FDG PET and cognitive symptoms of dementia

Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis

OBJECTIVE

FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls.

METHODS

18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomics analysis was performed on the whole brain after normalization to an optimized template. After selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, the features obtained were added to a neural network model to find the accuracy in classifying HC and demented patients. Forty subjects not included in the training were used to test the models. The results of the three models (radiomics, 3D-CNN, combined model) were compared with each other.

RESULTS

Four radiomics features were selected. The sensitivity was 100% for the three models, but the specificity was higher with radiomics and combined one (100% vs. 85%). Moreover, the classification scores were significantly higher using the combined model in both normal and demented subjects.

CONCLUSION

The combination of radiomics features and 3D-CNN in a single model, applied to the whole brain 18FDG PET study, increases the accuracy in demented patients.

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis

METHODS

18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomic analysis was performed on the whole brain after normalization to an optimized template. After feature selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, a Neural Network model was tested to find the accuracy to classify HC and demented patients. Twenty subjects not included in the training were used to test the models. The results were compared with those obtained by conventional CNN model.

RESULTS

Four radiomic features were selected. The validation and test accuracies were 100% for both models, but the probability scores were higher with radiomics, in particular for HC group ( P  = 0.0004).

CONCLUSION

Radiomic features extracted from standardized PET whole brain images seem to be more accurate than CNN to distinguish patients with and without dementia.

Comparison of dynamic susceptibility contrast enhanced MR and FDG-PET brain studies in patients with Alzheimer’s disease and amnestic mild cognitive impairment

Background

The aim of this study was to compare Dynamic Susceptibility Contrast Enhanced MRI (DSC-MRI) and PET with [18F]flurodeoxyglucose (FDG-PET) in the diagnosis of Alzheimer’s Disease (AD) and amnestic Mild Cognitive Impairment (aMCI).

Methods

Twenty-seven age-and sex-matched patients with AD, 39 with aMCI and 16 controls underwent brain DSC-MRI followed by FDG-PET. Values of relative Cerebral Blood Volume (rCBV) and rCBV z-scores from frontal, temporal, parietal and PCG cortices were correlated with the rate of glucose metabolism from PET. Sensitivity, specificity and accuracy of DSC-MRI and FDG-PET in the diagnosis of AD and aMCI were assessed and compared.

Results

In AD, hypoperfusion was found within all the examined locations, while in aMCI in both parietal and temporal cortices and left PCG. FDG-PET showed the greatest hypometabolism in parietal, temporal and left PCG regions in both AD and aMCI. FDG-PET was more accurate in distinguishing aMCI from the controls than DSC-MRI. In the AD and combined group (AD + aMCI) there were numerous correlations between DSC-MRI and FDG-PET results.

Conclusions

In AD the patterns of hypoperfusion and glucose hypometabolism are similar, thus DSC-MRI may be a competitive method to FDG-PET. FDG-PET is a more accurate method in the diagnosis of aMCI.

Is there utility for fluorine-18-fluorodeoxyglucose positron-emission tomography scan before surgery in breast cancer? A 15-year overall survival analysis

BACKGROUND

The prognostic value of preoperative fluorine-18-fluorodeoxyglucose positron-emission tomography (¹⁸F-FDG PET) scan for determining overall survival (OS) in breast cancer (BC) patients is controversial.

AIM

To evaluate the OS predictive value of preoperative PET positivity after 15 years.

METHODS

We performed a retrospective search of the Universitair Ziekenhuis Brussel patient database for nonmetastatic patients who underwent preoperative PET between 2002-2008. PET positivity was determined by anatomical region of interest (AROI) findings for breast and axillary, sternal, and distant sites. The prognostic role of PET was examined as a qualitative binary factor (positive vs negative status) and as a continuous variable [maximum standard uptake value (SUVmax)] in multivariate survival analyses using Cox proportional hazards models. Among the 104 identified patients who received PET, 36 were further analyzed for the SUVmax in the AROI.

