Stochastic modeling of glioblastoma spread: a numerical simulation study

Glioblastoma, themost aggressive form of primary brain tumor, presents significant challenges in clinical management and research due to its invasive nature and resistance to standard therapies. Mathematicalmodeling offers a promising avenue to understand its complex dynamics and develop innovative treatment strategies. Building upon previous research, this paper reviews and adapts some existing mathematical formulations to themodeling study of glioblastoma infiltration and growth, utilizing the Partial Differential Equation (PDE) formalismto describe the time-varying and space-dependent cancer cell density. Experimental data fromthe literature are nicely reproduced and can be better interpreted based on themodel behavior. Simulations highlight that the proposed framework is promising for further investigations.