Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments

Clinical application of sparse canonical correlation analysis to detect genetic associations with cortical thickness in Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cerebral cortex atrophy. In this study, we used sparse canonical correlation analysis (SCCA) to identify associations between single nucleotide polymorphisms (SNPs) and cortical thickness in the Korean population. We also investigated the role of the SNPs in neurological outcomes, including neurodegeneration and cognitive dysfunction.

Methods

We recruited 1125 Korean participants who underwent neuropsychological testing, brain magnetic resonance imaging, positron emission tomography, and microarray genotyping. We performed group-wise SCCA in Aβ negative (-) and Aβ positive (+) groups. In addition, we performed mediation, expression quantitative trait loci, and pathway analyses to determine the functional role of the SNPs.

Results

We identified SNPs related to cortical thickness using SCCA in Aβ negative and positive groups and identified SNPs that improve the prediction performance of cognitive impairments. Among them, rs9270580 was associated with cortical thickness by mediating Aβ uptake, and three SNPs (rs2271920, rs6859, rs9270580) were associated with the regulation of CHRNA2, NECTIN2, and HLA genes.

Conclusion

Our findings suggest that SNPs potentially contribute to cortical thickness in AD, which in turn leads to worse clinical outcomes. Our findings contribute to the understanding of the genetic architecture underlying cortical atrophy and its relationship with AD.

The Early Perfusion Image Is Useful to Support the Visual Interpretation of Brain Amyloid-PET With 18F-Flutemetamol in Borderline Cases

PURPOSE

Visual interpretation of brain amyloid-β (Aβ) PET can be difficult in individuals with borderline Aβ burden. Coregistration with individual MRI is recommended in these cases, which, however, is not always available. This study evaluated coregistration with the early perfusion frames acquired immediately after tracer injection to support the visual interpretation of the late Aβ-frames in PET with 18F-flutemetamol (FMM).

PATIENTS AND METHODS

Fifty dual-time-window FMM-PET scans of cognitively normal subjects with 0 to 60 Centiloids were included retrospectively (70.1 ± 6.9 years, 56% female, MMSE score 28.9 ± 1.3, 42% APOE ɛ4 carrier). Regional Aβ load was scored with respect to a 6-point Likert scale by 3 independent raters in the 10 regions of interest recommended for FMM reading using 3 different settings: Aβ image only, Aβ image coregistered with MRI, and Aβ image coregistered with the perfusion image. The impact of setting, within- and between-readers variability, region of interest, and Aβ-status was tested by repeated-measure analysis of variance of the Likert score.

RESULTS

The Centiloid scale ranged between 2 and 52 (interquartile range, 7-19). Support of visual scoring by the perfusion image resulted in the best discrimination between Aβ-positive and Aβ-negative cases, mainly by improved certainty of excluding Aβ plaques in Aβ-negative cases (P = 0.030). It also resulted in significantly higher between-rater agreement. The setting effect was most pronounced in the frontal lobe and in the posterior cingulate cortex/precuneus area (P = 0.005).

CONCLUSIONS

The early perfusion image is a suitable alternative to T1-weighted MRI to support the visual interpretation of the late Aβ image in FMM-PET.

New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map

OBJECTIVE

This study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses.

METHODS

Twenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual's CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects.

RESULTS

In visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern.

CONCLUSIONS

Our new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual's brain CT can be of great help for the visual assessment.

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BACKGROUND

Head motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.

METHODS

FMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.

RESULTS

In total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).

CONCLUSIONS

The motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

Predicting amyloid-PET status in a memory clinic: The role of the novel antero-posterior index and visual rating scales

INTRODUCTION

Visual rating scales are increasingly utilized in clinical practice to assess atrophy in crucial brain regions among patients with cognitive disorders. However, their capacity to predict Alzheimer's disease (AD)-related pathology remains unexplored, particularly within a heterogeneous memory clinic population. This study aims to assess the accuracy of a novel visual rating assessment, the antero-posterior index (API) scale, in predicting amyloid-PET status. Furthermore, the study seeks to determine the optimal cohort-based cutoffs for the medial temporal atrophy (MTA) and parietal atrophy (PA) scales and to integrate the main visual rating scores into a predictive model.

