HIT Featured Talks: Maria Cristina Colombo
HIT Centre, via Luzzatti 4, Padua
08.06.2016
A Mathematical Model to Predict Glioblastoma Invasion: use of patient-specific diffusion tensor imaging data in simulations
Time: 08 JUNE 2016 - 18:00
Venue: MEETING ROOM at HIT Centre, via Luzzati 4
Speaker: Maria Cristina Colombo
Affiliation: TRILOG Spa, Milan
Abstract:
Even though brain tumors account for only 2-3% of all cancers, they are responsible for 7% of the years of life lost from cancer before the age of 70. Among them, the most aggressive is the glioblastoma, a highly malignant cancer that arises in the neuroglia, the supportive tissue of the neurons. Glioblastoma presents long extensions that infiltrate deeply the white matter, following the alignment of the fibers. From the medical viewpoint, this peculiarity makes it difficult to treat. For the same reasons, in the last years, biomathematical modeling applied to infiltrative brain tumor has gained in importance. Indeed, a good model could offer a better understanding of the microstrucutral dynamics of the cancer and thus it could be helpful to predict its evolution. In this study, we propose a diffuse interface binary mixture model which consists of a fourth order non-linear equation for the cancerous cellular fraction coupled with a reaction diffusion equation for the nutrient component. The model takes into account the mechanical dynamics, e.g. adhesive forces or viscous interactions among cells, and the chemotactic cellular movement in response to certain environment factors. Moreover, we include brain tissue heterogeneity and anisotropy in the model by the introduction of patient-specific diffusion tensor imaging data, thanks to which we manage to probe brain fibers architecture. The aim of this research is to demonstrate the importance of considering anisotropy, heterogeneity and patient-specific data into mathematical models in order to better predict the tumor growth. Specifically, we deal with the theoretical and the numerical framework of the mathematical model proposed. Starting from a real MR of a patient affected by glioblastoma and using imaging techniques, we create a patient-specific computational mesh and we extract the necessary data from the DTI medical images. Then we develop numerical codes making use of an open-source software name FEniCS. To study the anisotropic development of the tumor in relation to the biological parameters presented in the model, we per-form a sensitivity analysis on a homogeneous geometry. Finally, we provide two numerical tests that simulate common clinical situations.
