Glioblastoma Cell Count Approximation through Mathematical Modeling (2024)

Undergraduates: Aryan Kodali, Nathaniel Burchette

Faculty Advisor: Ivan Cherednik
Department: Mathematics

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This study develops mathematical models to predict tumor sizes in glioblastoma multiforme (GBM), a notably aggressive brain tumor. It critiques existing estimation methods and presents a new model grounded in empirical data and mathematical fundamentals. The analysis is based on three assumptions: uniform cell composition, spatial independence, and minimal external impact on growth. It outlines GBM growth phases: rapid expansion, steady progression, and eventual saturation. The models elucidate the complex dynamics of GBM growth and aim to enhance tumor size prediction in clinical settings. Future work will focus on refining these models to determine optimal surgery timing for GBM patients.