Towards a Framework for Predictive Mathematical Modeling of the Biomechanical Forces Causing Brain Tumor Mass-Effect

GBMs present with different growth phenotypes, ranging from invasive lesions without notable mass-effect to strongly displacing lesions that induce mechanical stresses and result in healthy-tissue deformation, midline shift or herniation. Biomechanical forces, such as those resulting from displacive tumor growth, are recognized to shape the tumor environment and to contribute to tumor progression. We therefore expect that biomechanical forces exerted by lesions on the brain parenchyma have implications on the biophysical level, and that they may affect treatment response and outcome. To better understand the role of biomechanics in the formation of different GBM phenotypes we started developing a framework for the predictive mathematical modeling of mechanical tumor-healthy tissue interaction on the macroscopic level. The tumor’s mass-effect is represented by a solid-mechanics model of brain tissue that computes tumor-induced strain based on local tumor cell concentration. The framework allows to seed tumors at multiple locations in a human brain atlas. It simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. Despite its simplicity, the mathematical model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to publicly available GBM imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. Here we present and evaluate an extended version of this mechanically-coupled reaction-diffusion model that takes into account tissue anisotropies based on MRI diffusion tensor imaging (MR-DTI). Structural anisotropies in brain tissue have been found to affect the directionality of tumor cell migration and are critical to mechanical behavior. This makes them likely to play a role also in the development of GBM phenotypes