A Nomogram for Predicting Amyloid PET Positivity in Amnestic Mild Cognitive Impairment

Kim, Si Eun; Woo, Sook‐Young; Kim, Seon Woo; Chin, Juhee; Kim, Hee Jin; Lee, Ki Young; Park, Jinse; Park, Kyung Won; Kang, Do‐Young; Noh, Young; Ye, Byoung Seok; Yoo, Hyung Sik; Lee, Jin San; Kim, Yeshin; Kim, Seungjoo; Cho, Soo Hyun; Na, Duk L.; Lockhart, Samuel N.

doi:10.3233/jad-180048

Cited by 41 publications

(28 citation statements)

References 43 publications

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“…A few studies have previously proposed different types predictive models for detecting cerebral amyloid positivity based on demographics, NP tests, MRI measures, and blood or CSF-based biomarkers [33][34][35][36][37][38]. For example, Kander et al [34] reported AUCs of 0.59-0.67 for individual NP tests, AUC of 0.64 using all NP tests, and AUC of 0.64 for hippocampal volume.…”

Section: Discussionmentioning

confidence: 99%

“…Palmqvist et al [36] applied a forward selection logistic regression model to demographics, ApoE4, NP tests, and white matter lesions for prediction of amyloid positivity and achieved AUCs of 0.80-0.82 in ADNI. Kim et al [35] used similar variables and using logistic regressions, developed a nomogram that achieved predictive AUCs of 0.74-0.77.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques

Ezzati

Harvey

Habeck

et al. 2020

JAD

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Background: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. Objective: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. Methods: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. Results: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques

Ezzati

Harvey

Habeck

et al. 2020

JAD

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“…We defined Aβ PET positivity (PET+) for the three different types of PET images as follows: 1) Global PiB SUVR (assessed from the volume-weighted average SUVR of 28 bilateral cerebral cortical VOIs) of greater than 1.5 as described in our previous study, 20 2) visual rating score on florbetaben PET of 2 or 3 on the brain Aβ plaque load scoring system, 21 3) positive visual interpretation of 18 F-flutemetamol PET in any one of the five brain regions (frontal, parietal, posterior cingulate and precuneus, striatum, and lateral temporal lobes) in either hemisphere. 22 …”

Section: Methodsmentioning

confidence: 99%

Cerebrospinal Fluid Biomarkers for the Diagnosis and Classification of Alzheimer's Disease Spectrum

et al. 2020

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Background: Cerebrospinal fluid (CSF) biomarkers are increasingly used in clinical practice for the diagnosis of Alzheimer's disease (AD). We aimed to 1) determine cutoff values of CSF biomarkers for AD, 2) investigate their clinical utility by estimating a concordance with amyloid positron emission tomography (PET), and 3) apply ATN (amyloid/tau/neurodegeneration) classification based on CSF results. Methods: We performed CSF analysis in 51 normal controls (NC), 23 mild cognitive impairment (MCI) and 65 AD dementia (ADD) patients at the Samsung Medical Center in Korea. We tried to develop cutoff of CSF biomarkers for differentiating ADD from NC using receiver operating characteristic analysis. We also investigated a concordance between CSF and amyloid PET results and applied ATN classification scheme based on CSF biomarker abnormalities to characterize our participants. Results: CSF Aβ42, total tau (t-tau) and phosphorylated tau (p-tau) significantly differ across the three groups. The area under curve for the differentiation between NC and ADD was highest in t-tau/Aβ42 (0.994) followed by p-tau/Aβ42 (0.963), Aβ42 (0.960), t-tau (0.918), and p-tau (0.684). The concordance rate between CSF Aβ42 and amyloid PET results was 92%. Finally, ATN classification based on CSF biomarker abnormalities led to a majority of NC categorized into AT -N-(73%), MCI as A+T-N-(30%)/A+T+N+(26%), and ADD as A+T+N+(57%). Conclusion: CSF biomarkers had high sensitivity and specificity in differentiating ADD from NC and were as accurate as amyloid PET. The ATN subtypes based on CSF biomarkers may further serve to predict the prognosis.

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“…However, risk factor models do not directly translate into clinically useful or practical algorithms. Many models lack external validation, include inputs with small effect sizes, or include inputs that are burdensome or costly (e.g., extensive neuropsychological testing or imaging) [10,15–17]. Recently, more practical algorithms to estimate the likelihood of Aβ positivity among patients with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) were published [11,18,19].…”

Section: Introductionmentioning

confidence: 99%

Practical algorithms for amyloid β probability in subjective or mild cognitive impairment

Maserejian

Bian

Wang

et al. 2019

Alz & Dem Diag Ass & Dis Mo

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Introduction Practical algorithms predicting the probability of amyloid pathology among patients with subjective cognitive decline or mild cognitive impairment may help clinical decisions regarding confirmatory biomarker testing for Alzheimer's disease. Methods Algorithm feature selection was conducted with Alzheimer's Disease Neuroimaging Initiative and Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing data. Probability algorithms were developed in Alzheimer's Disease Neuroimaging Initiative using nested cross‐validation accompanied by stratified subsampling to obtain 1000 internally validated decision trees. Semi‐independent validation was conducted using Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Independent external validation was conducted in the population‐based Mayo Clinic Study of Aging. Results Two algorithms were developed using age and normalized immediate recall z‐scores, with or without apolipoprotein E ε4 carrier status. Both algorithms had robust performance across data sets and when substituting different recall memory tests. Discussion The statistical framework resulted in robust probability estimation. Application of these algorithms may assist in clinical decision‐making for further testing to diagnose amyloid pathology.

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A Nomogram for Predicting Amyloid PET Positivity in Amnestic Mild Cognitive Impairment

Cited by 41 publications

References 43 publications

Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques

Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques

Cerebrospinal Fluid Biomarkers for the Diagnosis and Classification of Alzheimer's Disease Spectrum

Practical algorithms for amyloid β probability in subjective or mild cognitive impairment

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