RESULTS

Poor OS within the 15-year study period was predicted by PET-positive status for axillary (P = 0.033), sternal (P = 0.033), and combined PET-axillary/sternal (P = 0.008) nodes. Poor disease-free survival was associated with PET-positive axillary status (P = 0.040) and combined axillary/sternal status (P = 0.023). Cox models confirmed the long-term prognostic value of combined PET-axillary/sternal status [hazard ratio (HR): 3.08, 95% confidence interval: 1.42-6.69]. SUVmax of ipsilateral breast and axilla as continuous covariates were significant predictors of long-term OS with HRs of 1.25 (P = 0.048) and 1.54 (P = 0.029), corresponding to relative increase in the risk of death of 25% and 54% per SUVmax unit, respectively. In addition, the ratio of the ipsilateral axillary SUVmax over the contralateral axillary SUVmax was the most significant OS predictor (P = 0.027), with 1.94 HR, indicating a two-fold relative increase of mortality risk.

CONCLUSION

Preoperative PET is valuable for prediction of long-term survival. Ipsilateral axillary SUVmax ratio over the uninvolved side represents a new prognostic finding that warrants further investigation.

Distinct brain dysfunctions underlying visuo-constructive deficit in DLB and AD

Visuo-constructive abilities are a multicomponential process that can be impaired in several neurodegenerative dementias. Among visuo-constructive tasks, the Rey-Osterrieth Complex Figure-copy (ROCF-c) is the most commonly used and it seems influenced by different skills mediated by specific brain regions. This task complexity allows exploring differential mechanisms of impairment in different neurodegenerative disorders. In this study we examined the neuroanatomical substrates of ROCF-c performance in patients with Dementia with Lewy Bodies (DLB) and patients with Alzheimer's disease (AD). We included forty-five patients with probable DLB, and thirty-four patients with probable typical-AD. To identify the ROCF-c scores neural correlates we performed a regression analysis with brain hypometabolism using the ROCF-c scores as independent variable. Then we evaluated the correlation between regional hypometabolism and ROCF-c scores in each group separately, throughout offline Pearson correlation analysis. The DLB and AD groups differed only in visuo-constructive and memory performances. DLB patients performed worse at the visuo-constructive test, while typical-AD patients performed worse at the verbal memory task. In DLB, worse performance at ROCF-c scores (more severe visuo-constructive impairment) correlated with occipital and temporo-parietal hypometabolism. In AD, worse performance at ROCF-c score was associated with brain hypometabolism in the temporo-parietal regions. The inability to correctly perform the ROCF-c derives from distinct brain dysfunctions in DLB and AD. The present results suggest alterations in visuoperceptual processes due to the severe occipital hypometabolism in DLB, and in visuospatial processes related to temporo-parietal hypometabolism in AD.

Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option?

Biomarkers are playing a progressively leading role in both clinical practice and scientific research in dementia. Although amyloid and tau biomarkers have gained ground in the clinical community in recent years, neurodegeneration biomarkers continue to play a key role due to their ability to identify different patterns of brain involvement that sign the transition between asymptomatic and symptomatic stages of the disease with high sensitivity and specificity. Both ¹⁸F-FDG positron emission tomography (PET) and perfusion single photon emission computed tomography (SPECT) have proved useful to reveal the functional alterations underlying various neurodegenerative diseases. Although the focus of nuclear neuroimaging has shifted to PET, the lower cost and wider availability of SPECT make it a still valid alternative for the study of patients with dementia. This review discusses the principles of both techniques, compares their diagnostic performance for the diagnosis of neurodegenerative diseases and highlights the role of SPECT to characterize patients from low- and middle-income countries, where special care of additional costs is particularly needed to meet the new recommendations for the diagnosis and characterization of patients with dementia.

Primary Progressive Aphasia: Natural History in an Italian Cohort

BACKGROUND/AIMS

Few longitudinal studies have explored the progression of cognitive and functional impairment of patients with primary progressive aphasia (PPA). The aims of the study were to describe the clinical, neuroimaging, and genetic features of a cohort of 68 PPA patients, and to outline the natural history of the disease.

MATERIALS AND METHODS

A sample of 23 patients with the logopenic variant, 26 with the nonfluent/agrammatic variant, and 19 with the semantic variant was retrospectively collected and followed-up for a maximum of 6 years. Clinical-neuropsychological assessment, fluorodeoxyglucose positron emission tomographic imaging, and genetic analyses were acquired at baseline. Disease progression was evaluated in terms of language impairment, global cognitive decline, and functional dependency.

RESULTS

During follow-up, one third of subjects presented total language loss, and 20% severe functional dependency. Global cognitive decline after the first year (hazard ratio, 5.93; confidence interval, 1.63-21.56) and high schooling (hazard ratio, 0.07; confidence interval, 0.008-0.74) represented risk factors for functional impairment. The apolipoprotein E status was associated with the progression of cognitive decline. Positive family history for dementia was frequent and 3 genetic autosomal dominant mutations were identified.

