Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation

The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving the understanding of neurodegenerative disorders, and for providing a reference for the prediction and quantification of the pathology in unseen individuals. Nonetheless, the use of disease progression models for probabilistic predictions still requires investigation, for example for accounting for missing observations in clinical data, and for accurate uncertainty quantification. We tackle this problem by proposing a novel Gaussian process-based method for the joint modeling of imaging and clinical biomarker progressions from time series of individual observations. The model is formulated to account for individual random effects and time reparameterization, allowing non-parametric estimates of the biomarker evolution, as well as high flexibility in specifying correlation structure, and time transformation models. Thanks to the Bayesian formulation, the model naturally accounts for missing data, and allows for uncertainty quantification in the estimate of evolutions, as well as for probabilistic prediction of disease staging in unseen patients. The experimental results show that the proposed model provides a biologically plausible description of the evolution of Alzheimer's pathology across the whole disease time-span as well as remarkable predictive performance when tested on a large clinical cohort with missing observations.Comment: 13 pages, 2 figure

Fast Predictive Simple Geodesic Regression

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.Comment: 19 pages, 10 figures, 13 table

Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Implicit measures of actual versus ideal body image : relations with self-reported body dissatisfaction and dieting behaviors

Body dissatisfaction refers to a negative appreciation of one’s own body stemming from a discrepancy between how one perceives his/her body (actual body image) and how he/she wants it to be (ideal body image). To circumvent the limitations of self-report measures of body image, measures were developed that allow for a distinction between actual and ideal body image at the implicit level. The first goal of the present study was to investigate whether self-reported body dissatisfaction is related to implicit measures of actual and ideal body image as captured by the Relational Responding Task (RRT). Secondly, we examined whether these RRT measures were related to several indices of dieting behavior. Women high in body dissatisfaction (n = 30) were characterized by relatively strong implicit I-am-fat beliefs, whereas their implicit I-want-to-be-thinner beliefs were similar to individuals low in body dissatisfaction (n = 37). Implicit body image beliefs showed no added value over explicit body image beliefs in predicting body dissatisfaction and dieting behavior. These findings support the idea that the interplay between ideal and actual body image drives (self-reported) body dissatisfaction. However, strong support for the view that it would be critical to differentiate between explicit and implicit body image beliefs is missing

Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration.

We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer

Predicting the On-Study Relapse Rate for Multiple Sclerosis Patients in Clinical Trials

Background: The annual relapse rate has been commonly used as a primary efficacy endpoint in phase III multiple sclerosis (MS) clinical trials. The aim of this study was to determine the relative contribution of different possible prognostic factors available at baseline to the on-study relapse rate in MS. Methods: A total of 821 patients from the placebo arms of the Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) database were available for this analysis. The univariate relationships between on-study relapse rate and the baseline demographic, clinical, and MRI-based predictors were assessed. The multiple relationships were then examined using a Poisson regression model. Two predictor subsets were selected. Subset 1 included age at disease onset, disease duration, gender, Expanded Disability Status Scale (EDSS) at baseline, number of relapses in the last 24 months prior to baseline, and the disease course (RR and SP). Subset 2 consisted of Subset 1 plus gadolinium enhancement status in MRI. The number of patients for developing the models with no missing values was 727 for Subset 1 and 306 for Subset 2. Results:The univariate relationships show that the on-study relapse rate was higher for younger and for female patients, for RR patients than for SP patients, and for patients with positive enhancement status at entry (Wilcoxon test, p<0.05). A higher on-study relapse rate was associated with a shorter disease duration, lower entry EDSS, more pre-study relapses and more enhancing lesions in T1 at entry. The fitted Poisson model shows that disease duration (estimate=-0.02) and previous relapse number (estimate=0.59 for 1, 0.91 for 2 and 1.45 for 3 or more relapses vs 0 relapse) remain. We were able to confirm these findings in a second, independent dataset. Conclusions: The relapse number prior to entry into clinical trials together with disease duration are the best predictors for the on-study relapse rate. Disease course and gadolinium enhancement status, given the other covariates, have no significant influence on the on-study relapse rate

Tracking of toddler fruit and vegetable preferences to intake and adiposity later in childhood

This study examined whether toddlers' liking for fruit and vegetables (FV) predicts intake of FV later in childhood, how both relate to childhood adiposity and how these were moderated by factors in infancy. Children in the Gateshead Millennium Study were recruited at birth in 1999–2000. Feeding data collected in the first year were linked to data from a parental questionnaire completed for 456 children at age 2.5 years (30 m) and to anthropometry, skinfolds and bioelectrical impedance and 4‐day food diary data collected for 293 of these children at age 7 years. Aged 30 months, 50% of children were reported to like eight different vegetables and three fruits, but at 7 years, children ate a median of only 1.3 (range 0–7) portions of vegetables and 1.0 portion of fruit (0–4). Early appetite, feeding problems and food neophobia showed significant univariate associations with liking for FV aged 30 m, but the number of vegetables toddlers liked was the only independent predictor of vegetable consumption at age 7 years (odds ratio (OR) 1.28 p &lt; 0.001). Liking for fruit aged 30 m also independently predicted fruit intake (OR = 1.31, p = 0.016), but these were also related to deprivation (OR = 2.69, p = 0.001) maternal education (OR = 1.28, p = 0.039) and female gender (OR = 1.8, p = 0.024). Children eating more FV at age 7 years had slightly lower body mass index and skinfolds. An early liking for FV predicted increased later intake, so increasing early exposure to FV could have long term beneficial consequences