Michalis E. Zervakis

High-Grade Glioma Diffusive Modeling Using Statistical Tissue Information and Diffusion Tensors Extracted from Atlases

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.

In-Depth Analysis and Evaluation of Diffusive Glioma Models

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.

On the Identification of Circulating Tumor Cells in Breast Cancer

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.

Synchronization coupling investigation using ICA cluster analysis in resting MEG signals in reading difficulties

The understanding of the mechanisms of human brain is a demanding issue for neuroscience research. Physiological studies acknowledge the usefulness of synchronization coupling in the study of dysfunctions associated with reading difficulties. Magnetoencephalogram (MEG) is a useful tool towards this direction having been assessed for its superior accuracy over other modalities. In this paper we consider synchronization features for identifying brain operations. Independent Component Analysis (ICA) is applied on MEG surface signals in controls and children with reading difficulties and are clustered to representative components. Then, coupling measures of mutual information and partial directed coherence are estimated in order to reveal dysfunction of cerebral networks and its related coordination.

Efficient Molecular Imaging Techniques Using Optically Active Molecules

Efficient imaging techniques aimed at the increasing of the image contrast of a structure, surrounded by a scattering medium, using optically active and high index of refraction molecules as molecular contrast agents, are presented. Specifically, an enhanced degree of linear polarization (DOLP) target detection and imaging is obtained by doping the surrounding medium with molecular contrast agents consisting of aqueous glucose, aqueous alcohol, and salt molecules, in conjunction with advanced polarimetric imaging techniques. The outcome of this paper opens new horizons in the areas of imaging, with emphasis on medical arena, industry, and detection technology.

Polynomial and RBF Kernels as Marker Selection Tools-A Breast Cancer Case Study

The problem of marker selection in DNA microarray experiment, due to the curse of dimensionality, has been mostly addressed so far by linear approaches. Taking into account the fact that the domain of interest is a complex one, where non-linear interconnections and dependencies may also exist among the extremely large number of examined genes, we address the use of nonlinear tools to assess the problem. In this study, we propose to apply the kernel ability of Support Vector Machines in combination with Fishers ratio as an alternative approach to assess the problem.

Modeling stent deployment in realistic arterial segment geometries: The effect of the plaque composition

Stents are medical devices used in cardiovascular intervention for unblocking the diseased arteries and restoring blood flow. During stent implantation the deformation of the arterial wall as well as the resulted stresses caused in the arterial morphology are studied. In this paper we study the effect of the composition of the atherosclerotic plaque during the stent deployment procedure, using Finite Element modeling. The stenting procedure is simulated for two different cases; in the first the presence of the plaque is ignored whereas in the second a three dimensional (3D) stiff calcified plaque is located in the stenotic area of the artery. Results indicate that in the second case the von Mises stresses in the arterial wall are higher than the stresses occurred in the first case. In addition, the distribution of the arterial von Mises stress depends on the plaque composition.

On the Relevance of Glycolysis Process on Brain Gliomas

The proposed analysis considers aspects of both statistical and biological validation of the glycolysis effect on brain gliomas, at both genomic and metabolic levels. In particular, two independent datasets are analyzed in parallel: one engaging genomic (microarray expression) data and the other metabolomic (magnetic resonance spectroscopy imaging) data. The aim of this study is twofold. First to show that, apart from the already studied genes (markers), other genes such as those involved in the human cell glycolysis significantly contribute in gliomas discrimination. Second, to demonstrate how the glycolysis process can open new ways toward the design of patient-specific therapeutic protocols. The results of our analysis demonstrate that the combination of genes participating in the glycolytic process (ALDOA, ALDOC, ENO2, GAPDH, HK2, LDHA, LDHB, MDH1, PDHB, PFKM, PGI, PGK1, PGM1, and PKLR) with the already known tumor suppressors (PTEN, Rb, and TP53), oncogenes (CDK4, EGFR, and PDGF), and HIF-1 enhance the discrimination of low- versus high-grade gliomas, providing high prediction ability in a cross-validated framework. Following these results and supported by the biological effect of glycolytic genes on cancer cells, we address the study of glycolysis for the development of new treatment protocols.

Complementary Gene Signature Integration in Multiplatform Microarray Experiments

The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. In this paper, we verify the aforementioned fact, but we also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature. We proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions, we derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution.

Multi-platform Data Integration in Microarray Analysis

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray plat forms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among platforms scale implemented by Shen et at. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.