CONCLUSIONS

There were no differences in the progression of PPA subtypes. Genetics plays an important role in disease onset and progression.

Two distinct pathological substrates associated with MMSE-pentagons item deficit in DLB and AD

INTRODUCTION

Dementia with Lewy Bodies (DLB) is characterized by a prominent deficit in visuospatial abilities. Visuospatial impairment is also detectable in the course of Alzheimer's dementia (AD). However, visuospatial impairment presents some differences in these two conditions, suggesting pathological involvement of distinct brain circuits. Recent studies applied a new method to score the Mini Mental State Examination (MMSE) pentagon copy subtest, namely the Qualitative Scoring Pentagon Test (QSPT), which is a sensitive measure of visuospatial abilities. Using [¹⁸F]fluorodeoxy-glucose positron emission tomography (FDG-PET), we assessed the relationship between in vivo brain metabolic dysfunction and visuospatial deficits, in terms of QSPT total value, in DLB and AD.

MATERIALS AND METHODS

Sixty Patients were diagnosed as DLB (n = 35) and AD (n = 25) dementia according with the standard research diagnostic criteria. Each patient underwent a FDG-PET scan as support for the final diagnosis. Patients underwent an extended neuropsychological evaluation, including MMSE, language, memory, executive functions and visuospatial abilities tests. The MMSE QSPT scoring was calculated following the methods by Caffarra et al. (2013). Offline voxel-wise correlation analysis between QSPT total scores and FDG-PET brain metabolism was then performed, correcting for MMSE, sex and disease duration.

RESULTS

Both groups presented reduced visuospatial performances, as assessed by QSPT scores. DLB compared to AD showed a statistically significant difference in QSPT rotation parameter (p = 0.022). In DLB, worse performance at QSPT total score, i.e. more severe visuospatial impairment, correlated with brain occipital hypometabolism (i.e. lateral occipital cortex, calcarine cortex, fusiform and lingual gyri). In AD, worse performance at QSPT total score correlated with brain hypometabolism in the right parietal cortex (i.e. superior and inferior parietal cortex and angular gyrus).

DISCUSSION

These findings reveal that visuospatial deficits may derive from distinct brain alterations in AD and DLB. We propose that the inabilities to perform correctly the QSPT task is related to altered visuoperceptual process in DLB, and visuospatial process in AD. This is consistent with our results showing hypometabolism in brain system related to visuoperceptual processing, namely the occipital cortex in DLB, and visuospatial processing, namely parietal cortex in AD.

Variant-specific vulnerability in metabolic connectivity and resting-state networks in behavioural variant of frontotemporal dementia

Brain connectivity measures represent candidate biomarkers of neuronal dysfunction in neurodegenerative diseases. Previous findings suggest that the behavioural variant of frontotemporal dementia (bvFTD) and its variants (i.e., frontal and temporo-limbic) may be related to the vulnerability of distinct functional connectivity networks. In this study, 82 bvFTD patients were included, and two patient groups were identified as frontal and temporo-limbic bvFTD variants. Two advanced multivariate analytical approaches were applied to FDG-PET data, i.e., sparse inverse covariance estimation (SICE) method and seed-based interregional correlation analysis (IRCA). These advanced methods allowed the assessment of (i) the whole-brain metabolic connectivity, without any a priori assumption, and (ii) the main brain resting-state networks of crucial relevance for cognitive and behavioural functions. In the whole bvFTD group, we found dysfunctional connectivity patterns in frontal and limbic regions and in all major brain resting-state networks as compared to healthy controls (HC N = 82). In the two bvFTD variants, SICE and IRCA analyses identified variant-specific reconfigurations of whole-brain connectivity and resting-state networks. Specifically, the frontal bvFTD variant was characterised by metabolic connectivity alterations in orbitofrontal regions and anterior resting-state networks, while the temporo-limbic bvFTD variant was characterised by connectivity alterations in the limbic and salience networks. These results highlight different neural vulnerabilities in the two bvFTD variants, as shown by the dysfunctional connectivity patterns, with relevance for the different neuropsychological profiles. This new evidence provides further insight in the variability of bvFTD and may contribute to a more accurate classification of these patients.

