Relative Biological Effectiveness Predictions Research Articles

Benchmarking proton RBE models

Objective. To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset to evaluate predictions when all models are fit using the same data and fitting techniques, and to assess the statistical robustness of the models. Approach. Model performance was initially evaluated by fitting to the full dataset, and then a cross-validation approach was applied to assess model generalisability and robustness. The impact of weighting the fit and the choice of biological endpoint (either single or multiple survival levels) was also evaluated. Main results. Fitting the models to a common dataset reduced differences between their predictions, however significant disagreements remained due to different underlying assumptions. All models performed poorly under cross-validation in the weighted fits, suggesting that some uncertainties on the experimental data were significantly underestimated, resulting in over-fitting and poor performance on unseen data. The simplest model, which depends linearly on the LET but has no tissue or dose dependence, performed best for a single survival level. However, when fitting to multiple survival levels simultaneously, more complex models with tissue dependence performed better. All models had significant residual uncertainty in their predictions compared to experimental data. Significance. This analysis highlights that poor quality of error estimation on the dose response parameters introduces substantial uncertainty in model fitting. The significant residual error present in all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and the importance of robust statistical validation of RBE models.

Commissioning of Helium Ion Therapy and the First Patient Treatment With Active Beam Delivery

Helium ions offer intermediate physical and biological properties to the clinically used protons and carbon ions. This work presents the commissioning of the first clinical treatment planning system (TPS) for helium ion therapy with active beam delivery to prepare the first patients' treatment at the Heidelberg Ion-Beam Therapy Center (HIT). Through collaboration between RaySearch Laboratories and HIT, absorbed and relative biological effectiveness (RBE)-weighted calculation methods were integrated for helium ion beam therapy with raster-scanned delivery in the TPS RayStation. At HIT, a modified microdosimetric kinetic biological model was chosen as reference biological model. TPS absorbed dose predictions were compared against measurements with several devices, using phantoms of different complexities, from homogeneous to heterogeneous anthropomorphic phantoms. RBE and RBE-weighted dose predictions of the TPS were verified against calculations with an independent RBE-weighted dose engine. The patient-specific quality assurance of the first treatment at HIT using helium ion beam with raster-scanned delivery is presented considering standard patient-specific measurements in a water phantom and 2 independent dose calculations with a Monte Carlo or an analytical-based engine. TPS predictions were consistent with dosimetric measurements and independent dose engines computations for absorbed and RBE-weighted doses. The mean difference between dose measurements to the TPS calculation was 0.2% for spread-out Bragg peaks in water. Verification of the first patient treatment TPS predictions against independent engines for both absorbed and RBE-weighted doses presents differences within 2% in the target and with a maximum deviation of 3.5% in the investigated critical regions of interest. Helium ion beam therapy has been successfully commissioned and introduced into clinical use. Through comprehensive validation of the absorbed and RBE-weighted dose predictions of the RayStation TPS, the first clinical TPS for helium ion therapy using raster-scanned delivery was employed to plan the first helium patient treatment at HIT.

An empirical model of proton RBE based on the linear correlation between x-ray and proton radiosensitivity.

Proton relative biological effectiveness (RBE) is known to depend on physical factors of the proton beam, such as its linear energy transfer (LET), as well as on cell-line specific biological factors, such as their ability to repair DNA damage. However, in a clinical setting, proton RBE is still considered to have a fixed value of 1.1 despite the existence of several empirical models that can predict proton RBE based on how a cell's survival curve (linear-quadratic model [LQM]) parameters α and β vary with the LET of the proton beam. Part of the hesitation to incorporate variable RBE models in the clinic is due to the great noise in the biological datasets on which these models are trained, often making it unclear which model, if any, provides sufficiently accurate RBE predictions to warrant a departure from RBE=1.1. Here, we introduce a novel model of proton RBE based on how a cell's intrinsic radiosensitivity varies with LET, rather than its LQM parameters. We performed clonogenic cell survival assays for eight cell lines exposed to 6 MV x-rays and 1.2, 2.6, or 9.9keV/µm protons, and combined our measurements with published survival data (n=397 total cell line/LET combinations). We characterized how radiosensitivity metrics of the form DSF% , (the dose required to achieve survival fraction [SF], e.g., D10% ) varied with proton LET, and calculated the Bayesian information criteria associated with different LET-dependent functions to determine which functions best described the underlying trends. This allowed us to construct a six-parameter model that predicts cells' proton survival curves based on the LET dependence of their radiosensitivity, rather than the LET dependence of the LQM parameters themselves. We compared the accuracy of our model to previously established empirical proton RBE models, and implemented our model within a clinical treatment plan evaluation workflow to demonstrate its feasibility in a clinical setting. Our analyses of the trends in the data show that DSF% is linearly correlated between x-rays and protons, regardless of the choice of the survival level (e.g., D10% , D37% , or D50% are similarly correlated), and that the slope and intercept of these correlations vary with proton LET. The model we constructed based on these trends predicts proton RBE within 15%-30% at the 68.3% confidence level and offers a more accurate general description of the experimental data than previously published empirical models. In the context of a clinical treatment plan, our model generally predicted higher RBE-weighted doses than the other empirical models, with RBE-weighted doses in the distal portion of the field being up to 50.7% higher than the planned RBE-weighted doses (RBE=1.1) to the tumor. We established a new empirical proton RBE model that is more accurate than previous empirical models, and that predicts much higher RBE values in the distal edge of clinical proton beams.

