FDG-PET Imaging Research Articles

Memories and mimics: unveiling the potential of FDG-PET in guiding therapeutic approaches for neurodegenerative cognitive disorders.

Fluorodeoxyglucose F18 (FDG) positron emission tomography (PET) imaging can help clinicians pursue the differential diagnosis of various neurodegenerative diseases. It has become an invaluable diagnostic tool in routine clinical practice in conjunction with computed tomography (CT) imaging, magnetic resonance imaging (MRI), and biomarker studies. We present a single-institution case series and systematic literature review, showing how FDG-PET imaging has helped physicians diagnose neurodegenerative diseases and their mimickers and how patient care was amended. A single institution analysis and comprehensive literature search were completed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. These medical subjects' headings (MeSH) terms were used: "FDG-PET" AND "dementia" OR "Alzheimer's" OR "neurodegeneration" OR "frontotemporal dementia" OR "atypical parkinsonian syndrome" OR "primary progressive aphasia" OR "lewy body dementia." The inclusion criteria included studies with uncertain diagnoses of neurocognitive disease resolved with FDG-PET, PET/MRI, or PET/CT hybrid imaging. A literature search resulted in 3,976 articles. After considering inclusion and exclusion criteria, 14 case reports and 1 case series were selected, representing 19 patients. The average age of patients was 70.8 years (range: 54-83 years). Five of the 19 patients were females. Dementia with Lewy bodies (DLB) had the highest propensity for being misidentified as another neurodegenerative disease, followed by Alzheimer's disease (AD) and frontotemporal dementia (FTD). Without accurate molecular imaging, neurodegenerative diseases may be missed or misdiagnosed. Our single-institution case series and literature review demonstrate how FDG-PET brain imaging can be used to correct and clarify preexisting clinical diagnoses of neurodegenerative disease.

Elucidation of FDG-PET Imaging Characteristics that Reflect the Heterogeneity within Tumors Due to Variability in Metabolic Activity: A Phantom Study

Abstract Objective Focusing on the heterogeneity within cancer lesions and revealing cancer lesions with a high signal-to-noise ratio will help improve the quality of positron emission tomography (PET) images. This study aimed to understand how glucose metabolic activity can be shown with less statistical noise using a quality assessment phantom modeled after cancer lesions with a two-layer structure and a Clear adaptive Low-noise Method (CaLM) filter. Materials and Methods A National Electrical Manufacturers Association phantom with two spheres with a two-layer structure filled with 2-deoxy-2[18F]fluoro-D-glucose (inner and outer diameters of the spheres were 13/22 and 22/37 mm, respectively; radioactivity ratio of the background [BG] to outer sphere layer was 1:4; and BG-to-inner sphere layer ratios were 1:1, 1:2, and 1:3) was evaluated. The acquisition time was set at 120 seconds, and imaging was repeated five times. The image data in photomultiplier tube (PMT)-PET were reconstructed using a time-of-flight (TOF) ordered subset expectation maximization (OSEM) algorithm (point spread function [PSF] − , iteration: 3, subset: 10) with a 4-mm Gaussian filter (GF) for normal images. Silicon photomultiplier (SiPM)-PET data were reconstructed using a TOF OSEM algorithm (PSF − , iteration: 2, subset: 12) and a 3-mm GF for normal images. The target images were reconstructed using three CaLM parameters (mild, standard, and strong). All the obtained images were investigated quantitatively, with calculation of maximum standard uptake value (SUVmax), coefficient of variation (CV), and contrast-to-noise ratio (CNR), after setting regions of interest on the lesions. Statistical analysis using the Dunnett's test compared normal images (control group) and target images (treatment group). Statistical significance was considered at p < 0.05. Results Quantitative assessment revealed that the SUVmax of target images (standard and strong) was equivalent to that of normal images in PMT-PET, with SUVmax of 3 to 3.5 for both layers. The SUVmax in SiPM-PET was similar across all CaLM types, ranging from 3 to 4 for all spheres. The target images (standard and strong) had a significantly reduced CV and improved CNR compared with normal images. Conclusion The boundary of the 22/37-mm spheres was visible with CaLM (strong) at radioactivity ratios of 1:4:1 and 1:4:2 on both scanners. For the 13/22-mm sphere boundary, visibility with CaLM (strong) was observed only with SiPM-PET, with the SUVmax equivalent to normal images. CaLM (strong) was deemed the optimal postprocessing filter for PMT-PET due to significant improvements in CV and CNR, while CaLM (standard) was suggested as the optimal filter for SiPM-PET due to excessive BG smoothing.

