Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient...

In Alzheimer's disease, asymptomatic patients may have amyloid deposition, but predicting their progression rate remains a substantial challenge with implications for clinical trial enrollment. Here, we demonstrate an artificial intelligence approach to use baseline clinical information and images to predict changes in quantitative biomarkers of brain pathology on future images.

Patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent positron emission tomography (PET) with the amyloid radiotracer 18F-AV45 (florbetapir) were included. We identified important baseline PET image features using a deep convolutional neural network based on ResNet. These were combined with eight clinical, demographic, and genetic markers using a gradient-boosted decision tree (GBDT) algorithm to predict future quantitative standardized uptake value ratio (SUVR), an established biomarker of brain amyloid deposition. We used this model to better identify individuals with the highest positive change in amyloid deposition on future images and compared this to typical inclusion criteria for clinical trials. We also compared the model's performance to other methods such as multivariate linear regression and GBDT without imaging features.

Using 2577 PET scans from 1224 unique individuals, we showed that the GBDT with deep image features was significantly more accurate than the other approaches, reaching a root mean squared error of 0.0339 ± 0.0027 for future SUVR prediction. Using this approach, we could identify individuals with the highest 10% SUVR accumulation at rates 2- to 4-fold higher than by random pick or existing inclusion criteria.

Predicting quantitative biomarkers on future images using machine learning methods consisting of deep image features combined with clinical data may allow better targeting of treatments or enrollment in clinical trials.