Optimisation and usefulness of quantitative analysis of 18F-florbetapir PET

Fakhry-Darian, Daniel; Patel, Neva; Khan, Saba; Barwick, Tara; Svensson, W. E.; Khan, Sameer; Perry, Richard; Malhotra, Paresh; Carswell, Christopher; Nijran, K. S.; Win, Zarni

doi:10.1259/bjr.20181020

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…We found thresholds of 0.19, 0.23 and 0.29 for BP ND , and thresholds of 1.28, 1.34 and 1.43 for SUVr. Literature has generated inconsistent findings with respect to amyloid thresholds, ranging from 1.08 to 1.34 for SUVr, with 1.10 being reported most frequently [12,[18][19][20][21][22][23][42][43][44][45]. The large variability indicates that thresholds may to some extent rely on methodology, image processing pipeline used and study sample.…”

Section: Discussionmentioning

confidence: 99%

Grey zone amyloid burden affects memory function: the SCIENCe project

Ebenau

Verfaillie

Bosch

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose To determine thresholds for amyloid beta pathology and evaluate associations with longitudinal memory performance with the aim to identify a grey zone of early amyloid beta accumulation and investigate its clinical relevance. Methods We included 162 cognitively normal participants with subjective cognitive decline from the SCIENCe cohort (64 ± 8 years, 38% F, MMSE 29 ± 1). Each underwent a dynamic [ 18 F] florbetapir PET scan, a T1-weighted MRI scan and longitudinal memory assessments (RAVLT delayed recall, n = 655 examinations). PET scans were visually assessed as amyloid positive/ negative. Additionally, we calculated the mean binding potential (BP ND) and standardized uptake value ratio (SUVr 50-70) for an a priori defined composite region of interest. We determined six amyloid positivity thresholds using various data-driven methods (resulting thresholds: BP ND 0.19/0.23/0.29; SUVr 1.28/1.34/1.43). We used Cohen's kappa to analyse concordance between thresholds and visual assessment. Next, we used quantiles to divide the sample into two to five subgroups of equal numbers (median, tertiles, quartiles, quintiles), and operationalized a grey zone as the range between the thresholds (0.19-0.29 BP ND / 1.28-1.43 SUVr). We used linear mixed models to determine associations between thresholds and memory slope. Results As determined by visual assessment, 24% of 162 individuals were amyloid positive. Concordance with visual assessment was comparable but slightly higher for BP ND thresholds (range kappa 0.65-0.70 versus 0.60-0.63). All thresholds predicted memory decline (range beta − 0.29 to − 0.21, all p < 0.05). Analyses in subgroups showed memory slopes gradually became steeper with higher amyloid load (all p for trend < 0.05). Participants with a low amyloid burden benefited from a practice effect (i.e. increase in memory), whilst high amyloid burden was associated with memory decline. Memory slopes of individuals in the grey zone were intermediate. Conclusion We provide evidence that not only high but also grey zone amyloid burden subtly impacts memory function. Therefore, in case a binary classification is required, we suggest using a relatively low threshold which includes grey zone amyloid pathology.

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Section: Discussionmentioning

confidence: 99%

Grey zone amyloid burden affects memory function: the SCIENCe project

Ebenau

Verfaillie

Bosch

et al. 2020

Eur J Nucl Med Mol Imaging

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“…Magnetic resonance imaging (MRI) and FDG-PET images were selected for all subjects. Notably, among these participants, 26 NC and 32 SCD subjects were selected from the Tiantan Hospital (Capital Medical University; a subcenter of SILCODE project), and they additionally had amyloid-PET (Florbetapir F-18 [AV45]) images taken, in which 38.5% of the NC ( n = 10) and 37.5% of the SCD subjects ( n = 12) were classified as amyloid positivity according to a priori with the established cutoff of cortical standardized uptake value ratio (SUVR) > 1.18 [ 17 , 18 ]; the remaining subjects were all recruited from Xuanwu Hospital of Capital Medical University (center of SILCODE project), and they did not undergo amyloid-PET scans.…”

Section: Methodsmentioning

confidence: 99%

“…For FDG-PET, the smoothed images for each subject were normalized to obtain the SUVR map using the cerebral cortex as a reference region. For amyloid-PET, the whole cerebellum was used as the reference region, and the whole cerebral cortex was used as the ROI [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a Han population

Dong

Jiang

et al. 2021

Alz Res Therapy

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Introduction Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer’s disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level. Methods This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition. Results Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus. Conclusions These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism.

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“…The whole brain voxel-wise standardized uptake value ratio (SUVR) was normalized to the whole cerebellum, representing the mean cortical SUVR. For the dichotomy, amyloid-PET positivity (that is, participants who in the preclinical stage of the Alzheimer’s continuum) was defined a priori with the established cutoff of >1.18 ( Fakhry-Darian et al, 2019 ). The results for each participant were confirmed by two senior radiologists who were blinded to any clinical information and made positive or negative judgments.…”

Section: Methodsmentioning

confidence: 99%

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer’s Disease: An Exploratory Study

Jiang

et al. 2020

Front. Cell Dev. Biol.

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Diagnosing Alzheimer’s disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into “converters” and “nonconverters” according to individuals’ future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer’s Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7–95.9% and 87.1–90.8% in the validation set and 81.9–89.1% and 83.2–83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649–0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

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Optimisation and usefulness of quantitative analysis of 18F-florbetapir PET

Cited by 16 publications

References 18 publications

Grey zone amyloid burden affects memory function: the SCIENCe project

Grey zone amyloid burden affects memory function: the SCIENCe project

Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a Han population

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer’s Disease: An Exploratory Study

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