Optimal time-step selection for accurate simulation results in the tumor growth simulation utility (TG-SU)

Accurate simulation of brain tumor growth is essential for predicting patient prognosis and evaluating treatment efficacy. The Tumor Growth Simulation Utility (TG-SU), based on the Reaction Diffusion Equation (RDE) and Finite Element (FE) method, provides a spatio-temporal framework to simulate glioma progression using segmented brain images. However, the choice of simulation time-step (dt) significantly impacts the accuracy and computational cost of the simulation. This study investigates the effect of varying time-step values on simulation error and computational rounds. Results show that while smaller time-steps improve accuracy, they increase simulation time. An equation is proposed to determine the optimal time-step for a given maximum acceptable error. This balance between speed and precision is crucial for practical neuro-oncology applications.