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This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18 F-Florbetaben PET and clinical data to improve diagnostic accuracy. Furthermore, the study explored machine learning models to predict cognitive status from early-phase PET, maximizing the clinical utility of dual-phase imaging protocols. 176 subjects who completed dual-phase 18 F-FBB PET scanning including 38 with normal cognition, 94 with mild cognitive impairment, and 44 with dementia were enrolled. Aβ status was determined from delayed-phase 18 F-FBB PET scans (90–110 min post-injection). To develop a machine learning model for predicting Aβ positivity, we utilized early-phase PET and clinical features. From early-phase 18 F-FBB PET scans (0–10 min post-injection), we extracted brain region-specific standardized uptake value ratios (SUVR) as imaging features. Various classifiers, including Random Forest, Gradient Boosting, and XGBoost, were trained and evaluated using accuracy, ROC AUC, recall, and F1 scores. Feature importance was assessed to identify key predictors, and the importance of features that most significantly influenced each model’s results was calculated. The early-phase PET alone showed moderate performance (80.56% accuracy with Random Forest), with hippocampus (importance: 0.086), isthmus of cingulate (0.051), and entorhinal (0.038) SUVR values as top predictors. The combined PET and clinical data model achieved the highest accuracy (88.89%) using Gradient Boosting, with key predictors including APOE genotype (importance: 0.2485), Medial Orbitofrontal SUVR (0.0996), and hippocampal SUVR (0.0663). In predicting cognitive status using early-phase PET, most classifiers achieved high accuracy (> 80%) and F1 scores (0.82–0.90), with Decision Tree showing the highest accuracy of 83.33%. Machine learning models combining PET and clinical data demonstrated superior predictive accuracy for Aβ positivity prediction, while early-phase PET alone showed robust performance in predicting cognitive status, highlighting the synergistic potential of multimodal data and versatile utility of early-phase PET imaging.

Optimal pharmacokinetic models for quantifying amyloid beta (Aβ) burden using both [18F]flutemetamol and [18F]florbetaben scans have previously been identified at a region of interest (ROI) level. The purpose of this study was to determine optimal quantitative methods for parametric analyses of [18F]flutemetamol and [18F]florbetaben scans. Forty-six participants were scanned on a PET/MR scanner using a dual-time window protocol and either [18F]flutemetamol (N=24) or [18F]florbetaben (N=22). The following parametric approaches were used to derive DVR estimates: reference Logan (RLogan), receptor parametric mapping (RPM), two-step simplified reference tissue model (SRTM2) and multilinear reference tissue models (MRTM0, MRTM1, MRTM2), all with cerebellar grey matter as reference tissue. In addition, a standardized uptake value ratio (SUVR) was calculated for the 90–110 min post injection interval. All parametric images were assessed visually. Regional outcome measures were compared with those from a validated ROI method, i.e. DVR derived using RLogan. Visually, RPM, and SRTM2 performed best across tracers and, in addition to SUVR, provided highest AUC values for differentiating between Aβ-positive vs Aβ-negative scans ([18F]flutemetamol: range AUC=0.96–0.97 [18F]florbetaben: range AUC=0.83–0.85). Outcome parameters of most methods were highly correlated with the reference method (R2≥0.87), while lowest correlation were observed for MRTM2 (R2=0.71–0.80). Furthermore, bias was low (≤5%) and independent of underlying amyloid burden for MRTM0 and MRTM1. The optimal parametric method differed per evaluated aspect; however, the best compromise across aspects was found for MRTM0 followed by SRTM2, for both tracers. SRTM2 is the preferred method for parametric imaging because, in addition to its good performance, it has the advantage of providing a measure of relative perfusion (R1), which is useful for measuring disease progression.

