Alpha-particle Monte Carlo simulation for microdosimetric calculations using a commercial spreadsheet

Phys Med Biol. 2007 Apr 7;52(7):1909-22. doi: 10.1088/0031-9155/52/7/010. Epub 2007 Mar 12.

Abstract

Alpha-particle emitters are currently being evaluated in the treatment of cancer. Because of the short range and high linear energy transfer (LET) of most therapeutic alpha-particle emitters, there are significant stochastic variations in the energy deposited within the cellular nucleus. Hence microdosimetric spectra are often necessary to interpret biological endpoints. However, alpha-particle microdosimetric codes are not readily available. In this paper, we describe how a commercial spreadsheet may be used to perform a Monte Carlo simulation of alpha-particle transport. Subsequently, this information is used to determine the distribution of path lengths, energy deposited, and specific energy for a single alpha-particle traversal through the cell nucleus. These data may then be used to determine microdosimetric parameters for multiple alpha-particle emissions. In our analysis, comparison of the first and second moments of the single-event spectra with previously published data show agreement on the order of a few per cent. These small discrepancies are due to differences in interpolation of stopping powers between the various algorithms. Thus, the spreadsheet Monte Carlo method represents a simple and efficient method to calculate single-event spectra for alpha-particle emitters. Copies of the spreadsheet are available from the corresponding author upon request.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Alpha Particles*
  • Animals
  • Cell Nucleus / metabolism*
  • Cytoplasm / metabolism
  • Data Interpretation, Statistical
  • Dose-Response Relationship, Radiation
  • Humans
  • Linear Energy Transfer
  • Models, Statistical
  • Monte Carlo Method
  • Programming Languages
  • Radiometry / instrumentation*
  • Radiometry / methods*
  • Radiotherapy Dosage
  • Software*
  • Stochastic Processes