Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints. (10th June 2015)

• Record Type: Journal Article
• Authors: Polster, Lisa; Schuemann, Jan; Rinaldi, Ilaria; Burigo, Lucas; McNamara, Aimee L; Stewart, Robert D; Attili, Andrea; Carlson, David J; Sato, Tatsuhiko; Ramos Méndez, José; Faddegon, Bruce; Perl, Joseph; Paganetti, Harald
• Is Part Of: Physics in medicine & biology. Volume 60:Number 13(2015:Jul.)
• Page Start: 5053
• Page End: 5070
• Publication Date: 2015-06-10
• Subjects: Monte Carlo -- simulation -- proton therapy -- relative biological effectiveness; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/60/13/5053
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.