Optimisation of Animal Handing and Timing of 2-deoxy-2-[18F]fluoro-D-glucose PET Tumour Imaging in Mice.

In humans, 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) tumour-to-background contrast continues to increase long after a typical uptake period of 45 - 60min. Similar studies have not been performed in mice and the static imaging time point for most studies is arbitrarily set at 30 - 60min post-injection of [18F]FDG. Ideally, static PET imaging should be performed after the initial period of rapid uptake but this period has not been defined in mice, with previous dynamic studies in mice being limited to 60min. This study aimed to define the kinetics of [18F]FDG biodistribution over periods of 3 - 4h in different murine tumour models, both subcutaneous and autochthonous, and to further refine fasting and warming protocols used prior to imaging. Dynamic [18F]FDG PET-CT scans lasting 3 or 4h were performed with C57BL/6J and Balb/c nude mice bearing subcutaneous EL4 murine T-cell lymphoma and Colo205 human colorectal tumours, respectively, and with transgenic Eμ-Myc lymphoma mice. Prior to [18F]FDG injection, four combinations of different animal handling conditions were used: warming for 1h at 31°C; maintenance at room temperature (20 - 24°C), fasting for 6 - 10h and a fed state. Tumour mean standardised uptake value (SUVmean) peaked at 147 ± 48min post injection in subcutaneous tumours and 74 ± 31min in autochthonous Eμ-Myc lymphomas. The tumour-to-blood ratio (TBR) peaked at 171 ± 57 and 83 ± 33min in subcutaneous and autochthonous Eμ-Myc tumours, respectively. Fasting increased tumour [18F]FDG uptake and suppressed myocardial uptake in EL4 tumour-bearing mice. There was a good correlation between tumour SUVmean and Ki calculated using an input function (IDIF) derived from the inferior vena cava. Delayed static [18F]FDG-PET imaging (> 60min) in both autochthonous and subcutaneous tumours in improved tumour-to-background contrast and increased reproducibility.

DDDR-43. ERAS-801 IS A SELECTIVE BRAIN-PENETRANT EGFR INHIBITOR WITH IMPROVED ACTIVITY AGAINST EGFR EXTRACELLULAR DOMAIN-MUTANT GLIOBLASTOMA

Abstract Epidermal growth factor receptor (EGFR) is a driver of nearly 50% of glioblastoma (GBM), yet EGFR inhibitors have failed in GBM (e.g., erlotinib, lapatinib, neratinib, gefitinib, and afatinib) due to poor CNS penetration and/or an inability to inhibit EGFR in its wildtype and extracellular domain (ECD) mutated forms (e.g., EGFRvIII). Here we describe ERAS-801, a reversible and selective EGFR tyrosine kinase inhibitor with unique binding properties that enable potent activity for amplified and ECD mutant EGFR. ERAS-801 demonstrated the highest nonclinical CNS penetration out of all tested EGFR inhibitor comparators. Accordingly, relative to other EGFR inhibitors tested in GBM trials (e.g., erlotinib, lapatinib, and osimertinib), ERAS-801 showed superior survival benefit in EGFR altered orthotopic PDX models. Finally, further characterization of ERAS-801 in a preclinical trial of glioma orthotopic PDX models that capture the molecular heterogeneity of GBM, ERAS-801 demonstrated a significant survival benefit at a 25 mg/kg QD dose in the vast majority of EGFR-driven orthotopic GBM PDX models, with FDG-PET imaging serving as a non-invasive biomarker of response. Based on these nonclinical data, ERAS-801 is currently under clinical investigation for GBM.

