Purpose: We developed a tumor model for glioblastomas with the aim to study tumor growth, transition from avascular to vascular, oxygenation effects, cell repair, and to predict glioma control or recurrence when the malignancy is treated with ionizing radiation. Methods: Anatomical structures imaged using MRI were contoured and compartmentalized to simulate white matter, grey matter, and vasculature structures proximal to a simulated glioblastoma tumor. The gliomas were modeled using micro compartments to represent groups of tumor cells of the same structure and functionality. The model incorporates the interactions between healthy and tumor tissue using a mechanistic approach based on forces and link strengths between cells and the extra cellular matrix. The evolution and response of the cells to biomolecules, nutrients, and oxygen distribution were modeled through discrete transport and diffusion equations. Cell biology was simulated using parameters that represent nutrient consumption, cell cycle, cell history, and cell oxygenation. The delivered dose was modeled using a probabilistic approach to compute the likelihood of DNA damage and repair. Results: We successfully simulated tumor growth, invasion, and disruption of the local anatomy and tumor control or recurrence under ionizing radiation stress. We modeled tumor radiotherapy under hypoxic and oxic conditions. Results of these numerical experiments show qualitative agreement with observed tumor evolution and response to ionizing radiation treatment. Transition from an avascular to a vascular tumor and recruitment of blood vessels was also successfully modeled. Spatial resolution of compartments is higher than current imaging devices, making our model a valuable tool to link simulations with anatomical and functional imaging. Conclusions: We developed a micro compartmental model of glioblastoma tumors to evaluate the role of the local anatomy and the microenvironment oxygenation when the malignancy is treated with ionizing radiation. The model can be used to predict glioblastoma growth and response to radiotherapy.