Subjective cognitive decline (SCD) is considered an early risk stage for dementia due to Alzheimer's disease (AD) and the development of pathological brain changes, such as the aggregation of amyloid-beta (amyloid-β) plaques. This study evaluates the association between specific features of SCD and cerebral amyloid-β load measured by positron emission tomography (PET) with 18 F-florbetaben in 40 cognitively normal older individuals. Global amyloid-β, as well as regional amyloid-β load for the frontal, temporal, parietal, and cingulate cortex, was quantified. Specific features of SCD, such as subjective cognitive complaints and worry, were assessed using the 39-item Everyday Cognition Scales and the 16-item Penn State Worry Questionnaire. Spearman's rank partial correlation analyses, adjusted for age and apolipoprotein E ε4 status, were conducted to test the associations between specific features of SCD and cerebral amyloid-β load. The severity of subjective cognitive complaints in everyday memory and organization was positively correlated with amyloid-β load in the frontal cortex. In addition, the severity of subjective cognitive complaints in everyday planning was positively correlated with amyloid-β load in the parietal cortex. Higher levels of worry were associated with higher amyloid-β load in the frontal cortex. After correction of the PET data for partial volume effects, these associations were reduced to trend level. In conclusion, the severity of subjective cognitive complaints and the level of trait worry were positively associated with cortical amyloid-β burden, particularly in the frontal and parietal cortex. Further studies are required to elucidate the direction of these associations in order to develop strategies to prevent amyloid deposition and cognitive decline.

The accumulation of beta amyloid in the brain has a complex and poorly understood impact on the progression of Parkinson’s disease pathology and much controversy remains regarding its role, specifically in cognitive decline symptoms. Some studies have found increased beta amyloid burden is associated with worsening cognitive impairment in Parkinson’s disease, especially in cases where dementia occurs, while other studies failed to replicate this finding. To better understand this relationship, we examined a cohort of 25 idiopathic Parkinson’s disease patients and 30 healthy controls from the Parkinson’s Progression Marker Initiative database. These participants underwent [ 18 F]Florbetaben positron emission tomography scans to quantify beta amyloid deposition in 20 cortical regions. We then analyzed this beta amyloid data alongside the longitudinal Montreal Cognitive Assessment scores across 3 years to see how participant’s baseline beta amyloid levels affected their cognitive scores prospectively. The first analysis we performed with these data was a hierarchical cluster analysis to help identify brain regions that shared similarity. We found that beta amyloid clusters differently in Parkinson’s disease patients compared to healthy controls. In the Parkinson’s disease group, increased beta amyloid burden in cluster 2 was associated with worse cognitive ability, compared to deposition in clusters 1 or 3. We also performed a stepwise linear regression where we found an adjusted R 2 of 0.495 (49.5%) in a model explaining the Parkinson’s disease group’s Montreal Cognitive Assessment score 1-year post-scan, encompassing the left gyrus rectus, the left anterior cingulate cortex, and the right parietal cortex. Taken together, these results suggest regional beta amyloid deposition alone has a moderate effect on predicting future cognitive decline in Parkinson’s disease patients. The patchwork effect of beta amyloid deposition on cognitive ability may be part of what separates cognitive impairment from cognitive sparing in Parkinson’s disease. Thus, we suggest it would be more useful to measure beta amyloid burden in specific brain regions rather than using a whole-brain global beta amyloid composite score and use this information as a tool for determining which Parkinson’s disease patients are most at risk for future cognitive decline.

Dementia is a degenerative disease that is increasingly prevalent in an aging society. Alzheimer’s disease (AD), the most common type of dementia, is best mitigated via early detection and management. Deep learning is an artificial intelligence technique that has been used to diagnose and predict diseases by extracting meaningful features from medical images. The convolutional neural network (CNN) is a representative application of deep learning, serving as a powerful tool for the diagnosis of AD. Recently, vision transformers (ViT) have yielded classification performance exceeding that of CNN in some diagnostic image classifications. Because the brain is a very complex network with interrelated regions, ViT, which captures direct relationships between images, may be more effective for brain image analysis than CNN. Therefore, we propose a method for classifying dementia images by applying 18F-Florbetaben positron emission tomography (PET) images to ViT. Data were evaluated via binary (normal control and abnormal) and ternary (healthy control, mild cognitive impairment, and AD) classification. In a performance comparison with the CNN, VGG19 was selected as the comparison model. Consequently, ViT yielded more effective performance than VGG19 in binary classification. However, in ternary classification, the performance of ViT cannot be considered excellent. These results show that it is hard to argue that the ViT model is better at AD classification than the CNN model.