Machine Learning Models Based on [18F]FDG PET Radiomics for Bone Marrow Assessment in Non-Hodgkin Lymphoma

Non-Hodgkin lymphoma is a heterogeneous group of cancers that triggers bone marrow infiltration in 20–40% of cases. Bone marrow biopsy in combination with a visual assessment of [18F]FDG PET/CT images is used to assess the marrow status. Despite the potential of both techniques, they still have limitations due to the subjectivity of visual assessment. The present study aims to develop models based on bone marrow uptake in [18F]FDG PET/CT images at the time of diagnosis to differentiate bone marrow status. For this purpose, a model trained for skeleton segmentation and based on the U-Net architecture is retrained for bone marrow segmentation from CT images. The mask obtained from this segmentation together with the [18F]FDG PET image is used to extract radiomics features with which 11 machine learning models for marrow status differentiation are trained. The segmentation model yields very satisfactory results with Jaccard and Dice index values of 0.933 and 0.964, respectively. As for the classification models, a maximum F1_score_weighted and F1_score_macro of 0.962 and 0.747, respectively, are achieved. This highlights the potential of these features for bone marrow assessment, laying the foundation for a new clinical decision support system.

Recurrence Detection by Hybrid [18F]FDG-PET in Advanced Head and Neck Squamous Cell Carcinoma.

In the clinical management of advanced stage head and neck squamous cell carcinoma, the use of hybrid [18F]FDG-PET imaging is well established. However, there is ongoing debate regarding the optimal frequency of hybrid PET scans to be included in follow-up protocols, particularly if the initial post-therapeutic scan is negative. We conducted a single-center 10-year retrospective study involving all patients who underwent hybrid PET scans for the follow-up of advanced stage head and neck cancer. A total of 285 patients were included. Out of 199 patients with a negative 3-month hybrid PET, 15% subsequently developed a recurrence during further follow-up. Notably, 90% of these recurrences were detected by hybrid PET. Fifteen percent of patients with a negative 3-month hybrid PET experienced a recurrence. Given that the majority of recurrences were detected by hybrid PET scans, there is a compelling argument for incorporating regular repetitive scans during follow-up.

Depression and metabolic connectivity: insights into the locus coeruleus, HF-rTMS, and anxiety

The use of repetitive Transcranial Magnetic Stimulation (rTMS) in treating major depressive disorder (MDD) is increasingly being explored in precision medicine. However, there’s a notable lack of understanding of the underlying neurobiological effects, which limits our ability to correlate specific imaging features with treatment efficacy. As one possible neurobiological mechanism, clinical research has already shown that in MDD, lower norepinephrine release in the locus coeruleus (LC) triggers depressive symptoms, and pharmacological approaches that block norepinephrine reuptake boost its levels, easing depression. Surprisingly, the LC has not received a more pronounced focus in contemporary rTMS research. This study investigates the role of the LC in MDD and its response to high-frequency (HF)-rTMS using 18FDG-PET imaging. We compared LC metabolic connectivity between MDD patients (n = 43) and healthy controls (n = 32). Additionally, we evaluated the predictive value of LC connectivity for HF-rTMS treatment outcomes and examined post-treatment changes in LC metabolic connectivity. Our findings revealed significant differences in LC metabolic connectivity between MDD patients and controls. Baseline LC metabolic connectivity did not predict HF-rTMS treatment outcomes. However, post-treatment analyses showed a significant correlation between improved clinical outcomes and attenuation of LC metabolic connectivity in regions associated with cognitive control and the default mode network. Notably, a reduction in state anxiety moderated this relationship, highlighting the role of anxiety in HF-rTMS efficacy for MDD treatment. Our findings suggest that LC metabolic connectivity, influenced by state anxiety levels, may be crucial in HF-rTMS efficacy, offering further insights for personalized MDD treatment strategies.

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [18F]FDG PET study.

In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [18F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD. This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction. We evaluated the IDLR model's ability to distinguish between subjects with different cognitive statuses and MCI trajectories (sMCI and pMCI) and compared results with radiomic and deep learning (DL) models. A Cox model tested the IDLR signature's predictive capability for MCI to AD progression. Correlation analyses identified critical IDLR features and verified their clinical diagnostic value. The IDLR model achieved the best classification results for subjects with different cognitive statuses as well as in those with MCI with distinct trajectories, with an accuracy of 76.51% [72.88%, 79.60%], (95% confidence interval, CI) while those of radiomic and DL models were 69.13% [66.28%, 73.12%] and 73.89% [68.99%, 77.89%], respectively. According to the Cox model, the hazard ratio (HR) of the IDLR model was 1.465 (95% CI: 1.236-1.737, p < 0.001). Moreover, three crucial IDLR features were significantly different across cognitive stages and were significantly correlated with cognitive scale scores (p < 0.01). Preliminary results demonstrated that the IDLR model based on [18F]FDG PET images enhanced accuracy in diagnosing the clinical spectrum of AD. Question The study addresses the lack of interpretability in existing DL classification models for diagnosing the AD spectrum. Findings The proposed interpretable DL radiomics model, using radiomics-supervised DL features, enhances interpretability from traditional DL models and improves classification accuracy. Clinical relevance The IDLR model interprets DL features through radiomics supervision, potentially advancing the application of DL in clinical classification tasks.