The Role of Single-Subject Brain Metabolic Patterns in the Early Differential Diagnosis of Primary Progressive Aphasias and in Prediction of Progression to Dementia

Background and Objective: Primary progressive aphasia (PPA) is a clinical syndrome due to different neurodegenerative conditions in which an accurate early diagnosis needs to be supported by a reliable diagnostic tool at the individual level. In this study, we investigated in PPA the FDG-PET brain metabolic patterns at the single-subject level, in order to assess the case-to-case variability and its relationship with clinical-neuropsychological findings.

Material and Methods: 55 patients (i.e., 11 semantic variant/sv-PPA, 19 non fluent variant/nfv-PPA, 17 logopenic variant/lv-PPA, 3 slowly progressive anarthria/SPA, and 5 mixed PPA/m-PPA) were included. Clinical-neuropsychological information and FDG-PET data were acquired at baseline. A follow-up of 27.4±12.55 months evaluated the clinical progression. Brain metabolism was analyzed using an optimized and validated voxel-based SPM method at the single-subject level.

Results: FDG-PET voxel-wise metabolic assessment revealed specific metabolic signatures characterizing each PPA variant at the individual level, reflecting the underlying neurodegeneration in language networks. Notably, additional dysfunctional patterns predicted clinical progression to specific dementia conditions. In the case of nfv-PPA, a metabolic pattern characterized by involvement of parietal, subcortical and brainstem structures predicted progression to a corticobasal degeneration syndrome or to progressive supranuclear palsy. lv-PPA and sv-PPA cases who progressed to Alzheimer’s disease and frontotemporal dementia at the follow-up presented with extended bilateral patterns at baseline.

Discussion: Our results indicate that FDG-PET voxel-wise imaging is a valid biomarker for the early differential diagnosis of PPAs and for the prediction of progression to specific dementia condition. This study supports the use of FDG-PET imaging quantitative assessment in clinical settings for a better characterization of PPA individuals and prognostic definition of possible endo-phenotypes.

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort

Background/aims

In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias.

Methods

We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as "typical-AD", "atypical-AD" (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), "non-AD" (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or "negative" patterns. To perform the statistical analyses, the individual patterns were grouped either as "AD dementia vs. non-AD dementia (all diseases)" or as "FTD vs. non-FTD (all diseases)". Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated.

Results

The multivariate logistic model identified FDG-PET "AD" SPM classification (Expβ = 19.35, 95% C.I. 4.8-77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64-25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The "FTD" SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1-63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55-70.46, p < 0.001).

Conclusions

Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers.

The emerging role of PET imaging in dementia

A compelling need in the field of neurodegenerative diseases is the development and validation of biomarkers for early identification and differential diagnosis. The availability of positron emission tomography (PET) neuroimaging tools for the assessment of molecular biology and neuropathology has opened new venues in the diagnostic design and the conduction of new clinical trials. PET techniques, allowing the in vivo assessment of brain function and pathology changes, are increasingly showing great potential in supporting clinical diagnosis also in the early and even preclinical phases of dementia. This review will summarize the most recent evidence on fluorine-18 fluorodeoxyglucose-, amyloid -, tau -, and neuroinflammation - PET tools, highlighting strengths and limitations and possible new perspectives in research and clinical applications. Appropriate use of PET tools is crucial for a prompt diagnosis and target evaluation of new developed drugs aimed at slowing or preventing dementia.

Evaluation of an optimized [18 F]fluoro-deoxy-glucose positron emission tomography voxel-wise method to early support differential diagnosis in atypical Parkinsonian disorders

BACKGROUND AND PURPOSE

Atypical Parkinsonian disorders (APD) frequently overlap in clinical presentations, making the differential diagnosis challenging in the early stages. The present study aimed to evaluate the accuracy of the [¹⁸ F]fluoro-deoxy-glucose positron emission tomography Statistical Parametric Mapping (SPM) optimized procedure in supporting the early and differential diagnosis of APD.

METHODS

Seventy patients with possible APD were retrospectively included from a large clinical cohort. The included patients underwent [¹⁸ F]fluoro-deoxy-glucose positron emission tomography within 3 months of the first clinical assessment and a diagnostic follow-up. An optimized SPM voxel-wise procedure was used to produce t-maps of brain hypometabolism in single subjects, which were classified by experts blinded to any clinical information. We compared the accuracy of both the first clinical diagnosis and the SPM t-map classifications with the diagnosis at follow-up as the reference standard.

