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

PURPOSE The physical and potential biological advantages of proton and carbon ions have not been fully exploited in radiation therapy for the treatment of cancer. In this work, an approach to predict proton and carbon ion relative biological effectiveness (RBE) in a representative spread-out Bragg peak (SOBP) is derived using the repair-misrepair-fixation (RMF) model. METHODS AND MATERIALS Formulas linking dose-averaged linear-quadratic parameters to DSB induction and processing are derived from the RMF model. The Monte Carlo Damage Simulation (MCDS) software is used to quantify the effects of radiation quality on the induction of DNA double-strand breaks (DSB). Trends in parameters α and β for clinically relevant proton and carbon ion kinetic energies are determined. RESULTS Proton and carbon ion RBE are shown to increase as particle energy, dose, and tissue α/β ratios decrease. Entrance RBE is ∼1.0 and ∼1.3 for protons and carbon ions, respectively. For doses in the range of 0.5 to 10 Gy, proton RBE ranges from 1.02 (proximal edge) to 1.4 (distal edge). Over the same dose range, the RBE for carbon ions ranges from 1.5 on the proximal edge to 6.7 on the distal edge. CONCLUSIONS The proposed approach is advantageous because the RBE for clinically relevant particle distributions is guided by well-established physical and biological (track structure) considerations. The use of an independently tested Monte Carlo model to predict the effects of radiation quality on DSB induction also minimizes the number of ad hoc biological parameters that must be determined to predict RBE. Large variations in predicted RBE across an SOBP may produce undesirable biological hot and cold spots. These results highlight the potential for the optimization of physical dose for a uniform biological effect.

[1]  Zuofeng Li,et al.  Prescribing, Recording, and Reporting Proton-Beam Therapy , 2009 .

[2]  U. Oelfke,et al.  Three-dimensional LET calculations for treatment planning of proton therapy. , 2004, Zeitschrift fur medizinische Physik.

[3]  T Bortfeld,et al.  An analytical approximation of the Bragg curve for therapeutic proton beams. , 1997, Medical physics.

[4]  Hak Choy,et al.  International Journal of Radiation Oncology Biology Physics: Introduction , 2004 .

[5]  Takuji Furukawa,et al.  Treatment planning for a scanned carbon beam with a modified microdosimetric kinetic model , 2010, Physics in medicine and biology.

[6]  Uwe Oelfke,et al.  Application of constant vs. variable relative biological effectiveness in treatment planning of intensity-modulated proton therapy. , 2011, International journal of radiation oncology, biology, physics.

[7]  M Zaider,et al.  The synergistic effects of different radiations. , 1980, Radiation research.

[8]  Harald Paganetti,et al.  Variations in linear energy transfer within clinical proton therapy fields and the potential for biological treatment planning. , 2011, International journal of radiation oncology, biology, physics.

[9]  R. Stewart,et al.  Monte Carlo simulation of DNA damage induction by x-rays and selected radioisotopes , 2008, Physics in medicine and biology.

[10]  M Scholz,et al.  Treatment planning for heavy-ion radiotherapy: calculation and optimization of biologically effective dose. , 2000, Physics in medicine and biology.

[11]  U. Oelfke,et al.  Direct comparison of biologically optimized spread-out bragg peaks for protons and carbon ions. , 2008, International journal of radiation oncology, biology, physics.

[12]  T. Kanai,et al.  Inactivation of Aerobic and Hypoxic Cells from Three Different Cell Lines by Accelerated 3He-, 12C- and 20Ne-Ion Beams , 2000, Radiation research.

[13]  B. Fertil,et al.  Predictive value of in vitro radiosensitivity parameters in head and neck cancers and cervical carcinomas: preliminary correlations with local control and overall survival. , 1993, International journal of radiation oncology, biology, physics.

[14]  Uwe Oelfke,et al.  Fast multifield optimization of the biological effect in ion therapy , 2006, Physics in medicine and biology.

[15]  J. Hopewell,et al.  Fractionation effects in particle radiotherapy: implications for hypo-fractionation regimes , 2010, Physics in medicine and biology.

[16]  V. Semenenko,et al.  Fast Monte Carlo simulation of DNA damage formed by electrons and light ions , 2006, Physics in medicine and biology.

[17]  Harald Paganetti,et al.  Relative biological effectiveness (RBE) values for proton beam therapy. , 2002, International journal of radiation oncology, biology, physics.

[18]  G Kraft,et al.  Design and construction of a ripple filter for a smoothed depth dose distribution in conformal particle therapy. , 1999, Physics in medicine and biology.

[19]  M Scholz,et al.  Track structure and the calculation of biological effects of heavy charged particles. , 1996, Advances in space research : the official journal of the Committee on Space Research.

[20]  Jian Z. Wang,et al.  Comparison of in vitro and in vivo alpha/beta ratios for prostate cancer. , 2004, Physics in medicine and biology.

[21]  R. Wilson.  Radiological use of fast protons. , 1946, Radiology.

[22]  H D Thames,et al.  Fractionation sensitivities and dose-control relations of head and neck carcinomas: analysis of the randomized hyperfractionation trials. , 1999, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[23]  David J. Carlson,et al.  Combined Use of Monte Carlo DNA Damage Simulations and Deterministic Repair Models to Examine Putative Mechanisms of Cell Killing , 2008, Radiation research.

[24]  H. Bethe.  Zur Theorie des Durchgangs schneller Korpuskularstrahlen durch Materie , 1930 .

[25]  V A Semenenko,et al.  A Fast Monte Carlo Algorithm to Simulate the Spectrum of DNA Damages Formed by Ionizing Radiation , 2004, Radiation research.

[26]  David J. Carlson,et al.  Comparison of in vitro and in vivo a/ ratios for prostate cancer , 2004 .