Personalized mathematical modeling of Glioblastoma
Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. However, current radiation planning is empirically-based, guided by population-level data, and does not account for patient-specific heterogeneities that are a hallmark of GBM. Here, we present a novel personalized approach for inferring glioma evolution and spatial distribution. The framework includes simulation of glioblastoma growth, pressure distributions and brain deformations in individual patient brain anatomies obtained from medical images. A distinctive feature of our approach is that the pressure is directly derived from tumor dynamics and patient-specific anatomy, providing non-invasive insights into the patient state. A diffuse-domain formalism is deployed to allow efficient numerical implementation directly in the patient brain anatomy. We discuss methods for model calibration and the development of personalized radiotherapy plans. The model is tested on synthetic and clinical cases. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline-shift, or neurological and cognitive impairments.