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

• Published in: Medical & biological engineering & computing Vol. 56; no. 9; pp. 1683 - 1697
• Main Authors: Papadogiorgaki, Maria, Koliou, Panagiotis, Zervakis, Michalis E.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2018; Springer Nature B.V
• Subjects: Biological effects; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Brain; Brain cancer; Brain tumors; Computer Applications; Computer simulation; Diffusion rate; Evolution; Glioma; Glioma cells; Human Physiology; Imaging; Mathematical analysis; Mathematical models; Metabolism; Nutrients; Original Article; Radiology; Three dimensional models; Vital nutrients; Metabolic wastes; Diffusion coefficient; Proliferation rate; Glioma tumor modeling
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-018-1809-0

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. The proposed model involves the interactions between different tumor cell phenotypes and their microenvironment, investigating how tumor growth is affected by complex biological exchanges. It focuses on the separate and combined effect of vital nutrients and cellular wastes on tumor expansion, leading to the formation of cell populations with different metabolic, proliferative, and diffusive profiles. Several simulations were performed on a virtual and a real glioma, using combinations of proliferation and diffusion rates for different evolution times. The model results were validated on a glioma model available in the literature and a real case of tumor progression. The experimental observations indicate that our model estimates quite satisfactorily the expansion of each region and the overall tumor growth. Based on the individual results, the proposed model may provide an important research tool for patient-specific simulation of different tumor evolution scenarios and reliable estimation of glioma evolution. Graphical Abstract Outline of the mathematical model functionality and application to glioma growth with indicative results