The progress of radiobiological models in modern radiotherapy with emphasis on the uncertainty issue

Abstract

Radiobiological models are used in modern radiotherapy to evaluate the biological effects of different treatment plans or modalities. A radiobiological model typically converts a physical quantity (e.g. absorbed dose) to a biological quantity (e.g. cell survival fraction). Currently, the linear-quadratic model (LQM) is the most widely used model. Since it is a deterministic model, the LQM naturally ignores the uncertainties arising from the stochastic randomness of energy depositions along radiation tracks and the inherent unpredictable nature of biological systems. Recently, many studies have revealed the detailed spatial and temporal distributions of DNA damages, e.g. the DNA double strand breaks (DSBs), along various types of radiation tracks traversing a cell nucleus. Studies have also been conducted to unravel the biological pathways that involve in how cells and tissues process DNA damages. As such, the author proposes to start developing a new multi-scale radiobiological model based on the "bottom-up" approach. The model includes a Monte Carlo procedure to treat the stochastic randomness of radiation-induced DNA damages, and it also includes the relevant intercellular and intracellular pathways to allow the DNA damages to evolve into higher-order biological endpoints, e.g. chromosome aberrations, cell death, or tumorigenesis. Because of its stochastic nature, the new model inherently addresses the uncertainty issue being ignored by LQM. More importantly perhaps is that the model opens a new way to study radiation effects that involve biological pathways, and therefore, may have a profound impact on radiotherapy in the future.