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Boosting power for clinical trials using classifiers based on multiple biomarkers
Open accessAbstract
Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline. We introduce a machine learning method to boost power in clinical trials. We created a Support Vector Machine algorithm that combines brain imaging and other biomarkers to classify 737 Alzheimer's disease Neuroimaging initiative (ADNI) subjects as having Alzheimer's disease (AD), mild cognitive impairment (MCI), or normal controls. We trained our classifiers based on example data including: MRI measures of hippocampal, ventricular, and temporal lobe volumes, a PET-FDG numerical summary, CSF biomarkers (t-tau, p-tau, and Abeta(42)), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Abeta(42), contributed more to MCI classification. Using all biomarkers jointly, we used our classifier to select the one-third of the subjects most likely to decline. In this subsample, fewer than 40 AD and MCI subjects would be needed to detect a 25% slowing in temporal lobe atrophy rates with 80% power--a substantial boosting of power relative to standard imaging measures.
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“Kohannim et al. (2010) demonstrated that combining MRI, FDG-PET, and cerebrospinal fluid (CSF) biomarkers yielded 5–10% accuracy gains over MRI alone”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Kohannim et al., 2010
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Metadata
- Authors
- Kohannim, O., Hua, X., Hibar, D. P., Lee, S., Chou, Y.-Y., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M.
- Affiliations
- University of California, Los Angeles (US)
- Mayo Clinic (US)
- University of California, San Francisco (US)
- Corresponding author
- Thompson, Paul M.
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2010-08
- Volume
- 31
- Issue
- 8
- Pages
- 1429-1442
- ISSN
- 0197-4580 (ISSN-L)
- PMC
- PMC2903199
- OpenAlex
- W2167638846
- Cited by
- 190
- References
- 74
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 6 · 2023: 13 · 2022: 11 · 2021: 6 · 2020: 11 · 2019: 11 · 2018: 11 · 2017: 10 · 2016: 9 · 2015: 13 · 2014: 19 · 2013: 22 · 2012: 10
- FWCI
- 12.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neurobiology of Aging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Alzheimer's Drug Discovery Foundation
- Pfizer
- Merck
- University of Pennsylvania
- F. Hoffmann-La Roche
- Medpace
- National Institutes of Health (5u01ag024904-10)
- National Institute on Aging (U01-AG-024904, P30AG010129, K01AG030514, U19AG010483)
- National Institute of Biomedical Imaging and Bioengineering
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, mild cognitive impairment, support vector machine, combined biomarker detection, brain MRI, FDG-PET, cerebrospinal fluid biomarkers, amyloid-beta 42, APOE genotype, temporal lobe atrophy, computer-aided diagnosis, ADNI
- MeSH terms
- Age Factors, Aged, Aged, 80 and over, Alzheimer Disease, Artificial Intelligence, Cognition Disorders, Female, Genotype, Humans, Male, Sex Factors, Biomarkers, Randomized Controlled Trials as Topic, Apolipoprotein E4
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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