Background
Various radiobiological models aim to estimate crucial tumor cell-killing effects for radiotherapy and radiation risk assessment, each with unique applications. This paper presents a specific probabilistic model for predicting tumor control probability (TCP) and introduces a user-friendly standalone simulation app tailored for this purpose.
Methods
A pragmatic probabilistic model is suggested for estimating tumor control probability (TCP) by incorporating a fractionated treatment approach. Within this model, ionizing radiation induces the formation of killed cells (KC), sublethally damaged cells (SLDC), and undamaged cells (UDC), the impact of which is contingent upon the radiosensitivity of cells. This triad of cell types can be influenced by radiation during subsequent fractions, providing a nuanced understanding of the treatment dynamics.
Results
A MATLAB app has been developed for a tumor control probability simulator. This simulator employs probabilistic modeling to describe radiation biological effects in a tumor subjected to homogeneous irradiation with a specified dose per fraction in a fractionated treatment. Key input parameters for the simulation include a cell radiosensitivity of 1.2, radiosensitivity of cell sub-lethal damage at 3, tumor cell volume of 1 cubic cm, tumor cell density of \(\:0.1\times\:{10}^{7}\), 30 virtual simulations, and 40 fractional radiation doses. Post-simulation, the resulting tumor control probability is determined to be 86.7%.
Conclusion
The study's simulator is a crucial tool for modeling radiation-induced biological effects in fractionated irradiation of tumors. Its use of probabilistic foundations generates hypotheses and assesses the efficacy of fractionated radiation therapy, holding promise for enhancing the safety and effectiveness of cancer treatment.
