Imaging based enrichment criteria using deep learning algorithms for efficient clinical trials in MCI

摘要

The Mild Cognitive Impairment (MCI) stage of 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, employing multi-modal imaging-derived inclusion criteria may be especially beneficial. Here, we present a novel multi-modal 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 (MRI), based on a new deep learning algorithm (randomized denoising autoencoder marker, rDAm). Using ADNI2 MCI data, we show that employing rDAm as a trial enrichment criterion reduces the required sample estimates by at least five times compared to the no-enrichment regime, and leads to smaller trials with high statistical power, compared to existing methods.