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

Abstract Introduction 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.


INTRODUCTION
While correct diagnosis of a disease entity is required to select appropriate therapies, knowledge about the disease evolution and prognosis is also important. For example, some diseases might be self-limited and require minimal or no interventions. Other diseases might have faster and more virulent time courses, pushing providers to consider higher risk or more invasive therapies. Furthermore, prognosis information plays a critical role in patient discussions and resource planning. While radiology has so far focused largely on diagnosis, an opportunity exists to use radiological information to risk-stratify patients regarding disease prognosis.
Dementia is one condition for which prognosis is especially important. Understanding the likelihood and rate of progression of this disease would be extremely helpful, not only for individual patients and families, but also to plan clinical trials. Alzheimer's disease (AD) trials face significant challenges with enrollment. 1,2 Being able to selectively recruit patients likely to progress quickly, based in part on brain imaging biomarkers such as amyloid and tau deposition, could significantly impact the design, duration, and cost of clinical trials.
Deep learning has shown much promise in classifying patients and predicting their future disease trajectories. [3][4][5][6] It has also been used at the image level to transform images, either for better image reconstruction or the synthesis of desired contrasts (i.e., predicting computed tomography [CT] from magnetic resonance imaging [MRI] to enable MR-based positron emission tomography [PET] attenuation correction [7][8][9] ). Combining these two approaches would allow prediction of future image-based biomarkers, such as regional radiotracer uptake. In this study, we combined clinical, genetic, and imaging features at baseline and then used these to identify individuals at highest risk of rapid biomarker progression, in this case the quantitative change in amyloid beta protein deposition on future imaging studies. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we show that a model combining deep learning-derived image features and gradient-boosted random forest regression outperforms existing methods, that the baseline imaging features play a large role in detecting fast progressors, and that this can be used to enrich cohorts with fast progressors.

Linear regression
We performed multivariate regression using the StatsModels library 17 in Python, which fits the following equation: Overview of ResNet-50 training procedure. Three central slices are fed into the input color channels. The ResNet algorithm is modified to perform regression on standardized uptake value ratio (SUVR) rather than classification. Finally, for the testing of ΔSUVR prediction, we extract 2048 deep features, the results of the average pool operation (scalar values)

Deep learning
We trained a convolutional neural network (CNN) to predict amyloid PET SUVR, 18 using methods described in Reith et al. 19 Of note, the CNN is not trained to predict future SUVR change, but instead learned image features associated with baseline SUVR. In brief, the ResNet-50 architecture 20 was used. Network input was three centrally located slices. Standard ResNet ends with a layer for distinguishing 1000 differing classes, but we modified it for SUVR prediction (a regression task). The final layer was changed to a single output without an activation function. The cost function was the mean squared error between predicted and true SUVR using the ADAM optimizer. 21 We applied the best-performing hyperparameters for training on current SUVR 19 Figure   S2 in supporting information).

Gradient boosting decision tree
To combine clinical/genetic features and deep imaging features, we used a gradient boosting decision tree (GBDT) algorithm, specifically the LightGBM implementation, to predict SUVR change. 23,24 Details of the training procedure can be found in the supporting information.
We tested GBDT models with and without deep learning-based PET

Data analysis
We analyzed GBDT performance via root mean squared error (RMSE) for ΔSUVR in all follow-up scans (1136 scans in 610 individuals). We performed 5-fold cross-validation and present the average RMSE. For cross-validation purposes, we divide our dataset into five distinct parts.
Each part has its unique subjects, meaning no subject can be found in more than one of the five distinct parts, guaranteeing that there is no subject shared by both the training and test sets. We analyzed the statistical significance of model design choices with linear mixed effects models and Wilcoxon rank sum tests, as appropriate. For these measures, we compared the squared error of ML system predictions.

F I G U R E 2
Training and test set performance. Root mean-squared error (RMSE) between prediction and the true ΔSUVR (standardized uptake value ratio) is shown, so lower values represent better model performance. The gradient-boosted decision tree (GBDT) using the image-based activations had the best performance.
To assess the practical value of these SUVR predictions, we also used them to select subjects with the highest SUVR changes. We also compared model performance with other methods of selection in two ways. The first is to randomly select patients that meet a specific criterion. We included the following groups for these tests: subjects with at least one APOE ε4 allele (n = 70). We also examined the various models' performance in pre-selected groups often targeted in clinical trials, specifically: mildly positive amyloid patients (as defined above) and subjects with mild dementia (baseline CDR 0.5; n = 229). Because these latter datasets start from a smaller denominator, the task was to identify the top 20% fastest true ΔSUVR progressors.

Patient cohort
The baseline demographics and clinical features of the 610 unique subjects with 1136 follow-up scans are summarized in Table 1. The time horizon of the follow-up predictions was as follows: 1 to 3 years (n = 553), 3 to 5 years (n = 354), and 5+ years (n = 227). We additionally compared the correlation between ΔSUVR predictions and ground truth changes, shown in Figure

Implications for study selection
We evaluated the ability of the various models to identify the fastest 10% of true amyloid accumulators ( Figure 4) with activations compared to GBDT without activations. More detail can be found in Figure S7 in supporting information.
We compared these results to other ways of selecting fast progres-  . These findings are shown in Figure S5 in supporting information.

DISCUSSION
In this study, we have extended prior work using neural networks to predict current SUVR for amyloid PET studies in the ADNI cohort 19 to predict SUVR in the future. We accomplished this by training a network on longitudinal studies and by including clinical and genetic features.
We found that a GBDT that includes clinical, demographic, and genetic Prior work in this area with the ADNI dataset has focused on predicting clinical outcomes. 3,[25][26][27][28] In this study, we take a different approach, focusing on predicting a quantitative biomarker which may be less subject to error compared to clinical evaluations. For example, the IDEAS trial showed that the assessments of clinicians diagnosing AD was often inaccurate. 28 Predicting the development of a relevant quantitative feature such as SUVR is objective and could find use in data mining, clinical trial assessments, and longitudinal analysis. We also believe that using regression (i.e., predicting the continuous value of an important biomarker) rather than classification allowed the algorithm to learn more from each individual subject's information. He is a board member of ISMRM and president-elect of the ASFNR.

AUTHOR CONTRIBUTIONS
Fabian Reith: study conceptualization, study design, data collection, data curation, data analysis, data interpretation, deep learning conceptualization, deep learning design, deep learning analysis, statistical analysis, manuscript drafting and editing. Elisabeth Mormino: study design, data interpretation. Greg Zaharchuk: funding, study conceptualization, study design, data collection, data interpretation, manuscript drafting, and editing.