FDG-PET patterns associate with survival in patients with prion disease.

Prion disease classically presents with rapidly progressive dementia, leading to death within months of diagnosis. Advances in diagnostic testing have improved recognition of patients with atypical presentations and protracted disease courses, raising key questions surrounding the relationship between patterns of neurodegeneration and survival. We assessed the contribution of fluorodeoxyglucose (FDG-PET) imaging for this purpose. FDG-PET were performed in 40 clinic patients with prion disease. FDG-PET images were projected onto latent factors generated in an external dataset to yield patient-specific eigenvalues. Eigenvalues were input into a clustering algorithm to generate data-driven clusters, which were compared by survival time. Median age at FDG-PET was 65.3 years (range 23-85). Median time from FDG-PET to death was 3.7 months (range 0.3-19.0). Four data-driven clusters were generated, termed "Neocortical" (n = 7), "Transitional" (n = 12), "Temporo-parietal" (n = 13), and "Deep nuclei" (n = 6). Deep nuclei and transitional clusters had a shorter survival time than the neocortical cluster. Subsequent analyses suggested that this difference was driven by greater hypometabolism of deep nuclei relative to neocortical areas. FDG-PET-patterns were not associated with demographic (age and sex) or clinical (CSF total-tau, 14-3-3) variables. Greater hypometabolism within deep nuclei relative to neocortical areas associated with more rapid decline in patients with prion disease and vice versa. FDG-PET informs large-scale network physiology and may inform the relationship between spreading pathology and survival in patients with prion disease. Future studies should consider whether FDG-PET may enrich multimodal prion disease prognostication models.

Predictive value of cardiac 18F-fluorodeoxyglucose positron emission tomography for fatal ventricular arrhythmic events in patients with cardiac sarcoidosis

Abstract Background Patients with cardiac sarcoidosis (CS) face an elevated risk of fatal ventricular arrhythmic events (FVAE), including sudden cardiac death. Late gadolinium enhancement (LGE) from contrast-enhanced cardiac magnetic resonance is used for diagnosis of CS and estimation of FVAE risk. However, its application is limited in cases with cardiac devices, renal failure or contrast agent allergies. 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) emerges as a diagnostic tool for CS, even in cases where LGE is not feasible. Purpose This study aimed to investigate the potential utility of FDG-PET in predicting FVAE in patients with CS by hypothesizing that higher or lower percent FDG uptake could predict the FVAE risk. Methods Cardiac FDG-PET data from 121 patients with CS, acquired at our university hospital between March 2008 and November 2020, were analyzed. A polar-map model was used to calculate percent uptake relative to maximal cardiac FDG uptake in each of the American Heart Association (AHA) 17 segments (Figure A). Percent uptakes of the AHA 17 segments were compared between groups with and without future FVAE. LGE information was available in 82 patients and was used to assess the presence of LGE in regions where percent FDG uptake differed between the two groups. Results Among enrolled patients (mean age 59.5 ± 10.0 years, 67.8% women), 43 experienced FVAE following FDG-PET image acquisition. The group with future FVAE had a higher history of FVAE (7.7% vs. 37.2%) and beta-blocker administration (38.5% vs. 60.5%), larger left ventricular diastolic diameter (LVDd) (49.3 ± 7.3 mm vs. 57.4 ± 9.5 mm) and lower ejection fraction (LVEF) (56.6 ± 13.6% vs. 42.0 ± 15.2%) compared to the group without it (P &amp;lt; 0.05 all). Percent FDG uptake in the basal interventricular septum part (segment 3) (Figure A) was lower in patients with future FVAE compared to those without it (54.4 ± 13.8% vs. 41.8 ± 15.2%, P &amp;lt; 0.001 corrected for multiple comparisons). Kaplan-Meier analysis with a median split of percent FDG uptake in segment 3 showed that patients with lower uptake (&amp;lt;49%, N= 62) had lower FVAE-free survival compared to those with higher uptake (≥49%, N = 59) (P = 0.007) (Figure B). A Cox hazard model also demonstrated an association between percent FDG uptake in segment 3 and FVAE onset (hazard ratio 0.972 [95% CI 0.951, 0.995], P = 0.015) after adjusting for previous FVAE, beta-blocker administration, LVDd and LVEF. Similarly, analysis within the subgroup with LGE in the septum (N = 58) revealed a higher risk of FVAE in the group with lower percent FDG uptake compared to those with higher uptake (P = 0.020) (Figure C(a)). However, this association was not evident in patients without LGE in the septum (N = 24) (P = 0.821) (Figure C(b)). Conclusions Reduced percent FDG uptake with LGE in the basal intraventricular septum may serve as a valuable predictor of future FVAE occurrence in patients with CS.