RESULTS

At first diagnosis, 60% of patients were classified as possible APD (progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies, multiple system atrophy) and about 40% as APD with uncertain diagnosis, providing 52% sensitivity, 97% specificity and 86% accuracy with respect to the reference standard. SPM t-map classification showed 98% sensitivity, 99% specificity and 99% accuracy, and a significant agreement with the diagnosis at follow-up (P < 0.001).

CONCLUSIONS

The SPM t-map classification at entry predicted the second diagnosis at follow-up. This indicates its significantly superior role for an early identification of APD subtypes, particularly in cases of uncertain diagnosis. The use of a metabolic biomarker at entry in the instrumental work-up of APD may shorten the diagnostic time, producing benefits for treatment options and support to the patients.

Neuropsychiatric subsyndromes and brain metabolic network dysfunctions in early onset Alzheimer's disease

Neuropsychiatric symptoms (NPSs) often occur in early-age-of-onset Alzheimer's disease (EOAD) and cluster into sub-syndromes (SSy). The aim of this study was to investigate the association between 18 F-FDG-PET regional and connectivity-based brain metabolic dysfunctions and neuropsychiatric SSy. NPSs were assessed in 27 EOAD using the Neuropsychiatric Inventory and further clustered into four SSy (apathetic, hyperactivity, affective, and psychotic SSy). Eighty-five percent of EOAD showed at least one NPS. Voxel-wise correlations between SSy scores and brain glucose metabolism (assessed with 18 F-FDG positron emission tomography) were studied. Interregional correlation analysis was used to explore metabolic connectivity in the salience (aSN) and default mode networks (DMN) in a larger sample of EOAD (N = 51) and Healthy Controls (N = 57). The apathetic, hyperactivity, and affective SSy were highly prevalent (>60%) as compared to the psychotic SSy (33%). The hyperactivity SSy scores were associated with increase of glucose metabolism in frontal and limbic structures, implicated in behavioral control. A comparable positive correlation with part of the same network was found for the affective SSy scores. On the other hand, the apathetic SSy scores were negatively correlated with metabolism in the bilateral orbitofrontal and dorsolateral frontal cortex known to be involved in motivation and decision-making processes. Consistent with these SSy regional correlations with brain metabolic dysfunction, the connectivity analysis showed increases in the aSN and decreases in the DMN. Behavioral abnormalities in EOAD are associated with specific dysfunctional changes in brain metabolic activity, in particular in the aSN that seems to play a crucial role in NPSs in EOAD. Hum Brain Mapp 37:4234-4247, 2016. © 2016 Wiley Periodicals, Inc.

Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia

[¹⁸F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [¹⁸F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. Their scans were compared to a large normal reference dataset developed and validated for comparison at single-subject level. Additionally, Aβ42 and Tau CSF values were available in 34 MCI subjects. Clinical follow-up (mean 28.5 ± 7.8 months) assessed subsequent progression to AD or non-AD dementias. The SPM analysis showed: 1) normal brain metabolism in 14 MCI cases, none of them progressing to dementia; 2) the typical temporo-parietal pattern suggestive for prodromal AD in 15 cases, 11 of them progressing to AD; 3) brain hypometabolism suggestive of frontotemporal lobar degeneration (FTLD) subtypes in 7 and dementia with Lewy bodies (DLB) in 2 subjects (all fulfilled FTLD or DLB clinical criteria at follow-up); and 4) 7 MCI cases showed a selective unilateral or bilateral temporo-medial hypometabolism without the typical AD pattern, and they all remained stable. In our sample, objective voxel-based analysis of [¹⁸F]FDG-PET scans showed high predictive prognostic value, by identifying either normal brain metabolism or hypometabolic patterns suggestive of different underlying pathologies, as confirmed by progression at follow-up. These data support the potential usefulness of this SPM [¹⁸F]FDG PET analysis in the early dementia diagnosis and for improving subject selection in clinical trials based on MCI definition.

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We used an optimized voxel-based single-subject [¹⁸F]FDG-PET analysis

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We showed different hypometabolic patterns (AD and non-AD) underlying MCI condition

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Heterogeneous PET profiles predicted progression into specific dementia subtypes.

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Statistical analyses showed high positive and negative post-test probability values.

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CSF findings agreed with [¹⁸F]FDG-PET imaging in single cases.