gms | German Medical Science

48th Meeting of the Particle Therapy Co-Operative Group

Particle Therapy Co-Operative Group (PTCOG)

28.09. - 03.10.2009, Heidelberg

Impact of tissue type and effect level on the uncertainties of RBE: Experimental and modelling aspects

Meeting Abstract

  • T. Friedrich - Biophysics, GSI, Darmstadt
  • T. Elsässer - Biophysics, GSI, Darmstadt
  • M. Durante - Biophysics, GSI, Darmstadt
  • M. Scholz - Biophysics, GSI, Darmstadt

PTCOG 48. Meeting of the Particle Therapy Co-Operative Group. Heidelberg, 28.09.-03.10.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. Doc09ptcog065

DOI: 10.3205/09ptcog065, URN: urn:nbn:de:0183-09ptcog0654

Published: September 24, 2009

© 2009 Friedrich et al.
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Outline

Text

The relative biological effectiveness (RBE) is generally used in ion therapy to characterize the enhanced effect of ions compared to that of photons. Experimental results as well as model predictions suggest, that RBE depends on the resistance of a cell or tissue type against photon radiation. Within the linear quadratic model this is reflected in a systematic dependence on the ratio of the parameters α and β.

Here we show, that based on clonogenic cell survival experiments reported in the literature for different cell lines the correlation between RBE and the β/α-ratio can be empirically described by a linear relationship within the uncertainty limits of the experimental data. This linear relationship is in good agreement with the predictions of the Local Effect Model (LEM) which is used to take into account the increased RBE of ions for treatment planning with the GSI pilot project and several future clinical carbon ion facilities.

The correlation between RBE and β/α-ratio is most pronounced for low doses and thus small effect levels, whereas the dependence is less pronounced with increasing doses. We discuss the implications of these findings for treatment planning in particular with respect to the uncertainties of the parameters α and β and β/α-ratio. These represent important input parameters for the LEM and thus also determine the uncertainty of the predicted value.