This study investigated the earliest change of cerebral blood flow (CBF) and its relationship with β-amyloid (Aβ) burden in preclinical Alzheimer’s disease (AD) employing dual-phase 18 F-florbetaben (FBB) PET. Seventy-one cognitively normal (NC) individuals were classified as Aβ negative (Aβ − NC) or positive (Aβ + NC) based on two different cutoff values: an SUVR of > 1.08 and a Centiloid scale of > 20. The PET scans were acquired in two phases: an early phase (0–10 min, eFBB) and a delayed phase (90–110 min, dFBB), which were averaged to generate single-frame images for each phase. Furthermore, an R1 parametric map was generated from the early phase data using a simplified reference tissue model. We conducted regional and voxel-based analyses to compare the eFBB, dFBB, and R1 images between the Aβ positive and negative groups. In addition, the correlations between the CBF proxy R1 and the dFBB SUVR were analyzed. The Aβ + NC group showed significantly higher dFBB SUVR in both the global cerebral cortex and target regions compared to the Aβ − NC group, while no significant differences were observed in eFBB SUVR between the two groups. Furthermore, the Aβ + NC group exhibited significantly higher R1 values, a proxy for cerebral perfusion, in both the global cerebral cortex and target regions compared to the Aβ − NC group. Significant positive correlations were observed between R1 and dFBB SUVR in both the global cerebral cortex and target regions, which remained significant after controlling for demographics and cognitive profiles, except for the medial temporal and occipital cortices. The findings reveal increased CBF in preclinical AD and a positive correlation between CBF and amyloid pathology. The positive correlation between R1 and amyloid burden may indicate a compensatory mechanism in the preclinical stage of Alzheimer’s disease, but to elucidate this hypothesis, further longitudinal observational studies are necessary.

The objective of the present work was to evaluate the potential of deep learning tools for characterizing the presence of cardiac amyloidosis from early acquired PET images, i.e. 15 min after [18F]-Florbetaben tracer injection. 47 subjects were included in the study: 13 patients with transthyretin-related amyloidosis cardiac amyloidosis (ATTR-CA), 15 patients with immunoglobulin light-chain amyloidosis (AL-CA), and 19 control-patients (CTRL). [18F]-Florbetaben PET/CT images were acquired in list mode and data was sorted into a sinogram, covering a time interval of 5 min starting 15 min after the injection. The resulting sinogram was reconstructed using OSEM iterative algorithm. A deep convolutional neural network (CAclassNet) was designed and implemented, consisting of five 2D convolutional layers, three fully connected layers and a final classifier returning AL, ATTR and CTRL scores. A total of 1107 2D images (375 from AL-subtype patients, 312 from ATTR-subtype, and 420 from Controls) have been considered in the study and used to train, validate and test the proposed network. CAclassNet cross-validation resulted with train error mean ± sd of 2.001% ± 0.96%, validation error of 4.5% ± 2.26%, and net accuracy of 95.49% ± 2.26%. Network test error resulted in a mean ± sd values of 10.73% ± 0.76%. Sensitivity, specificity, and accuracy evaluated on the test dataset were respectively for AL-CA sub-type: 1, 0.912, 0.936; for ATTR-CA: 0.935, 0.897, 0.972; for control subjects: 0.809, 0.971, 0.909. In conclusion, the proposed CAclassNet model seems very promising as an aid for the clinician in the diagnosis of CA from cardiac [18F]-Florbetaben PET images acquired a few minutes after the injection.

