Dataset Information

Challenges associated with biomarker-based classification systems for Alzheimer's disease.

ABSTRACT:

Introduction

We aimed to evaluate the consistency of the A/T/N classification system.

Methods

We included healthy controls, mild cognitive impairment, and dementia patients from Alzheimer's disease Neuroimaging Initiative. We assessed subject classification consistency with different biomarker combinations and the agreement and correlation between biomarkers.

Results

Subject classification discordance ranged from 12.2% to 44.5% in the whole sample; 17.3%-46.4% in healthy controls; 11.9%-46.5% in mild cognitive impairment, and 1%-35.7% in dementia patients. Amyloid, but not neurodegeneration biomarkers, showed good agreement both in the whole sample and in the clinical subgroups. Amyloid biomarkers were correlated in the whole sample, but not along the Alzheimer's disease continuum (as defined by a positive amyloid positron emission tomography). Neurodegeneration biomarkers were poorly correlated both in the whole sample and along the Alzheimer's disease continuum. The relationship between biomarkers was stage-dependent.

Discussion

Our findings suggest that the current A/T/N classification system does not achieve the required consistency to be used in the clinical setting.

SUBMITTER: Illan-Gala I

PROVIDER: S-EPMC6114028 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Challenges associated with biomarker-based classification systems for Alzheimer's disease.

Illán-Gala Ignacio I Pegueroles Jordi J Montal Victor V Vilaplana Eduard E Carmona-Iragui María M Alcolea Daniel D Dickerson Bradford C BC Sánchez-Valle Raquel R de Leon Mony J MJ Blesa Rafael R Lleó Alberto A Fortea Juan J

Alzheimer's & dementia (Amsterdam, Netherlands) 20180421

<h4>Introduction</h4>We aimed to evaluate the consistency of the A/T/N classification system.<h4>Methods</h4>We included healthy controls, mild cognitive impairment, and dementia patients from Alzheimer's disease Neuroimaging Initiative. We assessed subject classification consistency with different biomarker combinations and the agreement and correlation between biomarkers.<h4>Results</h4>Subject classification discordance ranged from 12.2% to 44.5% in the whole sample; 17.3%-46.4% in healthy co ...[more]

PMID: 30175226

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Project description:BackgroundIn a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method.MethodsWe recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability.ResultsThe classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1-4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA.ConclusionsIn the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

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Dataset Information

Challenges associated with biomarker-based classification systems for Alzheimer's disease.

Introduction

Methods

Results

Discussion

Publications

Challenges associated with biomarker-based classification systems for Alzheimer's disease.

Similar Datasets

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