Reduced 18F-FDG uptake in the basal interventricular septum as a predictor of fatal ventricular arrhythmic events in patients with cardiac sarcoidosis

BackgroundPatients with cardiac sarcoidosis (CS) are at an increased risk of fatal ventricular arrhythmic events (FVAE). However, the predictive value of 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in assessing the risk of FVAE in patients with CS remains uncertain. MethodsWe included data from 121 patients with CS (39 men and 82 women; mean age: 59.5 years) who underwent FDG-PET imaging between March 2008 and November 2020, with follow-ups completed in July 2023. Of these, 82 patients had available cardiac magnetic resonance imaging data, including late gadolinium enhancement (LGE). FDG-PET images were analysed using a polar-map model to determine the regional mean percentage uptake relative to the maximal cardiac 18F-FDG uptake in each of the 17 segments defined by the American Heart Association. ResultsPatients experiencing FVAE after FDG-PET (n = 43) showed lower percent uptake in the basal inferoseptal segment compared to those who did not (n = 78) (41.8 ± 15.2 % vs. 54.4 ± 13.8 %, P < 0.001). Patients with a basal inferoseptal percent uptake below the median had a lower FVAE-free survival rate than those with a higher percent uptake (58.1 % vs. 78 % at 5 years, P = 0.007), which was consistent in patients with LGE in the same regions with reduced 18F-FDG uptake. A Cox hazard model indicated that the FVAE risk decreased with a hazard ratio of 0.862 (95 % CI 0.770–0.964) for every 5 % increase in basal inferoseptal percent uptake (P = 0.009). ConclusionReduced 18F-FDG uptake in the basal interventricular septum, including the inferoseptal segment, may be a valuable predictor of future FVAE in patients with CS.

Identification of metabolic progression and subtypes in progressive supranuclear palsy by PET molecular imaging.

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder with diverse clinical presentations that are linked to tau pathology. Recently, Subtype and Stage Inference (SuStaIn) algorithm, an innovative data-driven method, has been developed to model both the spatial-temporal progression and subtypes of disease. This study explores PSP progression using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging and the SuStaIn algorithm to identify PSP metabolic progression subtypes and understand disease mechanisms. The study included 72 PSP patients and 70 controls, with an additional 24 PSP patients enrolled as a test set, undergoing FDG-PET, dopamine transporter (DAT) PET, and neuropsychological assessments. The SuStaIn algorithm was employed to analyze the FDG-PET data, identifying progression subtypes and sequences. Two PSP subtypes were identified: the cortical subtype with early prefrontal hypometabolism and the brainstem subtype with initial midbrain alterations. The cortical subtype displayed greater cognitive impairment and DAT reduction than the brainstem subtype. The test set demonstrates the robustness and reproducibility of the findings. Pathway analysis indicated that disruptions in dopaminergic cortico-basal ganglia pathways are crucial for elucidating the mechanisms of cognitive and behavioral impairment in PSP, leading to the two metabolic progression subtypes. This study identified two spatiotemporal progression subtypes of PSP based on FDG-PET imaging, revealing significant differences in metabolic patterns, striatal dopaminergic uptake, and clinical profiles, particularly cognitive impairments. The findings highlight the crucial role of dopaminergic cortico-basal ganglia pathways in PSP pathophysiology, especially in the cortical subtype, providing insights into PSP heterogeneity and potential avenues for personalized treatments.

