Sparse classification with MRI based markers for neuromuscular disease categorization

International audienceIn this paper, we present a novel method for disease classification between two patient populations based on features extracted from Magnetic Resonance Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the k-support SVM (ksup-SVM). The k-support regularized SVM has an inherent feature selection property, and thus it eliminates the requirement for a separate feature selection step. Our dataset consists of patients that suffer from facioscapulohumeral muscular dystrophy (FSH) and Myotonic muscular dystrophy type 1 (DM1) and our proposed method achieves a high performance. More specifically, it achieves a mean Area Under the Curve (AUC) of 0.7141 and mean accuracy 77% ± 0.013. Moreover, we provide a sparsity visualization of the features in order to indentify their discriminative value. The results suggest the potential of the combined use of MR markers to diagnose myopathies, and the general utility of the ksup-SVM. Source code is also available at https://gitorious.org/ksup-svm

Disease Progression Modeling in Chronic Obstructive Pulmonary Disease

Contains fulltext : 220761.pdf (Publisher’s version ) (Closed access)Rationale: The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging.Objectives: To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD.Methods: We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data.Measurements and Main Results: We identified two trajectories of disease progression in COPD: a "Tissue-->Airway" subtype (n = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway-->Tissue" subtype (n = 988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1/FVC decline (r = -0.16 [P Airway group; r = -0.14 [P = 0.011] in the Airway-->Tissue group). SuStaIn placed 30% of smokers with normal lung function at elevated stages, suggesting imaging changes consistent with early COPD. Individuals with early changes were 2.5 times more likely to meet COPD diagnostic criteria at follow-up.Conclusions: We demonstrate two distinct patterns of disease progression in COPD using SuStaIn, likely representing different endotypes. One third of healthy smokers have detectable imaging changes, suggesting a new biomarker of "early COPD.

Synergy between anti-CCL2 and docetaxel as determined by DW-MRI in a metastatic bone cancer model

Metastatic prostate cancer continues to be the second leading cause of cancer death in American men with an estimated 28,660 deaths in 2008. Recently, monocyte chemoattractant protein-1 (MCP-1, CCL2) has been identified as an important factor in the regulation of prostate metastasis. CCL2, shown to attract macrophages to the tumor site, has a direct promotional effect on tumor cell proliferation, migration, and survival. Previous studies have shown that anti-CCL2 antibodies given in combination with docetaxel were able to induce tumor regression in a pre-clinical prostate cancer model. A limitation for evaluating new treatments for metastatic prostate cancer to bone is the inability of imaging to objectively assess response to treatment. Diffusion-weighted MRI (DW-MRI) assesses response to anticancer therapies by quantifying the random (i.e., Brownian) motion of water molecules within the tumor mass, thus identifying cells undergoing apoptosis. We sought to measure the treatment response of prostate cancer in an osseous site to docetaxel, an anti-CCL2 agent, and combination treatments using DW-MRI. Measurements of tumor apparent diffusion coefficient (ADC) values were accomplished over time during a 14-day treatment period and compared to response as measured by bioluminescence imaging and survival studies. The diffusion data provided early predictive evidence of the most effective therapy, with survival data results correlating with the DW-MRI findings. DW-MRI is under active investigation in the pre-clinical and clinical settings to provide a sensitive and quantifiable means for early assessment of cancer treatment outcome

Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics

International audienceIn this paper, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algo-rithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects