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Cited by 55Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Image Segmentation Techniques

Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls

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Raamana, Pradeep Reddy, Rosen, Howard, Miller, Bruce, Weiner, Michael W., Wang, Lei, Beg, Mirza Faisal·2014·Frontiers in Neurology
DOI 10.3389/fneur.2014.00071PMC PMC4026692
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Abstract

Biomarkers derived from brain magnetic resonance (MR) imaging have promise in being able to assist in the clinical diagnosis of brain pathologies. These have been used in many studies in which the goal has been to distinguish between pathologies such as Alzheimer's disease and healthy aging. However, other dementias, in particular, frontotemporal dementia, also present overlapping pathological brain morphometry patterns. Hence, a classifier that can discriminate morphometric features from a brain MRI from the three classes of normal aging, Alzheimer's disease (AD), and frontotemporal dementia (FTD) would offer considerable utility in aiding in correct group identification. Compared to the conventional use of multiple pair-wise binary classifiers that learn to discriminate between two classes at each stage, we propose a single three-way classification system that can discriminate between three classes at the same time. We present a novel classifier that is able to perform a three-class discrimination test for discriminating among AD, FTD, and normal controls (NC) using volumes, shape invariants, and local displacements (three features) of hippocampi and lateral ventricles (two structures times two hemispheres individually) obtained from brain MR images. In order to quantify its utility in correct discrimination, we optimize the three-class classifier on a training set and evaluate its performance using a separate test set. This is a novel, first-of-its-kind comparative study of multiple individual biomarkers in a three-class setting. Our results demonstrate that local atrophy features in lateral ventricles offer the potential to be a biomarker in discriminating among AD, FTD, and NC in a three-class setting for individual patient classification.

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  • Machine learning techniques have been applied to distinguish bvFTD from cognitively normal controls, Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features.

    “Recently, machine learning techniques have been applied to distinguish between bvFTD and Cognitively Normal Subjects (CNCs), Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features. 9-17”

    From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [13]

    The Abstract and Materials and Methods describe an MRI-based classifier distinguishing bvFTD, Alzheimer disease, and normal controls. The Discussion describes including other neurodegenerative diseases only as a possible future extension; this paper does not support the claim about distinguishing bvFTD from other psychiatric or neurologic disorders. (LLM, conf=0.89)

    Experimental — accuracy not yet measured

  • Prior machine learning studies achieved moderate to high accuracy distinguishing bvFTD.

    “In general, they achieved moderate to high accuracy distinguishing bvFTD.”

    From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [13]

    The Discussion reports that a prior pair-wise classification study achieved 100% accuracy distinguishing dementia groups from controls, but cautions that this did not represent predictive value. The Introduction says most earlier studies grouped FTD variants together and few studied bvFTD alone, so the reported accuracy cannot be attributed specifically to distinguishing bvFTD. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • The best AUC among previous studies was reported by Raamana et al (AUC 0.938, 100% sensitivity and 88% specificity).

    “The best AUC was reported by Raamana et al 13 (AUC 0.938, 100% sensitivity and 88% specificity).”

    From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [13]

    The Discussion reports an AUC of 0.938 for lateral-ventricle features on an independent test set in the controls-versus-FTD comparison. The text of our copy does not give the claimed 100% sensitivity or 88% specificity for that result. It also reports an AUC of 1 for another pair, so 0.938 is not the paper’s highest reported AUC. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Previous studies classified bvFTD from a control group.

    “Previous studies classified bvFTD from a control group. 9-13”

    From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [13]

  • Raamana et al trained and tested their classifier on the same cohort.

    “However, an important limitation is that they trained and tested on the same cohort”

    From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [13]

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Sorted by citations

Authors
Raamana, P. R., Rosen, H., Miller, B., Weiner, M. W., Wang, L., & Beg, M. F.
Affiliations
  • Simon Fraser University (CA)
  • University of California, San Francisco (US)
  • San Francisco VA Medical Center (US)
  • Northwestern University (US)
Corresponding author
Raamana, Pradeep Reddy
ORCID
Venue
Frontiers in Neurology
Publisher
Frontiers Media SA
Published
2014-05-12
Volume
5
Pages
71-71
ISSN
1664-2295 (ISSN-L)
DOI
10.3389/fneur.2014.00071
PMC
PMC4026692
OpenAlex
W2121322199
Cited by
55
References
6
Citations by year
2026: 1 · 2025: 5 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 6 · 2020: 5 · 2019: 7 · 2018: 5 · 2017: 5 · 2016: 7 · 2015: 4 · 2014: 3
FWCI
4.84
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neurology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Alzheimer Society
  • Michael Smith Health Research BC
  • National Institutes of Health (AG023501, AG019724)
  • Natural Sciences and Engineering Research Council of Canada
  • National Institute on Aging (P01AG019724, P50AG023501)
  • National Institute of Biomedical Imaging and Bioengineering (P41EB015904)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
Alzheimer's disease, frontotemporal dementia, three-class classification, differential diagnosis, brain MRI biomarkers, hippocampal atrophy, shape invariants, brain morphometry, normal aging, dementia classification

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