Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations.

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns.

Glioblastoma is the most aggressive malignant primary brain tumor with a poor prognosis. Glioblastoma heterogeneous neuroimaging, pathologic, and molecular features provide opportunities for subclassification, prognostication, and the development of targeted therapies. Magnetic resonance imaging has the capability of quantifying specific phenotypic imaging features of these tumors. Additional insight into disease mechanism can be gained by exploring genetics foundations. Here, we use the gene expressions to evaluate the associations with various quantitative imaging phenomic features extracted from magnetic resonance imaging. We highlight a novel correlation by carrying out multi-stage genomewide association tests at the gene-level through a non-parametric correlation framework that allows testing multiple hypotheses about the integrated relationship of imaging phenotype-genotype more efficiently and less expensive computationally. Our result showed several novel genes previously associated with glioblastoma and other types of cancers, as the LRRC46 (chromosome 17), EPGN (chromosome 4) and TUBA1C (chromosome 12), all associated with our radiographic tumor features.

Shape-based assessment of vertebral fracture risk in postmenopausal women using discriminative shape alignment.

RATIONALE AND OBJECTIVES: Risk assessment of future osteoporotic vertebral fractures is currently based mainly on risk factors, such as bone mineral density, age, prior fragility fractures, and smoking. It can be argued that an osteoporotic vertebral fracture is not exclusively an abrupt event but the result of a decaying process. To evaluate fracture risk, a shape-based classifier, identifying possible small prefracture deformities, may be constructed. MATERIALS AND METHODS: During a longitudinal case-control study, a large population of postmenopausal women, fracture free at baseline, were followed. The 22 women who sustained at least one lumbar fracture on follow-up represented the case group. The control group comprised 91 women who maintained skeletal integrity and matched the case group according to the standard osteoporosis risk factors. On radiographs, a radiologist and two technicians independently performed manual annotations of the vertebrae, and fracture prediction using shape features extracted from the baseline annotations was performed. This was implemented using posterior probabilities from a standard linear classifier. RESULTS: The classifier tested on the study population quantified vertebral fracture risk, giving statistically significant results for the radiologist annotations (area under the curve, 0.71 ± 0.013; odds ratio, 4.9; 95% confidence interval, 2.94-8.05). CONCLUSIONS: The shape-based classifier provided meaningful information for the prediction of vertebral fractures. The approach was tested on case and control groups matched for osteoporosis risk factors. Therefore, the method can be considered an additional biomarker, which combined with traditional risk factors can improve population selection (eg, in clinical trials), identifying patients with high fracture risk.

A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs.

We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.