Designing a simulator for implementing Lea's target radiobiological model in cancer treatment

Numerous radiobiological models have been developed to evaluate the cell-killing effects in radiotherapy. However, many commonly adopted models face limitations, such as reduced accuracy in predicting the effects of specific radiation types or in complex biological conditions. Lea's target model was chosen for this study due to its established mathematical foundation and its ability to model cell survival in response to high-dose radiation scenarios, making it a suitable framework for clinical applications. This study applies Lea's target theory to model the relative biological effectiveness (RBE) and calculate the cell survival fraction for radiation therapy. A MATLAB standalone application, featuring a graphical user interface, was developed to enable easy input of parameters such as tumor target volume, the number of targets, and the number of hits. The application is designed for practical use, allowing clinicians and researchers to simulate and analyze survival fractions efficiently. Results demonstrate a mathematical relationship where an increase in the number of hits (n) (the number of times a cell target is hit by radiation particles) leads to a proportional increase in cell survival fraction. Specifically, under standard parameters of a 1 cm³ cell volume (V) and 5 targets (N), higher values of n show a marked improvement in survival predictions. Simulations revealed that varying n while holding other parameters constant results in a predictable survival fraction curve, emphasizing the sensitivity of survival to hit probability. The successful development of a simulator using Lea's target model provides an accurate and efficient tool for predicting cell survival fractions in radiation therapy. This represents a significant step forward in improving both treatment planning and patient outcome prediction. The tool's ability to account for key parameters of radiation interaction offers clinicians a valuable resource for refining therapeutic strategies.