TY - JOUR
T1 - Boosting power for clinical trials using classifiers based on multiple biomarkers
AU - Kohannim, Omid
AU - Hua, Xue
AU - Hibar, Derrek P.
AU - Lee, Suh
AU - Chou, Yi Yu
AU - Toga, Arthur W.
AU - Jack, Clifford R.
AU - Weiner, Michael W.
AU - Thompson, Paul M.
N1 - Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's disease Neuroimaging Initiative (ADNI) ( National Institutes of Health , Grant U01 AG024904 ). ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly, and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer, Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., and Wyeth, as well as nonprofit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the US Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH Grants P30 AG010129 , K01 AG030514 , and the Dana Foundation .
PY - 2010/8
Y1 - 2010/8
N2 - 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 Aβ42), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Aβ42, 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.
AB - 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 Aβ42), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Aβ42, 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.
KW - Alzheimer's disease
KW - Biomarkers
KW - Classification
KW - Clinical trial enrichment
KW - Magnetic resonance imaging
KW - Mild cognitive impairment
KW - Neuroimaging
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=77954032616&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954032616&partnerID=8YFLogxK
U2 - 10.1016/j.neurobiolaging.2010.04.022
DO - 10.1016/j.neurobiolaging.2010.04.022
M3 - Article
C2 - 20541286
AN - SCOPUS:77954032616
SN - 0197-4580
VL - 31
SP - 1429
EP - 1442
JO - Neurobiology of aging
JF - Neurobiology of aging
IS - 8
ER -