In vitro-based Modelling of Relative Biological Effectiveness in Proton Therapy

• Main Author: Lyngholm, Erlend
• Format: Dissertation
• Language: English

Many cancer patients can benefit from proton therapy, as the treatment modality have been shown to provide a more conformal irradiation of the target while sparing surrounding healthy tissue, compared to photon therapy. The concept of the relative biological effectiveness allows for appliance of the large amount of experience from photon therapy when considering irradiation with protons. In proton therapy treatment today, a constant proton RBE of 1.1 relative to high-energy photons is applied, assuming protons to be 10% more effective for the same physical dose. However, the proton RBE have been shown to vary with multiple physical and biological factors including the deposited physical dose, irradiated tissue and radiation quality of the beam. Precise modelling of the proton RBE have therefore become an important field of study. This work presents a comprehensive analysis of proton RBE dependencies, using a large up-to date database of in vitro data points from proton irradiation experiments. The analysis focus on the RBE dependence on the reference radiation fractionation sensitivity ((α⁄β)_x), and the radiation quality of the beam, quantified by the linear energy transfer (LET). Additionally, potential differences between RBE of monoenergetic and broad energy proton beams (for a given average LET value) was explored, and the effects of variations in the data selection procedure were investigated. The widely used assumption of an inverse proportionality of RBE with (α⁄β)_x was investigated through linear fitting and the fits were compared with previous published models applying this assumption. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 4th degree and polynomials of 1st and 2nd degree in combination with exponential functions. Additional fitting was performed on different restricted databases in terms of reduced range of included LET and (α⁄β)_x values. Furthermore, the database was seen to be highly imbalanced, i.e. data points were not evenly distributed over the (α⁄β)_x and LET range, and fitting was therefore also performed using an iterative sampling procedure to compensate for this. Selected regression fits were implemented as RBE models and the RBE estimates from these, both as a function of LET, dose and (α⁄β)_x and for a simulated spread out Bragg peak (SOBP) scenario were compared to the estimates of three published RBE models. Linear fitting on both the unrestricted and restricted databases showed a trend o