Platform for the radiomics analysis of brain regions: The case of Alzheimer's disease and metabolic imaging

ObjectiveThis study introduces a PET-based platform for brain radiomics analysis. We automatically identify key brain regions and features associated with Alzheimer's disease (AD), enabling more accurate diagnosis and staging compared to using predefined regions. MethodsTo create an integrated platform that covers all the phases of radiomics, we obtained FDG-PET images of 549 individuals from the ADNI database. We used FastSurfer to segment the brain into 95 regions. We then obtained 120 features for each of the 95 ROIs. We employed eight feature selection methods to select and analyze the features. We finally utilized nine different classifiers on the 20 most significant features extracted. ResultsFor all three predictions AD vs. cognitively normal (CN), AD vs. mild cognitive impairments (MCI), and CN vs. MCI the Random Forest (RF) classifier with LASSO demonstrated the highest accuracy with an AUC of 0.976 for AD vs CN, AUC=0.917 for AD vs MCI, and AUC=0.877 for MCI vs CN. This is the highest performance that we encountered compared to the studies in the literature. Three subregions hippocampus, entorhinal, and amygdala could then be identified as critical. ConclusionA brain radiomics platform can enable an efficient, standardized, and optimally accurate AD and MCI diagnosis from FDG PET images by using an automated pipeline. The three regions identified as having the highest discriminating power confirm the findings of previous clinical research results on AD. While the focus was AD in this study, the platform can potentially be used to address other brain conditions.

EANM/SNMMI guideline/procedure standard for [18F]FDG hybrid PET use in infection and inflammation in adults v2.0.

Hybrid [18F]FDG PET imaging is currently the method of choice for a wide variety of infectious and inflammatory disorders and was recently adopted in several clinical guidelines. A large amount of evidence-based articles, guidelines and appropriate use criteria have been published since the first version of this guideline in 2013. To provide updated evidence-based information to assist physicians in recommending, performing and interpreting hybrid [18F]FDG PET examinations for infectious and inflammatory disorders in the adult population. A systematic literature search of evidence-based articles using whole-body [18F]FDG hybrid imaging on the indications covered within this guideline was performed. All systematic reviews and meta-analyses published within the last 10 years until January 2023 were identified in PubMed/Medline or Cochrane. For each indication covered in this manuscript, diagnostic performance was provided based on meta-analyses or systematic reviews. If not available, results from prospective or retrospective studies were considered based on predefined selection criteria. RESULTSAND CONCLUSIONS: Hybrid [18F]FDG PET is extremely useful in the work-up and management of adults with infectious and inflammatory diseases, as supported by extensive and rapidly growing evidence-based literature and adoption in clinical guidelines. Practical recommendations are provided describing evidence-based indications as well as interpretation criteria and pitfalls.Monitoring treatment response is the most challenging but insufficiently studied potential application in infection and inflammation imaging.

18F]FDG PET for mapping the cerebral glucose metabolic characteristics of drug-sensitive and drug-resistant epilepsy in pediatric patients.

This study aimed to investigate [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) mapping for cerebral glucose metabolism in drug-sensitive and drug-resistant pediatric epilepsy patients. This retrospective study enrolled 40 patients and 25 controls. Patients were categorized into drug-sensitive epilepsy (n = 22) and drug-resistant epilepsy (n = 18) according to the seizure frequency at follow-up. All patients underwent two [18F]FDG PET scans separated by a minimum of one year. Absolute asymmetry index (|AI|) was calculated for assessing metabolic differences and changes in epileptic foci. Statistical Parametric Mapping (SPM) was utilized to reveal voxel-wise metabolic characteristics and alterations throughout the brain. Network analysis based on graph theory was used to investigate network-level differences between the two patient groups. The drug-sensitive group showed a lower |AI| at both baseline (P = 0.038) and follow-up (P = 0.003) PET scans than the drug-resistant group. |AI| decreased in the drug-sensitive group and increased in the drug-resistant group across scans, but these trends were not statistically significant (P = 0.240 and P = 0.450, respectively). Both groups exhibited hypometabolism at baseline. The drug-sensitive group showed less hypometabolic brain regions than the drug-resistant group. The drug-sensitive maintained stable level of hypometabolism between the two scans, whereas the drug-resistant group showed an increasing hypometabolism. Network analysis demonstrated that the drug-sensitive group had a higher global efficiency, average degree, and clustering, along with a shorter characteristic path length compared to the drug-resistant group. For the first time, this study revealed in vivo cerebral glucose metabolic pattern of nonsurgical pediatric epilepsy patients treated by antiepileptic drugs. Especially, drug-resistant epilepsy patients represented significantly extensive and progressive hypometabolism with inefficient brain network connectivity compared with drug-sensitive epilepsy. [18F]FDG PET imaging may be a potential visual approach for theranostics of epilepsy patients.

