A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies

Fada Guan, Changran Geng, David J. Carlson, Duo H. Ma, Lawrence Bronk, Drake Gates, Xiaochun Wang, Stephen F. Kry, David Grosshans, Radhe Mohan

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number015008
JournalPhysics in medicine and biology
Volume64
Issue number1
DOIs
StatePublished - Jan 1 2019

Fingerprint

Relative Biological Effectiveness
Radiobiology
Biological Models
Ions
Boidae
Programming Languages
Biological Phenomena
Helium
Therapeutics
Protons
Cell Survival
Carbon
Water

Keywords

  • Monte Carlo
  • RBE
  • RMF
  • biological dose optimization
  • charged particle
  • python

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Cite this

A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies. / Guan, Fada; Geng, Changran; Carlson, David J.; Ma, Duo H.; Bronk, Lawrence; Gates, Drake; Wang, Xiaochun; Kry, Stephen F.; Grosshans, David; Mohan, Radhe.

In: Physics in medicine and biology, Vol. 64, No. 1, 015008, 01.01.2019.

Research output: Contribution to journalArticle

Guan, Fada ; Geng, Changran ; Carlson, David J. ; Ma, Duo H. ; Bronk, Lawrence ; Gates, Drake ; Wang, Xiaochun ; Kry, Stephen F. ; Grosshans, David ; Mohan, Radhe. / A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies. In: Physics in medicine and biology. 2019 ; Vol. 64, No. 1.
@article{573c0e9f03a14b4f88be8ff7eea3a77f,
title = "A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies",
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.",
keywords = "Monte Carlo, RBE, RMF, biological dose optimization, charged particle, python",
author = "Fada Guan and Changran Geng and Carlson, {David J.} and Ma, {Duo H.} and Lawrence Bronk and Drake Gates and Xiaochun Wang and Kry, {Stephen F.} and David Grosshans and Radhe Mohan",
year = "2019",
month = "1",
day = "1",
doi = "10.1088/1361-6560/aaf5df",
language = "English (US)",
volume = "64",
journal = "Physics in Medicine and Biology",
issn = "0031-9155",
publisher = "IOP Publishing Ltd.",
number = "1",

}

TY - JOUR

T1 - A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies

AU - Guan, Fada

AU - Geng, Changran

AU - Carlson, David J.

AU - Ma, Duo H.

AU - Bronk, Lawrence

AU - Gates, Drake

AU - Wang, Xiaochun

AU - Kry, Stephen F.

AU - Grosshans, David

AU - Mohan, Radhe

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Monte Carlo

KW - RBE

KW - RMF

KW - biological dose optimization

KW - charged particle

KW - python

UR - http://www.scopus.com/inward/record.url?scp=85058917146&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058917146&partnerID=8YFLogxK

U2 - 10.1088/1361-6560/aaf5df

DO - 10.1088/1361-6560/aaf5df

M3 - Article

VL - 64

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 1

M1 - 015008

ER -