Glioma growth modeling based on the effect of vital nutrients and metabolic products.

Glioma brain tumors exhibit considerably aggressive behavior leading to high mortality rates. Mathematical modeling of tumor growth aims to explore the interactions between glioma cells and tissue microenvironment, which affect tumor evolution. Leveraging this concept, we present a three-dimensional model of glioma spatio-temporal evolution based on existing continuum approaches, yet incorporating novel factors of the phenomenon.

A Glycolysis-Based In Silico Model for the Solid Tumor Growth.

Cancer-tumor growth is a complex process depending on several biological factors, such as the chemical microenvironment of the tumor, the cellular metabolic profile, and its proliferation rate. Several mathematical models have been developed for identifying the interactions between tumor cells and tissue microenvironment, since they play an important role in tumor formation and progression. Toward this direction we propose a new continuum model of avascular glioma-tumor growth, which incorporates a new factor, namely, the glycolytic potential of cancer cells, to express the interactions of three different tumor-cell populations (proliferative, hypoxic, and necrotic) with their tissue microenvironment.

Mathematical modelling of spatio-temporal glioma evolution.

Background: Gliomas are the most common types of brain cancer, well known for their aggressive proliferation and the invasive behavior leading to a high mortality rate. Several mathematical models have been developed for identifying the interactions between glioma cells and tissue microenvironment, which play an important role in the mechanism of the tumor formation and progression.

Methods: Building and expanding on existing approaches, this paper develops a continuous three-dimensional model of avascular glioma spatio-temporal evolution.

Image analysis techniques for automated IVUS contour detection.

Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented.

A novel active contour model for fully automated segmentation of intravascular ultrasound images: in vivo validation in human coronary arteries.

The detection of lumen and media-adventitia borders in intravascular ultrasound (IVUS) images constitutes a necessary step for the quantitative assessment of atherosclerotic lesions. To date, most of the segmentation methods reported are either manual, or semi-automated, requiring user interaction at some extent, which increases the analysis time and detection errors. In this work, a fully automated approach for lumen and media-adventitia border detection is presented based on an active contour model, the initialization of which is performed via an analysis mechanism that takes advantage of the inherent morphologic characteristics of IVUS images.

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation.

>Intravascular ultrasound (IVUS) has become in the last years an important tool in both clinical and research applications. The detection of lumen and media-adventitia borders in IVUS images represents a first necessary step in the utilization of the IVUS data for the 3D reconstruction of human coronary arteries and the reliable quantitative assessment of the atherosclerotic lesions. To serve this goal, a fully automated technique for the detection of lumen and media-adventitia boundaries has been developed.