METHODS

We conducted a retrospective analysis of brain MRI and high-resolution TC scans from 153 patients with cognitive disorders who had undergone amyloid-PET assessments due to suspected AD pathology in a real-world memory clinic setting.

RESULTS

The API scale (cutoff ≥1) exhibited the highest accuracy (AUC = 0.721) among the visual rating scales. The combination of the cohort-based MTA and PA threshold with the API yielded favorable accuracy (AUC = 0.787). Analyzing a cohort of MCI/Mild dementia patients below 75 years of age, the API scale and the predictive model improved their accuracy (AUC = 0.741 and 0.813, respectively), achieving excellent results in the early-onset population (AUC = 0.857 and 0.949, respectively).

CONCLUSION

Our study emphasizes the significance of visual rating scales in predicting amyloid-PET positivity within a real-world memory clinic. Implementing the novel API scale, alongside our cohort-based MTA and PA thresholds, has the potential to substantially enhance diagnostic accuracy.

Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods

PURPOSE

Amyloid positron emission tomography (PET) with [18F]florbetaben (FBB) is an established tool for detecting Aβ deposition in the brain in vivo based on visual assessment of PET scans. Quantitative measures are commonly used in the research context and allow continuous measurement of amyloid burden. The aim of this study was to demonstrate the robustness of FBB PET quantification.

METHODS

This is a retrospective analysis of FBB PET images from 589 subjects. PET scans were quantified with 15 analytical methods using nine software packages (MIMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ) that used several metrics to estimate Aβ load (SUVR, centiloid, amyloid load, and amyloid index). Six analytical methods reported centiloid (MIMneuro, standard centiloid, Neurology Toolkit, SPM8 (PET only), CapAIBL, NMF). All results were quality controlled.

RESULTS

The mean sensitivity, specificity, and accuracy were 96.1 ± 1.6%, 96.9 ± 1.0%, and 96.4 ± 1.1%, respectively, for all quantitative methods tested when compared to histopathology, where available. The mean percentage of agreement between binary quantitative assessment across all 15 methods and visual majority assessment was 92.4 ± 1.5%. Assessments of reliability, correlation analyses, and comparisons across software packages showed excellent performance and consistent results between analytical methods.

CONCLUSION

This study demonstrated that quantitative methods using both CE marked software and other widely available processing tools provided comparable results to visual assessments of FBB PET scans. Software quantification methods, such as centiloid analysis, can complement visual assessment of FBB PET images and could be used in the future for identification of early amyloid deposition, monitoring disease progression and treatment effectiveness.

Deep learning application for the classification of Alzheimer's disease using 18F-flortaucipir (AV-1451) tau positron emission tomography

The positron emission tomography (PET) with 18F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively unimpaired (CU) individuals. This study aimed to evaluate the utility of 18F-flortaucipir-PET images and multimodal data integration in the differentiation of CU from MCI or AD through DL. We used cross-sectional data (18F-flortaucipir-PET images, demographic and neuropsychological score) from the ADNI. All data for subjects (138 CU, 75 MCI, 63 AD) were acquired at baseline. The 2D convolutional neural network (CNN)-long short-term memory (LSTM) and 3D CNN were conducted. Multimodal learning was conducted by adding the clinical data with imaging data. Transfer learning was performed for classification between CU and MCI. The AUC for AD classification from CU was 0.964 and 0.947 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.947, and 0.976 in multimodal learning. The AUC for MCI classification from CU had 0.840 and 0.923 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.845, and 0.850 in multimodal learning. The 18F-flortaucipir PET is effective for the classification of AD stage. Furthermore, the effect of combination images with clinical data increased the performance of AD classification.

