| Abstract: |
| Glioblastoma remains one of the most challenging malignancies to treat, owing in part to its aggressive and heterogeneous progression. Developing reliable predictive frameworks for tumor dynamics has the potential to inform clinical decision-making and improve patient outcomes. In this talk, we present a computational approach to modeling glioblastoma progression, integrating mathematical and data-driven techniques to capture key features of tumor behavior. We discuss the theoretical foundations of our framework, its predictive capabilities, and directions for future development. |
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