Computer implementation of a new therapeutic model for GBM tumor

Area of Science:

• Oncology
• Mathematical Biology
• Radiotherapy

Background:

• Modeling tumor behavior and cell proliferation is crucial for effective cancer treatment.
• Glioblastoma multiforme (GBM) presents complex challenges in predicting treatment response.

Purpose of the Study:

• To develop a novel mathematical model for estimating glioblastoma multiforme (GBM) tumor growth and normal cell proliferation.
• To utilize this model for optimizing radiation therapy and potentially increasing patient survival.

Main Methods:

• A new differential equation model incorporating radiation dose delivery was developed.
• Gene expression programming (GEP) was employed for model estimation, enhanced by the Linear-Quadratic (LQ) model.
• The model predicts cell counts based on initial cell status, dose timing/amount, and brain condition.

Main Results:

• The model accurately predicts tumor and normal brain cell populations during treatment.
• A critical normal cell level was defined, indicating patient mortality.
• Model predictions were validated against clinical data and experimental results.

Conclusions:

• The proposed mathematical model offers a tool for predicting tumor growth dynamics under radiation therapy.
• This predictive capability can inform and control treatment strategies for glioblastoma patients.
• The model aids in optimizing radiation dose and timing for improved therapeutic outcomes.