Imaging‐based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment

Alzheimer's & Dementia - Tập 11 - Trang 1489-1499 - 2015
Vamsi K. Ithapu1,2, Vikas Singh1,2,3, Ozioma C. Okonkwo2,4, Richard J. Chappell3, N. Maritza Dowling2,3, Sterling C. Johnson2,5,6
1Department of Computer Sciences, University of Wisconsin Madison, Madison, WI, USA
2Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA
3Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison, Madison, WI, USA
4Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA
5Department of Medicine, University of Wisconsin, Madison, Madison, WI, USA
6William S. Middleton Memorial Veterans Hospital, University of Wisconsin Madison, Madison, WI, USA

Tóm tắt

AbstractThe 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.

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Alzheimer's & Dementia

Tập 11

1489-1499

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