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

By Vamsi Krishna Ithapu, Vikas Singh, Ozioma C. Okonkwo, Richard J. Chappell, N. Maritza Dowling, Sterling C. Johnson. 2015

61 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Yifan Peng
2. Marwin H. S. Segler

In Radiology, Nuclear Medicine and Imaging

1. Alexandr A Kalinin
2. Vladimir I. Iglovikov
3. Xiaosong Wang
4. Matthew P. Lungren
5. Alexey A. Shvets

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “[276] used a randomized denoising autoencoder to learn a multimodal imaging marker that predicts future cognitive and neural decline from positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging.”
• A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “This creates a more individualized, patienttailored approach (Ithapu et al., 2015), which is imperative in the current age of personalized medicine because it allows further consideration of genetic or life-style risks, by utilizing advanced computational power (Habes et al.”