Speaker
Description
Brain tumors remain one of the most formidable challenges in medicine, largely due to their unpredictable localization and varying degrees of malignancy. Their notorious aggressiveness in spreading often limits the efficacy of standard treatments. As the tumor mass grows, it compresses and displaces surrounding healthy tissues, often altering the volume of the cerebral ventricles and increasing intracranial pressure. Today, the standard of care typically relies on surgical resection, supplemented by radiotherapy and chemotherapy when needed.
To better understand and predict this behavior, this work introduces a multiphase mechanical model designed to simulate brain tumor growth. Specifically, our framework quantifies the solid deformations and stresses driven by tumor expansion. Crucially, the model incorporates the directionality of white matter fibers to capture the tumor's anisotropic growth patterns. By leveraging patient-specific Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) data, we are able to reconstruct highly realistic 3D brain geometries with precise ventricular representations. This allows us to deeply analyze how tumor growth mechanically impacts both the ventricles and the adjacent healthy tissue.
Through finite element simulations implemented in FEniCS, our numerical results highlight the model's accuracy in capturing the complex dynamics of tumor expansion and its mechanical consequences. Ultimately, the insights generated by this predictive framework hold significant potential to guide targeted, patient-specific therapeutic strategies, improving the overall clinical management of brain tumors.