Open AccessPredicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection
Author(s) -
Reith Fabian H.,
Mormino Elizabeth C.,
Zaharchuk Greg
Publication year - 2021
Publication title -
alzheimer's and dementia: translational research and clinical interventions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.49
H-Index - 30
ISSN - 2352-8737
DOI - 10.1002/trc2.12212
Subject(s) - neuroimaging , positron emission tomography , biomarker , clinical trial , random forest , artificial intelligence , medicine , dementia , standardized uptake value , imaging biomarker , alzheimer's disease neuroimaging initiative , disease , oncology , machine learning , nuclear medicine , radiology , computer science , magnetic resonance imaging , biology , biochemistry , psychiatry
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. Methods 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. Findings 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. Discussion 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.