Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden

Kim, Jiyoung; Oh, Dongkyu; Sung, Kiyoung; Choi, Hongyoon; Paeng, Jin Chul; Cheon, Gi Jeong; Kang, Keon Wook; Lee, Dong Hoon

doi:10.1007/s00259-020-05044-x

Cited by 23 publications

(28 citation statements)

References 37 publications

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“…A SUVRs threshold of 1.2 or greater was used to discriminate participants with an intermediate brain Aβ load (+SUVRs). Similar threshold value was identified in previous studies (Ciarmiello et al, 2019;Kim et al, 2020).…”

Section: Cerebral Imagingsupporting

confidence: 87%

Influence of Physical Activity Levels and Functional Capacity on Brain β-Amyloid Deposition in Older Women

Pedrero‐Chamizo

Szoeke

Dennerstein

et al. 2021

Front. Aging Neurosci.

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Physical activity (PA) and Alzheimer's disease are associated. However, how PA influences the cerebral β-amyloid (Aβ) burden remains unclear. The aim of this study was to determine if PA levels and/or functional capacity (FC) are associated with Aβ plaque deposition, and whether these associations differed according to APOE-ε4 genotype. A total of 117 women (69.7 ± 2.6 years; 33.3% APOE-ε4-carriers) from the Women's Healthy Ageing Project cohort (WHAP) were analyzed. PA was measured using the International Physical Activity Questionnaire and, FC was evaluated using the Timed Up and Go test (TUGt). Positron emission tomography with F-18 Florbetaben was carried out to assess cerebral Aβ burden, and quantified using standardized uptake value rations. The sample was split into PA and TUGt tertiles (T1, T2 and T3), and compared according to APOE-ε4 genotype (positive/negative). There were no significant differences in Aβ accumulation according to PA tertiles and APOE-ε4 genotype. Regarding FC, APOE-ε4+ participants in the first TUGt tertile (high performance) obtained significant lower Aβ accumulations compared with the other two tertiles (p < 0.05). Comparing between genotypes, greater Aβ depositions were found between T2 and T3 in APOE-ε4+ compared with those who were APOE-ε4– (p < 0.05). Values of TUGt ≥ 6.5 s (APOE-ε4+) and 8.5 s (APOE-ε4–) were associated with an increased risk of having higher Aβ retention. In conclusion, low performance in TUGt is associated with a negative effect on brain pathology with increasing cerebral Aβ depositions in older women who are APOE-ε4+. In physically active older women (> 600 METs·min/week), higher PA levels are not associated with reduction in Aβ depositions.

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Section: Cerebral Imagingsupporting

confidence: 87%

Influence of Physical Activity Levels and Functional Capacity on Brain β-Amyloid Deposition in Older Women

Pedrero‐Chamizo

Szoeke

Dennerstein

et al. 2021

Front. Aging Neurosci.

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“…11 Continued work on a multiradioligand approach may allow the construction of higher confidence research/clinical models and ease of tau PET interpretation, perhaps as diagnostic aid in challenging clinical contexts, as has been reported for work with amyloid PET. 18,19 Our work also adds to growing evidence that imaging tau 5,13 has utility as a high-specificity biomarker in the diagnosis of AD, with good-albeit lower-performance in less impaired individuals where tau deposition in early Braak regions may be the first sign. 47,48 A future deep-learning-based system to detect early disease and predict conversion may indeed rely on MRI, 15 if only because of greater availability of data (especially longitudinal).…”

Section: Discussionmentioning

confidence: 73%

“…Our successful application of deep learning to a framework using both radioligands suggests that our deep learning–derived measure could be standardized/harmonized across multiple radioligands, and provide an intuitive (i.e., probabilistic) and unitary measure in a way not directly possible with current methods 11 . Continued work on a multi‐radioligand approach may allow the construction of higher confidence research/clinical models and ease of tau PET interpretation, perhaps as diagnostic aid in challenging clinical contexts, as has been reported for work with amyloid PET 18,19 …”

Section: Discussionmentioning

confidence: 99%

“…Machine learning techniques, especially convolutional neural networks, have shown promise in a number of contexts important to neuroimaging, including with MRI 14,15 and fluorodeoxyglucose (FDG) PET 16 . Similarly, deep learning with PET radioligands has demonstrated improved research efficacy 17 and diagnostic utility 18,19 . To our knowledge, relatively few studies exist that use tau PET, 20–22 in part because of current limitations/challenges of applying deep learning in clinical research (especially the relatively small number of absolute subjects).…”

Section: Introductionmentioning

confidence: 99%

“…16 Similarly, deep learning with PET radioligands has demonstrated improved research efficacy 17 and diagnostic utility. 18,19 To our knowledge, relatively few studies exist that use tau PET, [20][21][22] in part because of current limitations/challenges of applying deep learning in clinical research (especially the relatively small number of absolute subjects).…”

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confidence: 99%

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Deep learning improves utility of tau PET in the study of Alzheimer's disease

Zou¹,

Park²,

Johnson³

et al. 2021

Alz & Dem Diag Ass & Dis Mo

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Introduction Positron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification. Methods 18F‐MK6240 (n = 320) and AV‐1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)‐based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making. Results Overall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding. Discussion CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.

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Prediction of Aβ State from Brain Amyloid PET Images Using Machine Learning Algorithm

Simfukwe

Youn

2022

Alzheimer's & Dementia

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Background: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-Amyloid (Aβ) deposition in Alzheimer's patients requires a lot of time and effort from physicians, and also the variation of each interpreter may differ. For this reason, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to predict the Aβ positive and Aβ negative status from brain amyloid PET images. Method: A total number of 7,344 PET images of 144 subjects were used in this study. The 18Forbetaben (18F-FBB) PET was administered on 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 two classes: Aβ positive and Aβ negative states based on the BAPL scores. Results: The binary prediction of the model average performance matrices was evaluated after 40 epochs of ve trials based on test datasets. The model accuracy for predicting Aβ positivity and Aβ negativity was 82.00±0.02 in the test dataset. The sensitivity and speci city were 97.00±0.02 and 97.00±0.02 with an area under the curve (AUC) of 90.00±0.03. Conclusion: Based on this study, the designed CNN model has the potential to be used clinically for screening amyloid PET images.

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Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden

Cited by 23 publications

References 37 publications

Influence of Physical Activity Levels and Functional Capacity on Brain β-Amyloid Deposition in Older Women

Influence of Physical Activity Levels and Functional Capacity on Brain β-Amyloid Deposition in Older Women

Deep learning improves utility of tau PET in the study of Alzheimer's disease

Prediction of Aβ State from Brain Amyloid PET Images Using Machine Learning Algorithm

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