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ObjectiveTo consolidate current understanding of detection sensitivity of brain 18F-FDG PET scans in the diagnosis of autoimmune encephalitis and to define specific metabolic imaging patterns for the most frequently occurring autoantibodies.MethodsA systematic and exhaustive search of data available in the literature was performed by querying the PubMed/MEDLINE and Cochrane databases for the search terms: ((PET) OR (positron emission tomography)) AND ((FDG) OR (fluorodeoxyglucose)) AND ((encephalitis) OR (brain inflammation)). Studies had to satisfy the following criteria: (i) include at least ten pediatric or adult patients suspected or diagnosed with autoimmune encephalitis according to the current recommendations, (ii) specifically present 18F-FDG PET and/or morphologic imaging findings. The diagnostic 18F-FDG PET detection sensitivity in autoimmune encephalitis was determined for all cases reported in this systematic review, according to a meta-analysis following the PRISMA method, and selected publication quality was assessed with the QUADAS-2 tool.ResultsThe search strategy identified 626 articles including references from publications. The detection sensitivity of 18F-FDG PET was 87% (80–92%) based on 21 publications and 444 patients included in the meta-analysis. We also report specific brain 18F-FDG PET imaging patterns for the main encephalitis autoantibody subtypes.Conclusion and relevanceBrain 18F-FDG PET has a high detection sensitivity and should be included in future diagnostic autoimmune encephalitis recommendations. Specific metabolic 18F-FDG PET patterns corresponding to the main autoimmune encephalitis autoantibody subtypes further enhance the value of this diagnostic.

PET plays an increasingly important role in the management of brain tumors. This review outlines currently available PET radiotracers and their respective indications. It specifically focuses on 18F-FDG, amino acid and somatostatin receptor radiotracers, for imaging gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in radiopharmaceuticals, image analyses and translational applications to therapy are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain MRI for all medical professionals implicated in brain tumor diagnosis and care.

This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[ 18 F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744–0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716–0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548–0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512–0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.

Differences in the performance of cadmium-zinc-telluride (CZT) cameras or collimation systems that have recently been commercialized for myocardial SPECT remain unclear. In the present study, the performance of 3 of these systems was compared by a comprehensive analysis of phantom and human SPECT images. We evaluated the Discovery NM 530c and DSPECT CZT cameras, as well as the Symbia Anger camera equipped with an astigmatic (IQ x SPECT) or parallel-hole (conventional SPECT) collimator. Physical performance was compared on reconstructed SPECT images from a phantom and from comparable groups of healthy subjects. Classifications were as follows, in order of performance. For count sensitivity on cardiac phantom images (counts x s(-1) x MBq(-1)), DSPECT had a sensitivity of 850; Discovery NM 530c, 460; IQ x SPECT, 390; and conventional SPECT, 130. This classification was similar to that of myocardial counts normalized to injected activities from human images (respective mean values, in counts x s(-1) x MBq(-1): 11.4 ± 2.6, 5.6 ± 1.4, 2.7 ± 0.7, and 0.6 ± 0.1). For central spatial resolution: Discovery NM 530c was 6.7 mm; DSPECT, 8.6 mm; IQ x SPECT, 15.0 mm; and conventional SPECT, 15.3 mm, also in accordance with the analysis of the sharpness of myocardial contours on human images (in cm(-1): 1.02 ± 0.17, 0.92 ± 0.11, 0.64 ± 0.12, and 0.65 ± 0.06, respectively). For contrast-to-noise ratio on the phantom: Discovery NM 530c had a ratio of 4.6; DSPECT, 4.1; IQ x SPECT, 3.9; and conventional SPECT, 3.5, similar to ratios documented on human images (5.2 ± 1.0, 4.5 ± 0.5, 3.9 ± 0.6, and 3.4 ± 0.3, respectively). The performance of CZT cameras is dramatically higher than that of Anger cameras, even for human SPECT images. However, CZT cameras differ in that spatial resolution and contrast-to-noise ratio are better with the Discovery NM 530c, whereas count sensitivity is markedly higher with the DSPECT.

