Simulating radiotherapy effect in high-grade glioma by using diffusive modeling and brain atlases

Area of Science:

• Oncology
• Computational Biology
• Medical Imaging

Background:

• Glioblastoma (GBM) is an aggressive brain tumor requiring advanced predictive models.
• Diffusive models are crucial for simulating tumor cell spatiotemporal dynamics.
• Radiotherapy is a key treatment modality for GBM, necessitating accurate simulation of its effects.

Purpose of the Study:

• To apply a linear quadratic model to an advanced diffusive glioma model for simulating radiotherapy effects.
• To incorporate heterogeneous glioma velocities in gray and white matter and anisotropic cell migration along white fibers.
• To validate the model using real clinical datasets.

Main Methods:

• Utilized a diffusive model for simulating glioblastoma (GBM) spatiotemporal concentration changes.
• Integrated a linear quadratic model to represent radiotherapy effects.
• Employed normal brain atlases to extract gray/white matter proportions and diffusion tensors for anisotropy analysis.

Main Results:

• The advanced diffusive glioma model successfully simulated radiotherapy effects.
• The model accounted for heterogeneous tumor cell migration velocities and anisotropy in brain tissue.
• Validation against real clinical datasets demonstrated the model's applicability.

Conclusions:

• The developed diffusive glioma model provides a robust framework for simulating radiotherapy in glioblastoma.
• Incorporating tissue heterogeneity and anisotropy enhances predictive accuracy in oncology.
• This approach aids in personalized treatment planning for brain tumors.