Imaging of Tauopathies with PET Ligands: State of the Art and Future Outlook

(1) Background: Tauopathies are a group of diseases characterized by the deposition of abnormal tau protein. They are distinguished into 3R, 4R, and 3R/4R tauopathies and also include Alzheimer's disease (AD) and chronic traumatic encephalopathy (CTE). Positron emission tomography (PET) imaging represents a pivotal instrument to guide clinicians. This systematic review aims to summarize the current and novel PET tracers. (2) Methods: Literature research was conducted on Pubmed, Scopus, Medline, Central, and the Web of Science using the query "pet ligands" and "tauopathies". Articles published from January 2018 to 9 February, 2023, were searched. Only studies on the development of novel PET radiotracers for imaging in tauopathies or comparative studies between existing PET tracers were included. (3) Results: A total of 126 articles were found, as follows: 96 were identified from PubMed, 27 from Scopus, one on Central, two on Medline, and zero on the Web of Science. Twenty-four duplicated works were excluded, and 63 articles did not satisfy the inclusion criteria. The remaining 40 articles were included for quality assessment. (4) Conclusions: PET imaging represents a valid instrument capable of helping clinicians in diagnosis, but it is not always perfect in differential diagnosis, even if further investigations on humans for novel promising ligands are needed.

Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography

BACKGROUND

The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from ¹⁸F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage.

RESULTS

Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU.

CONCLUSIONS

The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.

Classification of Aβ State From Brain Amyloid PET Images Using Machine Learning Algorithm

Background and Purpose

Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images.

Methods

A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores.

Results

The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03).

Conclusions

Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

Quantitative comparative analysis of amyloid PET images using three radiopharmaceuticals

OBJECTIVE

Amyloid positron emission tomography (PET) with F-18 florbetaben (FBB), F-18 flutemetamol (FMM), and F-18 florapronol (FPN) is being used clinically for the evaluation of dementia. These radiopharmaceuticals are commonly used to evaluate the accumulation of beta-amyloid plaques in the brain, but there are structural differences between them. We investigated whether there are any differences in the imaging characteristics.

METHODS

A total of 605 subjects were enrolled retrospectively in this study, including healthy subjects (HS) and patients with mild cognitive impairment or Alzheimer's disease. Participants underwent amyloid PET imaging using one of the three radiopharmaceuticals. The PET images were analyzed visually and semi-quantitatively using a standardized uptake value ratio (SUVR). In addition, we calculated and compared the cut-off SUVR of the representative regions for each radiopharmaceutical that can distinguish between positive and negative scans.

RESULTS

In the negative images of the HS group, the contrast between the white matter and the gray matter was high in the FMM PET images, while striatal uptake was relatively higher in the FPN PET images. The SUVR showed significant differences across the radiopharmaceuticals in all areas except the temporal lobe, but the range of differences was relatively small. Accuracy levels for the global cut-off SUVR to discriminate between positive and negative images were highest in FMM PET, with a value of 0.989. FBB PET also showed a high value of 0.978, while FPN PET showed a relatively low value of 0.901.

CONCLUSIONS

Negative amyloid PET images using the three radiopharmaceuticals showed visually and quantitatively similar imaging characteristics except in the striatum. Binary classification using the cut-off of the global cortex showed high accuracy overall, although there were some differences between the three PET images.

Longitudinal changes in 18F-Flutemetamol amyloid load in cognitively intact APOE4 carriers versus noncarriers: Methodological considerations

PURPOSE

Measuring longitudinal changes in amyloid load in the asymptomatic stage of Alzheimer's disease is of high relevance for clinical research and progress towards more efficacious, timely treatments. Apolipoprotein E ε4 (APOE4) has a well-established effect on the rate of amyloid accumulation. Here we investigated which region of interest and which reference region perform best at detecting the effect of APOE4 on longitudinal amyloid load in individuals participating in the Flemish Prevent Alzheimer's Disease Cohort KU Leuven (F-PACK).