Purpose Dopamine transporter (DAT) imaging is used to support the diagnosis of neurodegenerative parkinsonian disorders. Specific medications have been reported to confound the interpretation of [ 123 I]I-FP-CIT SPECT scans, but there is limited data. The aim of the current study is to identify potential medication effects on the interpretation of [ 123 I]I-FP-CIT SPECT scans in routine practice. Materials and methods Consecutive patients undergoing a [ 123 I]I-FP-CIT SPECT/CT scan on a 360° CZT camera between September 2019 and December 2022 were included. An exhaustive review of patient medications (antidepressants, antipsychotics, anti-epileptics, anti-parkinsonians, benzodiazepines, lithium, opioids, and stimulants) was performed. Two experienced nuclear physicians, blinded to the medication reports, interpreted the [ 123 I]I-FP-CIT SPECT scans visually and a semi-quantitative analysis was performed using a local normal database. Results The study included 305 patients (71.0 ± 10.4, 135 women) and 145 (47.5%) visually interpreted normal scans. In normal scans, the striatum/occiput radioligand uptake ratio was decreased by noradrenergic and specific serotonergic antidepressants (NASSAs) ( n  = 15, z-score of − 0.93) and opioid medication (tramadol, n  = 6, z-score of − 0.85) and was associated with a younger age in the multivariate analysis. In the overall population, the striatum/occiput ratio was influenced by NASSAs and associated with consensual visual analysis, age, sex, and anti-parkinsonian medications related to the status of the disease. Conclusion Our study confirms the potential impact of antidepressant (NASSA) and opioid (tramadol) medications on the semi-quantitative analysis of [ 123 I]I-FP-CIT SPECT scans. However, when performing a visual analysis, only NASSAs significantly impacted the interpretation of [ 123 I]I-FP-CIT SPECT scans.

Theranostics integrate molecular imaging and targeted radionuclide therapy for personalised cancer therapy. Theranostic treatments have shown meaningful efficacy in randomised clinical trials and are approved for clinical use in prostate cancer and neuroendocrine tumours. Brain tumours represent an unmet clinical need and theranostics might offer effective treatment options, although specific issues need to be considered for clinical development. In this Policy Review, we discuss opportunities and challenges of developing targeted radionuclide therapies for the treatment of brain tumours including glioma, meningioma, and brain metastasis. The rational choice of molecular treatment targets is highlighted, including the potential relevance of different types of targeted radionuclide therapeutics, and the role of the blood–brain barrier and blood–tumour barrier. Furthermore, we discuss considerations for effective clinical trial design and conduct, as well as logistical and regulatory challenges for implementation of radionuclide therapies into neuro-oncological practice. Rational development will foster successful translation of the theranostic concept to brain tumours.

BackgroundStatic [18F]-F-DOPA PET images are currently used for identifying patients with glioma recurrence/progression after treatment, although the additional diagnostic value of dynamic parameters remains unknown in this setting. The aim of this study was to evaluate the performances of static and dynamic [18F]-F-DOPA PET parameters for detecting patients with glioma recurrence/progression as well as assess further relationships with patient outcome.MethodsFifty-one consecutive patients who underwent an [18F]-F-DOPA PET for a suspected glioma recurrence/progression at post-resection MRI, were retrospectively included. Static parameters, including mean and maximum tumor-to-normal-brain (TBR) ratios, tumor-to-striatum (TSR) ratios, and metabolic tumor volume (MTV), as well as dynamic parameters with time-to-peak (TTP) values and curve slope, were tested for predicting the following: (1) glioma recurrence/progression at 6 months after the PET exam and (2) survival on longer follow-up.ResultsAll static parameters were significant predictors of glioma recurrence/progression (accuracy ≥ 94%) with all parameters also associated with mean progression-free survival (PFS) in the overall population (all p < 0.001, 29.7 vs. 0.4 months for TBRmax, TSRmax, and MTV). The curve slope was the sole dynamic PET predictor of glioma recurrence/progression (accuracy = 76.5%) and was also associated with mean PFS (p < 0.001, 18.0 vs. 0.4 months). However, no additional information was provided relative to static parameters in multivariate analysis.ConclusionAlthough patients with glioma recurrence/progression can be detected by both static and dynamic [18F]-F-DOPA PET parameters, most of this diagnostic information can be achieved by conventional static parameters.

Connectivity studies with nuclear medicine systems are scarce in literature. They mainly employ PET imaging and group level analyses due to the low temporal resolution of PET and especially SPECT imaging. Our current study analyses connectivity at an individual level using dynamic SPECT imaging, which has been enabled by the improved temporal resolution performances provided by the 360°CZT cameras. We present the case of an 80-year-old man referred for brain perfusion SPECT imaging for cognitive disorders for whom a dynamic SPECT acquisition was performed utilizing a 360°CZT camera (temporal sampling of 15 frames × 3 s, 10 frames × 15 s, 14 frames × 30 s), followed by a conventional static acquisition of 15 m. Functional SPECT connectivity (fSPECT) was assessed through a seed correlation analysis and 5 well-known resting-state networks were identified: the executive, the default mode, the sensory motor, the salience, and the visual networks. This case report supports the feasibility of fSPECT imaging to identify well known resting-state networks, thanks to the novel properties of a 360°CZT camera, and opens the way to the development of more dedicated functional connectivity studies using brain perfusion SPECT imaging.