Program Information
A Phenomenological Relative Biological Effectiveness (RBE) Model for Proton Therapy Based On All Published in Vitro Cell Survival Data
A McNamara*, J Schuemann , H Paganetti , Massachusetts General Hospital, Boston, MA
Presentations
SU-F-BRD-13 (Sunday, July 12, 2015) 4:00 PM - 6:00 PM Room: Ballroom D
Purpose: Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies. A recent review study (Paganetti 2014), collected over 72 experimental reports on proton RBE, providing a comprehensive dataset for predicting proton RBE. Using this dataset we develop a model to predict RBE based on dose, linear energy transfer (LET) and the tissue specific parameter (α/β)x.
Methods: The relationship of the RBE on dose, dose average LET (LETd) and (α/β)x was explored using 290 experimental data points. The RBE was calculated for each experimental point for doses ranging from 1 to 10 Gy. An RBE model, based on the linear-quadratic model, was derived using a nonlinear regression fit to the experimental data.
Results: The proposed RBE model predicts that the RBE increases with LET and decreases with (α/β)x. The model additionally predicts a decrease in RBE with increasing dose for low (α/β)x values (< 3 Gy). However the model predicts an increase in RBE with increasing dose for large (α/β)x (> 4 Gy). Previous phenomenological models were based on a small subset of experimental data and deviate significantly from our findings.
Conclusion: The proposed RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. The model agrees with previous theoretical predictions on the relationship between RBE, LETd and (α/β)x and also makes predictions on the relationship between RBE and dose. The proposed model shows a relationship between α and LETd but no significant relationship between β and LETd. Previously published phenomenological models based on a limited data set might have to be revised.
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