METHODS

Ninety cognitively intact F-PACK participants (baseline age: 68 (52-80) years, 46 males, 42 APOE4 carriers) received structural MRI and ¹⁸F-Flutemetamol PET scans at baseline and follow-up (6.2 (3.4-10.9) year interval). Standardised uptake value ratios (SUVRs) and Centiloids (CLs) were calculated in a composite cortical volume of interest (SUVR_comp/CL) and in the precuneus (SUVR_prec), and amyloid rate of change derived: (follow-up amyloid load - baseline amyloid load) / time interval (years). Four reference regions were used to derive amyloid load: whole cerebellum, cerebellar grey matter, eroded subcortical white matter, and pons.

RESULTS

When using whole cerebellum or cerebellar grey matter as reference region, APOE4 carriers had a significantly higher SUVR_comp amyloid rate of change than non-carriers (p_corr = 0.004, t = 3.40 (CI 0.005-0.018); p_corr = 0.036, t = 2.66 (CI 0.003-0.018), respectively). Significance was not observed for eroded subcortical white matter or pons (p_corr = 0.144, t = 2.13 (CI 0.0003-0.008); p_corr = 0.116, t = 2.22 (CI 0.005-0.010), respectively). When using CLs as the amyloid measurement, and whole cerebellum, APOE4 carriers had a higher amyloid rate of change than non-carriers (p_corr = 0.012, t = 3.05 (CI 0.499-2.359)). Significance was not observed for the other reference regions. No significance was observed with any of the reference regions and amyloid rate of change in the precuneus (SUVR_prec).

CONCLUSION

In this cognitively intact cohort, a composite neocortical volume of interest together with whole cerebellum or cerebellar grey matter as reference region are the methods of choice for detecting APOE4-dependent differences in amyloid rate of change.

Development and clinical validation of CT-based regional modified Centiloid method for amyloid PET

Background

The standard Centiloid (CL) method was proposed to harmonize and quantify global ¹⁸F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of ¹⁸F-florbetaben (FBB) and ¹⁸F-flutemetamol (FMM) Centiloid (rdcCL).

Methods

For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer’s disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD).

Results

Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R² = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R² = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05).

Conclusions

Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01099-0.

Computed Tomography Density and β-Amyloid Deposition of Intraorbital Optic Nerve May Assist in Diagnosing Mild Cognitive Impairment and Alzheimer's Disease: A 18F-Flutemetamol Positron Emission Tomography/Computed Tomography Study

Objective

The aim was to study whether the computed tomography (CT) density and β-amyloid (Aβ) level of intraorbital optic nerve could assist in diagnosing mild cognitive impairment (MCI) and Alzheimer's disease (AD).

Methods

A total of sixty subjects were recruited in our study, including nine normal control (NC) subjects (i.e., 4 men and 5 women), twenty four MCI subjects (i.e., 11 men and 13 women), and twenty seven AD subjects (i.e., 14 men and 13 women). All subjects conducted 18F-flutemetamol amyloid positron emission tomography (PET)/CT imaging. Blinded to the clinical information of the subjects, two physicians independently measured and calculated the standardized uptake value ratio (SUVR) of the bilateral occipital cortex, SUVR of the bilateral intraorbital optic nerve, and CT density of the bilateral intraorbital optic nerve by using GE AW 4.5 Workstation.

Results

Between AD and NC groups, the differences of the bilateral intraorbital optic nerve SUVR were statistically significant; between AD and MCI groups, the differences of the left intraorbital optic nerve SUVR were statistically significant. Between any two of the three groups, the differences in the bilateral intraorbital optic nerve density were statistically significant. The bilateral occipital SUVR was positively correlated with the bilateral intraorbital optic nerve SUVR and negatively correlated with the bilateral intraorbital optic nerve density. Bilateral intraorbital optic nerve SUVR was negatively correlated with the bilateral intraorbital optic nerve density. The area under the receiver operating characteristic (ROC) curve of multiple logistic regression was 0.9167 (for MCI vs. NC) and 0.8951 (for AD vs. MCI). The Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores were positively associated with the intraorbital optic nerve density and were negatively associated with the intraorbital optic nerve SUVR. The regression equation of MoCA was y = 16.37-0.9734 × x 1 + 0.5642 × x 2-3.127 × x 3 + 0.0275 × x 4; the R 2 was 0.848. The regression equation of MMSE was y = 19.57-1.633 × x 1 + 0.4397 × x 2-1.713 × x 3 + 0.0424 × x 4; the R 2 was 0.827.

