A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies. (21st December 2018)

• Record Type: Journal Article
• Authors: Guan, Fada; Geng, Changran; Carlson, David J; Ma, Duo H; Bronk, Lawrence; Gates, Drake; Wang, Xiaochun; Kry, Stephen F; Grosshans, David; Mohan, Radhe
• Is Part Of: Physics in medicine & biology. Volume 64:Number 1(2019:Jan.)
• Publication Date: 2018-12-21
• Subjects: charged particle -- RBE -- Monte Carlo -- python -- biological dose optimization -- RMF; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aaf5df
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: In charged particle therapy, the objective is to exploit both the physical and radiobiological advantages of charged particles to improve the therapeutic index. Use of the beam scanning technique provides the flexibility to implement biological dose optimized intensity-modulated ion therapy (IMIT). An easy-to-implement algorithm was developed in the current study to rapidly generate a uniform biological dose distribution, namely the product of physical dose and the relative biological effectiveness (RBE), within the target volume using scanned ion beams for charged particle radiobiological studies. Protons, helium ions and carbon ions were selected to demonstrate the feasibility and flexibility of our method. The general-purpose Monte Carlo simulation toolkit Geant4 was used for particle tracking and generation of physical and radiobiological data needed for later dose optimizations. The dose optimization algorithm was developed using the Python (version 3) programming language. A constant RBE-weighted dose (RWD) spread-out Bragg peak (SOBP) in a water phantom was selected as the desired target dose distribution to demonstrate the applicability of the optimization algorithm. The mechanistic repair-misrepair-fixation (RMF) model was incorporated into the Monte Carlo particle tracking to generate radiobiological parameters and was used to predict the RBE of cell survival in the iterative process of the biological dose optimization for the three selected ions. The post-optimization generated beam delivery strategy can be used in radiation biology experiments to obtain radiobiological data to further validate and improve the accuracy of the RBE model. This biological dose optimization algorithm developed for radiobiology studies could potentially be extended to implement biologically optimized IMIT plans for patients.