hyperfunctioning parathyroid gland, identified as parathyroid atypical adenoma at subsequent histology, and a recurrent maxillary cemento-ossifying fibroma. Genetic testing confirmed a CDC73 mutation, leading to the diagnosis of HPT-JT syndrome. Conclusions To our knowledge, this is the first reported case utilizing [18F]Fluorocholine PET/CT for the evaluation and management of HPT-JT syndrome with active presence of a maxillary cemento-ossifying fibroma. Given its superior sensitivity compared to conventional imaging, [18F]Fluorocholine PET/CT provided critical information for surgical planning and it might be a useful diagnostic tool for long-term disease monitoring. This case highlights the potential role of [18F]Fluorocholine PET/CT in detecting both parathyroid and jaw manifestations of HPT-JT syndrome, emphasizing the need for further research into its application in hereditary endocrine disorders.