Combined RBE and OER optimization in proton therapy with FLUKA based on EF5‐PET

INTRODUCTION: Tumor hypoxia is associated with poor treatment outcome. Hypoxic regions are more radioresistant than well‐oxygenated regions, as quantified by the oxygen enhancement ratio (OER). In optimization of proton therapy, including OER in addition to the relative biological effectiveness (RBE) could therefore be used to adapt to patient‐specific radioresistance governed by intrinsic radiosensitivity and hypoxia. METHODS: A combined RBE and OER weighted dose (ROWD) calculation method was implemented in a FLUKA Monte Carlo (MC) based treatment planning tool. The method is based on the linear quadratic model, with α and β parameters as a function of the OER, and therefore a function of the linear energy transfer (LET) and partial oxygen pressure (pO(2)). Proton therapy plans for two head and neck cancer (HNC) patients were optimized with pO(2) estimated from [(18)F]‐EF5 positron emission tomography (PET) images. For the ROWD calculations, an RBE of 1.1 (RBE(1.1,OER)) and two variable RBE models, Rørvik (ROR) and McNamara (MCN), were used, alongside a reference plan without incorporation of OER (RBE(1.1)). RESULTS: For the HNC patients, treatment plans in line with the prescription dose and with acceptable target ROWD could be generated with the established tool. The physical dose was the main factor modulated in the ROWD. The impact of incorporating OER during optimization of HNC patients was demonstrated by the substantial difference found between ROWD and physical dose in the hypoxic tumor region. The largest physical dose differences between the ROWD optimized plans and the reference plan was 12.2 Gy. CONCLUSION: The FLUKA MC based tool was able to optimize proton treatment plans taking the tumor pO(2) distribution from hypoxia PET images into account. Independent of RBE‐model, both elevated LET and physical dose were found in the hypoxic regions, which shows the potential to increase the tumor control compared to a conventional optimization approach.