title: Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment.
abstract: The mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) may be optimal for clinical trials to test potential treatments for preventing or delaying decline to dementia. However, MCI is heterogeneous in that not all cases progress to dementia within the time frame of a trial and some may not have underlying AD pathology. Identifying those MCIs who are most likely to decline during a trial and thus most likely to benefit from treatment will improve trial efficiency and power to detect treatment effects. To this end, using multimodal, imaging-derived, inclusion criteria may be especially beneficial. Here, we present a novel multimodal imaging marker that predicts future cognitive and neural decline from [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging, based on a new deep learning algorithm (randomized denoising autoencoder marker, rDAm). Using ADNI2 MCI data, we show that using rDAm as a trial enrichment criterion reduces the required sample estimates by at least five times compared with the no-enrichment regime and leads to smaller trials with high statistical power, compared with existing methods.
authors.affiliation: Department of Computer Sciences, University of Wisconsin Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA. Electronic address: ithapu@wisc.edu.
authors.forename: Vamsi K
authors.initials: VK
authors.lastname: Ithapu
authors.affiliation: Department of Computer Sciences, University of Wisconsin Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, WI, USA.
authors.forename: Vikas
authors.initials: V
authors.lastname: Singh
authors.affiliation: Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin Madison, Madison, WI, USA.
authors.forename: Ozioma C
authors.initials: OC
authors.lastname: Okonkwo
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, WI, USA.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Chappell
authors.affiliation: Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, WI, USA.
authors.forename: N Maritza
authors.initials: NM
authors.lastname: Dowling
authors.affiliation: Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin Madison, Madison, WI, USA; William S. Middleton Memorial Veterans Hospital, University of Wisconsin Madison, Madison, WI, USA.
authors.forename: Sterling C
authors.initials: SC
authors.lastname: Johnson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging / Q000473:pathology; D016229:Amyloid beta-Peptides; D015415:Biomarkers; D002986:Clinical Trials as Topic; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging / Q000473:pathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography / Q000379:methods*
chemical_list: D016229:Amyloid beta-Peptides; D015415:Biomarkers
keywords: Alzheimer's disease; Clinical trials; Deep learning; Sample enrichment