Literature DB >> 26061666

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints.

Lisa Polster1, Jan Schuemann, Ilaria Rinaldi, Lucas Burigo, Aimee L McNamara, Robert D Stewart, Andrea Attili, David J Carlson, Tatsuhiko Sato, José Ramos Méndez, Bruce Faddegon, Joseph Perl, Harald Paganetti.   

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.

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Year:  2015        PMID: 26061666      PMCID: PMC4511084          DOI: 10.1088/0031-9155/60/13/5053

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  43 in total

1.  A mechanism-based approach to predict the relative biological effectiveness of protons and carbon ions in radiation therapy.

Authors:  Malte C Frese; Victor K Yu; Robert D Stewart; David J Carlson
Journal:  Int J Radiat Oncol Biol Phys       Date:  2011-11-16       Impact factor: 7.038

2.  Range uncertainty in proton therapy due to variable biological effectiveness.

Authors:  Alejandro Carabe; Maryam Moteabbed; Nicolas Depauw; Jan Schuemann; Harald Paganetti
Journal:  Phys Med Biol       Date:  2012-02-14       Impact factor: 3.609

3.  TOPAS: an innovative proton Monte Carlo platform for research and clinical applications.

Authors:  J Perl; J Shin; J Schumann; B Faddegon; H Paganetti
Journal:  Med Phys       Date:  2012-11       Impact factor: 4.071

4.  A model for the relative biological effectiveness of protons: the tissue specific parameter α/β of photons is a predictor for the sensitivity to LET changes.

Authors:  Minna Wedenberg; Bengt K Lind; Björn Hårdemark
Journal:  Acta Oncol       Date:  2012-08-22       Impact factor: 4.089

5.  Biological considerations when comparing proton therapy with photon therapy.

Authors:  Harald Paganetti; Peter van Luijk
Journal:  Semin Radiat Oncol       Date:  2013-04       Impact factor: 5.934

6.  Relative biological effectiveness (RBE) values for proton beam therapy.

Authors:  Harald Paganetti; Andrzej Niemierko; Marek Ancukiewicz; Leo E Gerweck; Michael Goitein; Jay S Loeffler; Herman D Suit
Journal:  Int J Radiat Oncol Biol Phys       Date:  2002-06-01       Impact factor: 7.038

7.  Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy.

Authors:  José Ramos-Méndez; Joseph Perl; Bruce Faddegon; Jan Schümann; Harald Paganetti
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

8.  A modular method to handle multiple time-dependent quantities in Monte Carlo simulations.

Authors:  J Shin; J Perl; J Schümann; H Paganetti; B A Faddegon
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

Review 9.  Monte Carlo role in radiobiological modelling of radiotherapy outcomes.

Authors:  Issam El Naqa; Piotr Pater; Jan Seuntjens
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

10.  Systematic analysis of RBE and related quantities using a database of cell survival experiments with ion beam irradiation.

Authors:  Thomas Friedrich; Uwe Scholz; Thilo Elsässer; Marco Durante; Michael Scholz
Journal:  J Radiat Res       Date:  2012-12-23       Impact factor: 2.724

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  19 in total

1.  Analysis of the track- and dose-averaged LET and LET spectra in proton therapy using the geant4 Monte Carlo code.

Authors:  Fada Guan; Christopher Peeler; Lawrence Bronk; Changran Geng; Reza Taleei; Sharmalee Randeniya; Shuaiping Ge; Dragan Mirkovic; David Grosshans; Radhe Mohan; Uwe Titt
Journal:  Med Phys       Date:  2015-11       Impact factor: 4.071

Review 2.  Robust Proton Treatment Planning: Physical and Biological Optimization.

Authors:  Jan Unkelbach; Harald Paganetti
Journal:  Semin Radiat Oncol       Date:  2018-04       Impact factor: 5.934

Review 3.  Radiobiological issues in proton therapy.

Authors:  Radhe Mohan; Christopher R Peeler; Fada Guan; Lawrence Bronk; Wenhua Cao; David R Grosshans
Journal:  Acta Oncol       Date:  2017-08-22       Impact factor: 4.089

4.  Erratum: "Analysis of the track- and dose-averaged LET and LET spectra in proton therapy using the geant4 Monte Carlo code" [Med. Phys. 42 (11), page range 6234-6247(2015)].

Authors:  Fada Guan; Christopher Peeler; Lawrence Bronk; Changran Geng; Reza Taleei; Sharmalee Randeniya; Shuaiping Ge; Dragan Mirkovic; David Grosshans; Radhe Mohan; Uwe Titt
Journal:  Med Phys       Date:  2018-02-19       Impact factor: 4.071

Review 5.  Modelling variable proton relative biological effectiveness for treatment planning.

Authors:  Aimee McNamara; Henning Willers; Harald Paganetti
Journal:  Br J Radiol       Date:  2019-11-18       Impact factor: 3.039

6.  Energy optimization in gold nanoparticle enhanced radiation therapy.

Authors:  Wonmo Sung; Jan Schuemann
Journal:  Phys Med Biol       Date:  2018-06-25       Impact factor: 3.609

7.  Recent developments and comprehensive evaluations of a GPU-based Monte Carlo package for proton therapy.

Authors:  Nan Qin; Pablo Botas; Drosoula Giantsoudi; Jan Schuemann; Zhen Tian; Steve B Jiang; Harald Paganetti; Xun Jia
Journal:  Phys Med Biol       Date:  2016-10-03       Impact factor: 3.609

8.  The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research.

Authors:  Bruce Faddegon; José Ramos-Méndez; Jan Schuemann; Aimee McNamara; Jungwook Shin; Joseph Perl; Harald Paganetti
Journal:  Phys Med       Date:  2020-04-03       Impact factor: 2.685

9.  TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology.

Authors:  J Schuemann; A L McNamara; J Ramos-Méndez; J Perl; K D Held; H Paganetti; S Incerti; B Faddegon
Journal:  Radiat Res       Date:  2019-01-04       Impact factor: 2.841

Review 10.  National Cancer Institute Workshop on Proton Therapy for Children: Considerations Regarding Brainstem Injury.

Authors:  Daphne Haas-Kogan; Daniel Indelicato; Harald Paganetti; Natia Esiashvili; Anita Mahajan; Torunn Yock; Stella Flampouri; Shannon MacDonald; Maryam Fouladi; Kry Stephen; John Kalapurakal; Stephanie Terezakis; Hanne Kooy; David Grosshans; Mike Makrigiorgos; Kavita Mishra; Tina Young Poussaint; Kenneth Cohen; Thomas Fitzgerald; Vinai Gondi; Arthur Liu; Jeff Michalski; Dragan Mirkovic; Radhe Mohan; Stephanie Perkins; Kenneth Wong; Bhadrasain Vikram; Jeff Buchsbaum; Larry Kun
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-05-01       Impact factor: 7.038

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