Evaluation of a mechanically-coupled reaction-diffusion model for macroscopic brain tumor growth

Abler, Daniel; Büchler, Philippe (September 2016). Evaluation of a mechanically-coupled reaction-diffusion model for macroscopic brain tumor growth (Unpublished). In: 14th International Symposium Computer Methods in Biomechanics and Biomedical Engineering (CMBBE). Tel Aviv, Israel. 20.-22.09.2016.

Macroscopic growth of brain tumors has been studied by means of different computational modeling approaches. Glioblastoma multiforme (GBM), the most frequent malignant histological type, is commonly modeled as a reaction-diffusion type system, accounting for its invasive growth pattern, e.g. [1]. Purely mechanical models have been proposed to represent the mass-effect caused by the growing tumor, e.g.
[2]. Only few models, such as [3], consider both effects in a single 3D model.

We report first results of a comparative study that evaluates the ability of a simple computational model to reproduce the shape of pathologies and healthy tissue deformations found in patients.

We use the finite element method (FEM) for simulating GBM invasion into brain tissue and the mechanical interaction between tumor and healthy tissue components. Cell proliferation and invasion is modeled as a reaction-diffusion process; the simulation of the mechanic interaction relies on a linear-elastic material model. Both are coupled by relating local increase in tumor cell concentration to the generation of isotropic strain in the corresponding tissue element. The model accounts for multiple brain regions with values for proliferation, isotropic diffusion and mechanical properties derived from literature.

Tumors were seeded at multiple locations in FEM models derived from publicly available human brain atlases. Simulation results for a given tumor volume were compared to patient images, using a metric that takes into account extent and shape of the tumor, as well as healthy tissue deformation. Model parameterizations resulting in simulated tumors most similar to real-world pathologies were identified
by systematic variation of seed location and relative magnitude of diffusion and mechanical coupling.

1. Swanson et al., J. Neurol. Sci., 216 (1):1-10, 2003.
2. Hogea et al., Phys. Med. Biol., 52 (23):6893-6908, 2007.
3. Clatz et al., IEEE Trans. Med. Imag., 24 (10):1334-1346, 2005.

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