Crossover evaluation of time-of-flight-based attenuation correction in brain 18F-FDG and 18F-flutemetamol PET.

Brain-dedicated positron emission tomography (PET) systems offer high spatial resolution and sensitivity for accurate clinical assessments. Attenuation correction (AC) is important in PET imaging, particularly in brain studies. This study assessed the reproducibility of attenuation maps (µ-maps) generated by a specialized time-of-flight (TOF) brain-dedicated PET system for imaging using different PET tracers. Twelve subjects underwent both 18F-fluorodeoxyglucose (FDG)-PET and 18F-flutemetamol (FMM) amyloid-PET scans. Images were reconstructed with µ-maps obtained by a maximum likelihood-based AC method. Voxel-based and region-based analyses were used to compare µ-maps obtained with FDG-PET versus FMM-PET; FDG-PET images reconstructed using an FDG-PET µ-map (FDG × FDG) versus those reconstructed with an FMM-PET µ-map (FDG × FMM); and FMM-PET images reconstructed using an FDG-PET µ-map (FMM × FDG) versus those reconstructed with an FMM-PET µ-map (FMM × FMM). Small but significant differences in µ-maps were observed between tracers, primarily in bone regions. In the comparison between the µ-maps obtained with FDG-PET and FMM-PET, the µ-maps obtained with FDG-PET had higher µ-values than those obtained with FMM-PET in the parietal regions of the head and skull, in a portion of the cerebellar dentate nucleus and on the surface of the frontal lobe. The comparison between FDG and FDG × FMM values in different regions yielded findings similar to those of the µ-maps comparison. FDG × FMM values were significantly higher than FDG values in the bilateral temporal bones and a small part of the temporal lobe. Similarly, FMM values were significantly higher than FMM × FDG values in the bilateral temporal bones. FMM × FDG values were significantly higher than FMM values in a small area of the right cerebellar hemisphere. However, the relative errors in these µ-maps were within ± 4%, suggesting that they are clinically insignificant. In PET images reconstructed with the original and swapped µ-maps, the relative errors were within ± 7% and the quality was nearly equivalent. These findings suggest the clinical reliability of the AC method without an external radiation source in TOF brain-dedicated PET systems.

Quantitative brain [18F]FDG PET beyond normal blood glucose levels

Introduction SUV measurements from static brain [18F]FDG PET acquisitions are a commonly used tool in preclinical research, providing a simple alternative for kinetic modelling, which requires complex and time-consuming dynamic acquisitions. However, SUV can be severely affected by the animal handling and preconditioning protocols, primarily by those that may induce changes in blood glucose levels (BGL). Here, we aimed at developing and investigating the feasibility of SUV-based approaches for a wide range of BGL far beyond normal values, and consequently, to develop and validate a new model to generate standardized and reproducible SUV measurements for any BGL.Material and methods We performed dynamic and static brain [18F]FDG PET acquisitions in 52 male Sprague-Dawley rats sorted into control (n = 10), non-fasting (n = 14), insulin-induced hypoglycemia (n = 12) and glucagon-induced hyperglycemia (n = 16) groups. Brain [18F]FDG PET images were cropped, aligned and co-registered to a standard template to calculate whole-brain and regional SUV. Cerebral Metabolic Rate of Glucose (CMRglc) was also estimated from 2-Tissue Compartment Model (2TCM) and Patlak plot for validation purposes.Results Our results showed that BGL=100±6 mg/dL can be considered a reproducible reference value for normoglycemia. Furthermore, we successfully established a 2nd-degree polynomial model (C1=0.66E-4, C2=-0.0408 and C3=7.298) relying exclusively on BGL measures at pre-[18F]FDG injection time, that characterizes more precisely the relationship between SUV and BGL for a wide range of BGL values (from 10 to 338 mg/dL). We confirmed the ability of this model to generate corrected SUV estimations that are highly correlated to CMRglc estimations (R2= 0.54 2TCM CMRgluc and R2= 0.49 Patlak CMRgluc). Besides, slight regional differences in SUV were found in animals from extreme BGL groups, showing that [18F]FDG uptake is mostly directed toward central regions of the brain when BGLs are significantly decreased.Conclusion Our study successfully established a non-linear model that relies exclusively on pre-scan BGL measurements to characterize the relationship between [18F]FDG SUV and BGL. The extensive validation confirmed its ability to generate SUV-based surrogates of CMRglu along a wide range of BGL and it holds the potential to be adopted as a standard protocol by the preclinical neuroimaging community using brain [18F]FDG PET imaging.

