Literature DB >> 33692907

On calculation of the average linear energy transfer for radiobiological modelling.

Oleg N Vassiliev1.   

Abstract

Applying the concept of linear energy transfer (LET) to modeling of biological effects of charged particles usually involves calculation of the average LET. To calculate this, the energy distribution of particles is characterized by either the source spectrum or fluence spectrum. Also, the average can be frequency-or dose-weighted. This makes four methods of calculating the average LET, each producing a different number. The purpose of this note is to describe which of these four methods is best suited for radiobiological modelling. We focused on data for photons (x-rays and gamma radiation) because in this case differences in the four averaging methods are most pronounced. However, our conclusions are equally applicable to photons and hadrons. We based our arguments on recently emerged Monte Carlo data that fully account for transport of electrons down to very low energies comparable to the ionization potential of water. We concluded that the frequency average LET calculated using the fluence spectrum has better predictive power than does that calculated using any of the other three options. This optimal method is not new but is different from those currently dominating research in this area.

Entities:  

Keywords:  RBE modelling; average LET; hadron RBE; x-rays RBE

Mesh:

Year:  2020        PMID: 33692907      PMCID: PMC7939035          DOI: 10.1088/2057-1976/abc967

Source DB:  PubMed          Journal:  Biomed Phys Eng Express        ISSN: 2057-1976


  17 in total

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Authors:  M Scholz; G Kraft
Journal:  Adv Space Res       Date:  1996       Impact factor: 2.152

2.  Electron spectra and the RBE of X rays.

Authors:  Albrecht M Kellerer
Journal:  Radiat Res       Date:  2002-07       Impact factor: 2.841

3.  Formulation of the multi-hit model with a non-Poisson distribution of hits.

Authors:  Oleg N Vassiliev
Journal:  Int J Radiat Oncol Biol Phys       Date:  2012-01-13       Impact factor: 7.038

4.  A Monte Carlo study of the variation of electron fluence in water from a 6 MV photon beam outside of the field.

Authors:  C Kirkby; C Field; M MacKenzie; A Syme; B G Fallone
Journal:  Phys Med Biol       Date:  2007-05-22       Impact factor: 3.609

5.  Systematic microdosimetric data for protons of therapeutic energies calculated with Geant4-DNA.

Authors:  Oleg N Vassiliev; Christine B Peterson; Wenhua Cao; David R Grosshans; Radhe Mohan
Journal:  Phys Med Biol       Date:  2019-11-04       Impact factor: 3.609

Review 6.  Relative biological effectiveness (RBE) values for proton beam therapy. Variations as a function of biological endpoint, dose, and linear energy transfer.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2014-10-31       Impact factor: 3.609

7.  A new formalism for modelling parameters α and β of the linear-quadratic model of cell survival for hadron therapy.

Authors:  Oleg N Vassiliev; David R Grosshans; Radhe Mohan
Journal:  Phys Med Biol       Date:  2017-10-03       Impact factor: 3.609

8.  Clinical evidence of variable proton biological effectiveness in pediatric patients treated for ependymoma.

Authors:  Christopher R Peeler; Dragan Mirkovic; Uwe Titt; Pierre Blanchard; Jillian R Gunther; Anita Mahajan; Radhe Mohan; David R Grosshans
Journal:  Radiother Oncol       Date:  2016-11-16       Impact factor: 6.280

9.  A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data.

Authors:  Aimee L McNamara; Jan Schuemann; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-10-13       Impact factor: 3.609

10.  A simple model for calculating relative biological effectiveness of X-rays and gamma radiation in cell survival.

Authors:  Oleg N Vassiliev; Christine B Peterson; David R Grosshans; Radhe Mohan
Journal:  Br J Radiol       Date:  2020-06-04       Impact factor: 3.039

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