Predicting MCI outcome with clinically available MRI and CSF biomarkers

Heister, David; Brewer, James B.; Magda, Sebastian; Blennow, Kaj; McEvoy, Linda K.

doi:10.1212/wnl.0b013e3182343314

Cited by 189 publications

(178 citation statements)

References 45 publications

Supporting

Mentioning

167

Contrasting

Order By: Relevance

“…22 This software was evaluated against the manual segmentation, and a good agreement was found between manual and automatic segmentation. 23 However, we cannot compare it with our study because the ICV data were not presented.…”

Section: Discussionmentioning

confidence: 96%

Evaluation of Automatic Measurement of the Intracranial Volume Based on Quantitative MR Imaging

Ambarki

Lindqvist²,

Wåhlin³

et al. 2012

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

BACKGROUND AND PURPOSE:Brain size is commonly described in relation to ICV, whereby accurate assessment of this quantity is fundamental. Recently, an optimized MR sequence (QRAPMASTER) was developed for simultaneous quantification of T1, T2, and proton density. ICV can be measured automatically within minutes from QRAPMASTER outputs and a dedicated software, SyMRI. Automatic estimations of ICV were evaluated against the manual segmentation.

show abstract

Section: Discussionmentioning

confidence: 96%

Evaluation of Automatic Measurement of the Intracranial Volume Based on Quantitative MR Imaging

Ambarki

Lindqvist²,

Wåhlin³

et al. 2012

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

“…Shaffer et al biomarker utility have been published from ADNI (11,(18)(19)(20)(21)(22)(23)(24), but relatively few have examined the additive utility of such biomarkers above and beyond routine neuropsychological tests. This is critical because none of the biomarkers are likely to be used clinically before cognition is assessed.…”

Section: Neuroradiology: Alzheimer Disease Conversion Prediction Withmentioning

confidence: 99%

“…This makes their clinical outcomes more ambiguous, because it is possible that such subjects might have converted to AD after their last visit. One study by Heister et al (24), analyzing longitudinal data in subjects with MCI from the ADNI, showed that those who dropped out of the study early but retained the MCI diagnosis at their last visit had greater memory impairment than those who completed the study.…”

Section: Subject Characteristicsmentioning

confidence: 99%

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

et al. 2013

View full text Add to dashboard Cite

For the Alzheimer's Disease Neuroimaging InitiativePurpose:To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. Materials and Methods:All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data.The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). Results:Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P , .00001). FDG PET contributed more information to routine tests (P , .00001) than CSF (P = .32) or MR imaging (P = .08). Conclusion:Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.q RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup /suppl

show abstract

“…The estimation of medial temporal lobe atrophy (MTA) on MRI scans, either visually assessed or evaluated through manual or automatic volumetry, has been found to be a valid marker for the conversion from MCI to dementia [16,17,18]. However, both manual and automatic volumetry methods lack clinical feasibility.…”

Section: Introductionmentioning

confidence: 99%

Visual Evaluation of Medial Temporal Lobe Atrophy as a Clinical Marker of Conversion from Mild Cognitive Impairment to Dementia and for Predicting Progression in Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

Persson

Barca²,

Eldholm³

et al. 2017

Dement Geriatr Cogn Disord

View full text Add to dashboard Cite

Background/Aims: To evaluate whether visual assessment of medial temporal lobe atrophy (vaMTA) can predict 2-year conversion from mild cognitive impairment (MCI) to dementia and progression of MCI and Alzheimer's disease dementia as measured by the Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB). Methods: vaMTA was performed in 94 patients with MCI according to the Winblad criteria and in 124 patients with AD according to ICD-10 and NINCDS-ADRDA criteria. Demographic data, the Consortium to Establish a Registry for Alzheimer's Disease 10-word delayed recall, APOE ɛ4 status, Cornell Scale for Depression in Dementia, and comorbid hypertension were used as covariates. Results: vaMTA was associated with MCI conversion in an unadjusted model but not in an adjusted model (p = 0.075), where delayed recall and APOE ɛ4 status were significant predictors. With CDR-SB change as the outcome, an interaction between vaMTA and diagnosis was found, but in the adjusted model only delayed recall and age were significant predictors. For vaMTA below 2, the association between vaMTA and CDR-SB change differed between diagnostic groups. Similar results were found based on a trajectory analysis. Conclusion: In adjusted models, memory function, APOE ɛ4 status and age were significant predictors of disease progression, not vaMTA. The association between vaMTA and CDR-SB change was different in patients with MCI and Alzheimer's disease dementia.

show abstract

Predicting MCI outcome with clinically available MRI and CSF biomarkers

Abstract: Incorporating quantitative assessment of learning ability along with vMRI or CSF biomarkers in the clinical workup of MCI can provide critical information on risk of imminent conversion to AD.

Cited by 189 publications

References 45 publications

Evaluation of Automatic Measurement of the Intracranial Volume Based on Quantitative MR Imaging

Evaluation of Automatic Measurement of the Intracranial Volume Based on Quantitative MR Imaging

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

Visual Evaluation of Medial Temporal Lobe Atrophy as a Clinical Marker of Conversion from Mild Cognitive Impairment to Dementia and for Predicting Progression in Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

Contact Info

Product

Resources

About