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

Malignant Induction Probability (MIP) Maps for X-ray and Particle Beams

Meeting Abstract

  • C. Timlin - University of Oxford, Particle Therapy Cancer Research Institute, Oxford, United Kingdom
  • M. Houston - University of Oxford, e-Research Centre, Oxford, United Kingdom
  • B. Jones - University of Oxford, Particle Therapy Cancer Research Institute, Oxford, United Kingdom
  • M. Hill - University of Oxford, Gray Institute of Radiation Oncology and Biology, Oxford, United Kingdom
  • K. Peach - University of Oxford, Particle Therapy Cancer Research Institute, Oxford, United Kingdom
  • A. Trevethan - University of Oxford, e-Research Centre, Oxford, United Kingdom

PTCOG 48. Meeting of the Particle Therapy Co-Operative Group. Heidelberg, 28.09.-03.10.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. Doc09ptcog204

DOI: 10.3205/09ptcog204, URN: urn:nbn:de:0183-09ptcog2048

Veröffentlicht: 24. September 2009

© 2009 Timlin et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

One of the potential advantages of charged particle therapy is the reduction in the risk of radiation-induced malignancies compared with x-ray tele-therapy. Several studies, using the standard radioprotection model, have shown that the risk is reduced by a factor of 2-15 depending on the anatomical treatment location. However, the standard model has no allowances for major radiobiological processes such as fractionation, cellular radio-sensitivities and the variations of relative biological effect (RBE) with dose per fraction.

A new model based on Poisson statistics and the linear quadratic model can be used to predict malignant induction, taking into account the balance between malignant cell induction and cell killing caused by two classes of chromosomal damage [1]. The main features of the model are that the peaks in malignant induction probability (MIP) can be shifted to lower dose regions with increase in RBE or by an increase in radio-sensitivity (e.g. leukaemia induction), but are moved to higher doses for lower radio-sensitivities (for tumours such as sarcomas) and with greater fractionation. The MIP can be expressed as a relative risk per cell and varies with dose.

For particle therapy it is necessary to consider the total number of cells at risk and allow for different numbers of beams. This has been investigated in a preliminary model using MATLAB software. The physical doses can be displayed as well as MIP maps (using colour washes) and overall MIP. Comparative studies of different numbers of x-ray, proton and carbon ion beams intersecting over a virtual tumour show interesting results. For example, the overall MIP obtained for a 2-field proton plan is 2 times greater than for a 4-field x-ray plan. This ratio is increased to 5 if a 4-field proton plan is used. However, the use of proton therapy can reduce the average normal tissue dose by a factor of greater than 4. These results apply to two sets of intersecting opposed-fields (as in a 4-field box technique). A larger number of non-opposed fields, as used in x-ray IMRT, would inevitably lead to an increase in MIP because of the larger volumes irradiated at low dosage. Further improvements in MIP can be obtained using hypo-fractionated proton therapy prescribed to within acceptable tissue tolerances.

In conclusion, particle therapy should employ as few beams as possible to reduce the volume of cells irradiated, with minimal normal tissue traversal. Use of rotating gantries would help in this respect. More work is necessary to develop the model further, with use of more complex field arrangements to assess the benefits of using MIP maps as part of the treatment planning process.


References

1.
Jones B. Modelling Carcinogenesis after Radiotherapy using Poisson Statistics: Implications for IMRT, Protons and Ions. J Radiol Prot. 2009;29:A143-A157.