Conclusion

The CT density and Aβ deposition of the intraorbital optic nerve were associated with Aβ deposition of the occipital cortex and the severity of cognitive impairment. The intraorbital optic nerve CT density and intraorbital optic nerve Aβ deposition could assist in diagnosing MCI and AD.

18F-THK5351 PET Positivity and Longitudinal Changes in Cognitive Function in β-Amyloid-Negative Amnestic Mild Cognitive Impairment

PURPOSE

Neuroinflammation is considered an important pathway associated with several diseases that result in cognitive decline. ¹⁸F-THK5351 positron emission tomography (PET) signals might indicate the presence of neuroinflammation, as well as Alzheimer's disease-type tau aggregates. β-amyloid (Aβ)-negative (Aβ-) amnestic mild cognitive impairment (aMCI) may be associated with non-Alzheimer's disease pathophysiology. Accordingly, we investigated associations between ¹⁸F-THK5351 PET positivity and cognitive decline among Aβ- aMCI patients.

MATERIALS AND METHODS

The present study included 25 amyloid PET negative aMCI patients who underwent a minimum of two follow-up neuropsychological evaluations, including clinical dementia rating-sum of boxes (CDR-SOB). The patients were classified into two groups: ¹⁸F-THK5351-positive and -negative groups. The present study used a linear mixed effects model to estimate the effects of ¹⁸F-THK5351 PET positivity on cognitive prognosis among Aβ- aMCI patients.

RESULTS

Among the 25 Aβ- aMCI patients, 10 (40.0%) were ¹⁸F-THK5351 positive. The patients in the ¹⁸F-THK5351-positive group were older than those in the ¹⁸F-THK5351-negative group (77.4±2.2 years vs. 70.0±5.5 years; p<0.001). There was no difference between the two groups with regard to the proportion of apolipoprotein E ε4 carriers. Interestingly, however, the CDR-SOB scores of the ¹⁸F-THK5351-positive group deteriorated at a faster rate than those of the ¹⁸F-THK5351-negative group (B=0.003, p=0.033).

CONCLUSION

The results of the present study suggest that increased ¹⁸F-THK5351 uptake might be a useful predictor of poor prognosis among Aβ- aMCI patients, which might be associated with increased neuroinflammation (ClinicalTrials.gov NCT02656498).

Longitudinal Amyloid Cognitive Composite in Preclinical Alzheimer's Disease

BACKGROUND

Previous studies have developed several cognitive composites in preclinical AD. However, more sensitive measures to track cognitive changes and therapeutic efficacy in preclinical Alzheimer's disease (AD) are needed considering diverse sociocultural and linguistic backgrounds. This study developed a composite score that can sensitively detect the Aβ-related cognitive trajectory of preclinical AD using Korean data.

METHODS

A total of 196 cognitively normal (CN) participants who underwent amyloid positron emission tomography were followed-up with neuropsychological assessments. We developed the Longitudinal Amyloid cognitive Composite in Preclinical AD (LACPA) using the linear mixed-effects model (LMM) and z-scores. The LMM was also used to investigate the longitudinal sensitivity of LACPA and the association between time-varying brain atrophy and LACPA.

RESULTS

Considering the group-time interaction effects of each subtest, the Seoul Verbal Learning Test-Elderly's version (SVLT-E) immediate recall/delayed recall/recognition, the Korean Trail Making Test B time, and the Korean Mini-Mental State Examination were selected as components of LACPA. LACPA exhibited a significant group-time interaction effect between the Aβ+ and Aβ- groups (t = -3.288, p = 0.001). Associations between time-varying LACPA and brain atrophy were found in the bilateral medial temporal, right lateral parietal, and right lateral frontal regions, and hippocampal volume.

