Scan-stratified case-control sampling for modeling blood-brain barrier integrity in multiple sclerosis.
Multiple sclerosis (MS) is an immune-mediated neurological disease that causes morbidity and disability. In patients with MS, the accumulation of lesions in the white matter of the brain is associated with disease progression and worse clinical outcomes. Breakdown of the blood-brain barrier in newer lesions is indicative of more active disease-related processes and is a primary outcome considered in clinical trials of treatments for MS. Such abnormalities in active MS lesions are evaluated in vivo using contrast-enhanced structural MRI, during which patients receive an intravenous infusion of a costly magnetic contrast agent. In some instances, the contrast agents can have toxic effects. Recently, local image regression techniques have been shown to have modest performance for assessing the integrity of the blood-brain barrier based on imaging without contrast agents. These models have centered on the problem of cross-sectional classification in which patients are imaged at a single study visit and pre-contrast images are used to predict post-contrast imaging. In this paper, we extend these methods to incorporate historical imaging information, and we find the proposed model to exhibit improved performance. We further develop scan-stratified case-control sampling techniques that reduce the computational burden of local image regression models, while respecting the low proportion of the brain that exhibits abnormal vascular permeability.
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, U.S.A.
Gina-Maria
GM
Pomann
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.|Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, U.S.A.
Elizabeth M
EM
Sweeney
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.|Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, U.S.A.
Daniel S
DS
Reich
Department of Statistics, North Carolina State University, Raleigh, NC, U.S.A.
Ana-Maria
AM
Staicu
Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, U.S.A.
Russell T
RT
Shinohara
D001812:Blood-Brain Barrier / Q000502:physiology*; D001921:Brain / Q000473:pathology; D016022:Case-Control Studies; D003287:Contrast Media; D006801:Humans; D021621:Imaging, Three-Dimensional; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology
D003287:Contrast Media
case-control sampling; logistic regression; magnetic resonance imaging; multiple sclerosis