Advancements and Challenges in Personalized Therapy for BRAF-Mutant Melanoma: A Comprehensive Review.

Over the past several decades, advancements in the treatment of BRAF-mutant melanoma have led to the development of BRAF inhibitors, BRAF/MEK inhibitor combinations, anti-PD-1 therapy, and anti-CTLA4 therapy. Although these therapies have shown substantial efficacy in clinical trials, their sustained effectiveness is often challenged by the tumor microenvironment, which is a highly heterogeneous and complex milieu of immunosuppressive cells that affect tumor progression. The era of personalized medicine holds substantial promise for the tailoring of treatments to individual genetic profiles. However, tumor heterogeneity and immune evasion mechanisms contribute to the resistance to immunotherapy. Despite these challenges, tumor-infiltrating lymphocyte (TIL) therapy, as exemplified by lifileucel, has demonstrated notable efficacy against BRAF V600-mutant melanoma. Additionally, early response biomarkers, such as COX-2 and MMP2, along with FDG-PET imaging, offer the potential to improve personalized immunotherapy by predicting patient responses and determining the optimal treatment duration. Future efforts should focus on reducing the T-cell harvesting periods and costs associated with TIL therapy to enhance efficiency and accessibility.

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures.Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons.We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score.Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

Imaging finding of renal masses associated with pathogenic variation in succinate dehydrogenase subunit B gene

PurposeSuccinate dehydrogenase (SDH)-deficient renal cell carcinoma (RCC) is a newly defined, rare subtype of renal cancer, associated with pathogenic variations in the Succinate Dehydrogenase Subunit B (SDHB) gene. Our aim is to investigate the imaging findings of SDHB-associated renal tumors, utilizing cross-sectional and FDG-PET imaging in patients with pathogenic variations in SDHB gene, to facilitate accurate tumor characterization. MethodsTwenty SDH-deficient tumors from 16 patients with pathogenic variations in SDHB gene were retrospectively evaluated using cross-sectional and FDG-PET imaging. Clinical findings such as demographics, family history, extra-renal findings and metastases were recorded. Tumor imaging characteristics on CT/MRI included were laterality, size, homogeneity, morphology, margins, internal content, T1/T2 signal intensity, enhancement features, and restricted diffusion. ResultsSixteen patients (median age 31 years, IQR 19–41, 8 males) were identified with 68.8 % of patients having a known family history of SDHB variation. 81.3 % of lesions were solitary and majority were solid (86.7 % on CT, 87.5 % on MRI) with well-defined margins in >62.5 % of lesions, without evidence of internal fat, calcifications, or vascular invasion. 100 % of lesions demonstrated restricted diffusion and avid enhancement, with degree >75 % for most lesions on CT and MRI. On FDG-PET, all renal masses showed increased radiotracer uptake. 43.8 % of patients demonstrated extra-renal manifestations and 43.8 % had distant metastasis. ConclusionSDHB-associated RCC is predominantly noted in young patients with no gender predilection. On imaging, SDH-deficient RCC are frequently unilateral, solitary, and solid with well-defined margins demonstrating avid enhancement with variability in enhancement pattern and showing restricted diffusion.