SU‐GG‐T‐507: Clinically Derived Dose‐Response Relations for Prostate from Combined Photon and Proton Prostate Cancer Radiotherapy

Purpose Recent advances in radiation therapy have provided the possibility of combining different modalities in different parts of the treatment. The purpose of this study was the determination of the parameters D₅₀ and γ for the prostate adenocarcinoma using different radiobiological models, when proton and photon beams are combined. Material and Methods In this study 100 patients, who were treated for prostate cancer at the Academic Hospital in Uppsala, Sweden from 2002 to 2006, were used. The patients were treated with a conventional photon beam configuration to a dose of 50 Gy in 2 Gy fractions. Additionally, a proton boost of 20 Gy in 5 Gy fractions was applied. The photon and proton doses were corrected for the fractionation effects using the BED (biologically effective dose) concept. In the calculation of the proton dose, we considered RBE=1.1. For each patient, the clinical treatment outcome (follow‐up of 24 months) and the 3D dose distribution delivered to the target were available. The best estimates of the parameters D₅₀ and γ of the Binomial, Poisson and Probit models were determined by applying a maximum likelihood fitting. Results Of the 100 patients, 94 had tumor control (94.0%) whereas 6 patients had treatment failure (6.0%). The mean doses in the prostate in these two groups of patients were 72.97Gy and 62.60Gy, respectively. The estimated values of the parameters for the Poisson and Binomial models are D₅₀=49.6Gy (68%CI=47.1–51.9Gy) and γ=2.25 (68%CI=1.83–2.75), whereas for the Probit model they are D₅₀=47.3Gy (68%CI=45.1–49.4Gy) and γ=1.31 (68%CI=1.11–1.52). Conclusions The estimated parameters are able to describe the dose‐response relation of prostate adenocarcinoma. Since there are very limited data on radiation therapy using combinations of photon and proton beams, the determined parameters can be useful in estimating the expected clinical outcome during treatment planning and perform optimization of the delivered treatment.