In-Depth Analysis and Evaluation of Diffusive Glioma Models

• Published in: IEEE transactions on information technology in biomedicine Vol. 16; no. 3; pp. 299 - 307
• Main Authors: Roniotis, A., Sakkalis, V., Karatzanis, I., Zervakis, M. E., Marias, K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2012
• Subjects: Accuracy; Approximation methods; Brain Neoplasms - pathology; Cancer modeling; Computational Biology - methods; computational oncology; Computer Simulation; diffusion reaction equation; Equations; Finite element methods; glioblastoma; Glioma - pathology; Humans; Mathematical model; Middle Aged; Models, Biological; Numerical models; Tumors
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2012.2185704

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.