CONCLUSION

LACPA may contribute to reduction in time and financial burden when monitoring Aβ-related cognitive decline and therapeutic efficacy of the disease-modifying agents specifically targeting Aβ in secondary prevention trials.

Performance of the plasma Aβ42/Aβ40 ratio, measured with a novel HPLC-MS/MS method, as a biomarker of amyloid PET status in a DPUK-KOREAN cohort

Background

We assessed the feasibility of plasma Aβ42/Aβ40 determined using a novel liquid chromatography-mass spectrometry method (LC-MS) as a useful biomarker of PET status in a Korean cohort from the DPUK Study.

Methods

A total of 580 participants belonging to six groups, Alzheimer’s disease dementia (ADD, n = 134), amnestic mild cognitive impairment (aMCI, n = 212), old controls (OC, n = 149), young controls (YC, n = 15), subcortical vascular cognitive impairment (SVCI, n = 58), and cerebral amyloid angiopathy (CAA, n = 12), were included in this study. Plasma Aβ40 and Aβ42 were quantitated using a new antibody-free, LC-MS, which drastically reduced the sample preparation time and cost. We performed receiver operating characteristic (ROC) analysis to develop the cutoff of Aβ42/Aβ40 and investigated its performance predicting centiloid-based PET positivity (PET+).

Results

Plasma Aβ42/Aβ40 were lower for PET+ individuals in ADD, aMCI, OC, and SVCI (p < 0.001), but not in CAA (p = 0.133). In the group of YC, OC, aMCI, and ADD groups, plasma Aβ42/Aβ40 predicted PET+ with an area under the ROC curve (AUC) of 0.814 at a cutoff of 0.2576. When adding age, APOE4, and diagnosis, the AUC significantly improved to 0.912.

Conclusion

Plasma Aβ42/Aβ40, as measured by this novel LC-MS method, showed good discriminating performance based on PET positivity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00911-7.

PET/MRI in paediatric disease

Nuclear medicine and molecular imaging have a small but growing role in the management of paediatric and neonatal diseases. During the past decade, combined PET/MRI has emerged as a clinically important hybrid imaging modality in paediatric medicine due to diagnostic advantages and reduced radiation exposure compared to alternative techniques. The applications for nuclear medicine, radiopharmaceuticals and combined PET/MRI in paediatric diagnosis is broadly similar to adults, however there are some key differences. There are a variety of clinical applications for PET/MRI imaging in children including, but not limited to, oncology, neurology, cardiovascular, infection and chronic inflammatory diseases, and in renal-urological disorders. In this article, we review the applications of PET/MRI in paediatric and neonatal imaging, its current role, advantages and disadvantages over other hybrid imaging techniques such as PET/CT, and its future applications. Overall, PET/MRI is a powerful imaging technology in diagnostic medicine and paediatric diseases. Higher soft tissue contrasts and lower radiation dose of the MRI makes it the superior technology compared to other conventional techniques such as PET/CT or scintigraphy. However, this relatively new hybrid imaging has also some limitations. MRI based attenuation correction remains a challenge and although methodologies have improved significantly in the last decades, most remain under development.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cost and safety issues.

OBJECTIVE

We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers.

METHODS

We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers).

RESULTS

Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity.

CONCLUSION

Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

Novel Tracers and Radionuclides in PET Imaging

The use of PET imaging agents in oncology, cardiovascular disease, and neurodegenerative disease shows the power of this technique in evaluating the molecular and biological characteristics of numerous diseases. These agents provide crucial information for designing therapeutic strategies for individual patients. Novel PET tracers are in continual development and many have potential use in clinical and research settings. This article discusses the potential applications of tracers in diagnostics, the biological characteristics of diseases, the ability to provide prognostic indicators, and using this information to guide treatment strategies including monitoring treatment efficacy in real time to improve outcomes and survival.

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.