Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

The estimation of disease progression in Alzheimer’s disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression

Level Set Segmentation Using a Point-Based Statistical Shape Model Relying on Correspondence Probabilities

International audienceIn order to successfully perform automatic segmentation in medical images containing noise and intensity inhomogeneities, modern algorithms often rely on a priori knowledge about the respective anatomy. This is often introduced by statistical shape models (SSMs) which are typically based on one-to-one point correspondences. In this work, we propose a unified statistical framework for image segmentation with shape prior information. The shape prior is an explicitely represented probabilistic SSM based on point correspondence probabilities, and the segmentation contour is implicitly represented by the zero level set of a higher dimensional surface. These two aspects are unified in a Maximum a Posteriori (MAP) estimation where the level set is evolved to converge towards the boundary of the organ to be segmented based on the image information while taking into account the prior given by the SSM information. The optimization of the MAP formulation leads to an alternate update of the level set and an update of the fitting of the SSM. We demonstrate the efficiency of our new algorithm with soft tissue segmentation where adaptive weights ensure that the SSM constraint is optimally exploited. Our experimental results show the well-posedness of the approach on noisy kidney CT data impaired by breathing artefacts

To illustrate the functionality of progress estimation for synthetic data, the mean estimation errors are computed based on a set of <i>n</i>_test = 100 randomly generated test samples.

<p>The figures show the mean errors for <i>n</i>_runs = 100 runs of the experiments. The models correspond to the models analysed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153040#pone.0153040.g004" target="_blank">Fig 4B</a>. (A) Sensitivity to the sampling strategy. (B) Influence of additional data from more visits and multiple biomarkers.</p

Results for a classification based on the DP as single feature, which is estimated using image-based biomarkers (see Fig 7).

<p>The numbers indicate the mean accuracy (ACC), sensitivity (SENS) and specificity (SPEC) after 100 runs of a 10-fold cross-validation.</p

Overview on the synthetic models with <i>p</i>′ := <i>p</i> + 1095.

<p>Overview on the synthetic models with <i>p</i>′ := <i>p</i> + 1095.</p

Results for the prediction of future biomarker values for four different biomarkers (tree cognitive scores and the hippocampal volume).

<p>The prediction of the value at m36 is based on bl, m12 and m24 visits, using the ADNI data. Bold median values indicate a statistically significant improvement over the naive approach (<i>p</i> < 0.01).</p

Illustration of the model extrapolation approach.

<p>Illustration of the model extrapolation approach.</p

To measure the influence of the data pool on the model training, the sensitivity of quantile regression using VGAMs to different properties of the sampling set is analysed.

<p>The graphs show the mean reconstruction error after 100 cycles of random sample generation and model training. The numbers above the boxes indicate the median error. (A) Sensitivity to the number of samples. (B) Sensitivity to the sampling strategy. (C) Robustness against temporal misalignment. (D) Sensitivity to different progression rates.</p

Ranking of the 10 most discriminative progression models.

<p>D1 and D2 denote the first two ML coordinates.</p