Successful back-translating clinical biomarkers and molecular imaging methods of Alzheimer's disease (AD), including positron emission tomography (PET), are very valuable for the evaluation of new therapeutic strategies and increase the quality of preclinical studies. 18 F-Fluorodeoxyglucose (FDG)–PET and 18 F-Florbetaben–PET are clinically established biomarkers capturing two key pathological features of AD. However, the suitability of 18 F-FDG– and amyloid–PET in the widely used 5XFAD mouse model of AD is still unclear. Furthermore, only data on male 5XFAD mice have been published so far, whereas studies in female mice and possible sex differences in 18 F-FDG and 18 F-Florbetaben uptake are missing. The aim of this study was to evaluate the suitability of 18 F-FDG– and 18 F-Florbetaben–PET in 7-month-old female 5XFAD and to assess possible sex differences between male and female 5XFAD mice. We could demonstrate that female 5XFAD mice showed a significant reduction in brain glucose metabolism and increased cerebral amyloid deposition compared with wild type animals, in accordance with the pathology seen in AD patients. Furthermore, we showed for the first time that the hypometabolism in 5XFAD mice is gender-dependent and more pronounced in female mice. Therefore, these results support the feasibility of small animal PET imaging with 18 F-FDG- and 18 F-Florbetaben in 5XFAD mice in both, male and female animals. Moreover, our findings highlight the need to account for sex differences in studies working with 5XFAD mice.

Background/Objectives: Quantitative analysis of amyloid PET imaging plays a crucial role in diagnosing Alzheimer’s disease (AD), particularly in cases where visual interpretation is equivocal. Multiple commercial software tools are available for this purpose, yet differences in their quantification and diagnostic performance remain understudied, especially for Neurophet SCALE PET. Methods: We retrospectively analyzed 18F-florbetaben PET/CT scans from 129 patients with cognitive impairment, comprising 39 patients with AD and 90 with non-AD diagnoses, using three software tools: MIMneuro, CortexID Suite, and Neurophet SCALE PET. Standardized uptake value ratios (SUVRs) were obtained for six brain regions known for amyloid accumulation. Diagnostic accuracy was evaluated using ROC curve analysis, while inter-software correlations and reliability were assessed via Pearson correlation coefficients and intraclass correlation coefficients (ICC). Results: All three software programs significantly distinguished AD from non-AD patients in most brain regions. MIMneuro and Neurophet SCALE PET demonstrated the highest diagnostic performance, with MIMneuro achieving an AUC of 1.000 in the anterior cingulate gyrus. While MIMneuro and Neurophet SCALE PET showed moderate-to-strong SUVR correlations (r = 0.715–0.865), CortexID Suite showed limited correlation with the other tools. Inter-software reliability was moderate only in selected regions (ICC ≈ 0.5), indicating potential variability in SUVR measurements across platforms. Conclusions: MIMneuro, CortexID Suite, and Neurophet SCALE PET are effective for the semi-quantitative analysis of amyloid PET and can aid in the diagnosis of AD. However, clinicians should be cautious when interpreting SUVRs across different software tools due to limited inter-software consistency. Standardization efforts or consistent use of a single platform are recommended to avoid diagnostic discrepancies.

Background Kinetic modeling of 18 F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18 F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. Methods 18 F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer’s disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (V T , V s ) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ( was also calculated from the multi-reference tissue model (MRTM). Results Amyloid-positive (AD) patients showed the highest V T and V S in anterior cingulate, posterior cingulate, and precuneus, consistent with analysis. and V T from kinetic models were correlated (r² = 0.46, P  < 2 with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. V T from 2TCM was highly correlated ( = 0.65, P  < 2 ) with Logan graphical V T estimation. Conclusion Non-invasive quantification of amyloid binding from total-body 18 F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to in amyloid-positive and amyloid-negative older individuals.