A Dynamic Model of Glioma Tumor Growth Based on Changes in Blood Glucose Concentration using Runge-Kutta Order 45

Abstract

Abstract The aggressive nature of the glioma tumor and the low survival rate of glioma sufferers make it very difficult to cure glioma tumors. The use of biomarkers in the blood to detect early glioma tumors is necessary for the evaluation of special treatment in patients with these tumors. Because there is a relationship between blood glucose concentrations and glioma tumors. This also opens up opportunities for the early detection of glioma tumors using the dynamic model method. This study presents the model of coupled ordinary differential equations to describe glioma growth based on changes in blood glucose concentration. This dynamic model contains glioma-glucose-immune interaction. The Runge-Kutta Order 45 function that has been created gives similar results with the MATLAB ode solver (ode45, ode23s, and ode15s). The simulation results indicate that there is an inverse relationship between glioma tumor growth (active state not in a dormant state) and a decrease in blood glucose serum levels. Also, there is a relationship between glioma growth and the immune system, which is an important element in controlling glioma tumor growth.