A double-strand-break model for the relative biological effectiveness of electrons based on ionization clustering.

The effectiveness of ionizing radiation regarding DNA damage induction depends on its spatial energy deposition pattern. For electrons an increased effectiveness is observed at low kinetic energies due to the enhanced density of energy deposition events at electron track ends. A model is presented, which enables the calculation of the double-strand-break (DSB) yield and the relative biological effectiveness (RBE) for DSB induction of electrons. The model applies the mean free path between two ionizations and the assumption that two ionizations within a certain threshold distance are necessary to potentially lead to a DSB. Next to an expression for the electron RBE according to its common definition, a local RBE is determined, which describes the electrons' local effectiveness at a defined point on their track. This local RBE allows a better understanding of microscopic processes resulting from radiation and can be used, for instance, to describe the mean effectiveness of the mixed electron radiation field as a function of the radial distance to the center of an ion track. The presented model reflects the experimentally observed increased effectiveness of low-energetic electrons. It will be used in a future work to improve RBE predictions for ions performed with the local effect model.

Impact of DNA Repair Kinetics and Dose Rate on RBE Predictions in the UNIVERSE.

Accurate knowledge of the relative biological effectiveness (RBE) and its dependencies is crucial to support modern ion beam therapy and its further development. However, the influence of different dose rates of the reference radiation and ion beam are rarely considered. The ion beam RBE-model within our “UNIfied and VERSatile bio response Engine” (UNIVERSE) is extended by including DNA damage repair kinetics to investigate the impact of dose-rate effects on the predicted RBE. It was found that dose-rate effects increase with dose and biological effects saturate at high dose-rates, which is consistent with data- and model-based studies in the literature. In a comparison with RBE measurements from a high dose in-vivo study, the predictions of the presented modification were found to be improved in comparison to the previous version of UNIVERSE and existing clinical approaches that disregard dose-rate effects. Consequently, DNA repair kinetics and the different dose rates applied by the reference and ion beams might need to be considered in biophysical models to accurately predict the RBE. Additionally, this study marks an important step in the further development of UNIVERSE, extending its capabilities in giving theoretical guidance to support progress in ion beam therapy.

Extension of the BIANCA biophysical model up to Fe-ions and applications for space radiation research

BIANCA (BIophysical ANalysis of Cell death and chromosome Aberrations) is a biophysical model, implemented as a Monte Carlo code, which simulates the induction of chromosome aberrations and cell death by different monochromatic ion beams (i.e., different ion types and energy values), as well as photons. In previous works, the model predictions for cell survival and lymphocyte dicentrics along therapeutic-like ion beams have been successfully benchmarked against experimental data. With the aim of evaluating the biological damage induced by Galactic Cosmic Rays (GCR), in this study BIANCA was extended up to Fe-ions. A radiobiological database describing human skin fibroblast cell survival and lymphocyte dicentrics as a function of ion type (1≤Z≤26) and energy, as well as dose, was constructed. Afterwards, interfacing BIANCA with the FLUKA Monte Carlo transport code, a feasibility study was performed to calculate the Relative Biological Effectiveness (RBE) of Galactic Cosmic Rays, both for dicentrics and for cell survival; the results were discussed with respect to available space radiation protection limits. Following this work, BIANCA can now provide RBE predictions of cell killing, which can be related to deterministic effects, and lymphocyte dicentrics, more related to stochastic effects, for space radiation exposure.

Comprehensive comparison of local effect model IV predictions with the particle irradiation data ensemble.

The increased relative biological effectiveness (RBE) of ions is one of the key benefits of ion radiotherapy compared to conventional radiotherapy with photons. To account for the increased RBE of ions during the process of ion radiotherapy treatment planning, a robust model for RBE predictions is indispensable. Currently, at several ion therapy centers the local effect model I (LEM I) is applied to predict the RBE, which varies with biological and physical impacting factors. After the introduction of LEM I, several model improvements were implemented, leading to the current version, LEM IV, which is systematically tested in this study. As a comprehensive RBE model should give consistent results for a large variety of ion species and energies, the particle irradiation data ensemble (PIDE) is used to systematically validate the LEM IV. The database covers over 1100 photon and ion survival experiments in form of their linear-quadratic parameters for a wide range of ion types and energies. This makes the database an optimal tool to challenge the systematic dependencies of the RBE model. After appropriate filtering of the database, 571 experiments were identified and used as test data. The study confirms that the LEM IV reflects the RBE systematics observed in measurements well. It is able to reproduce the dependence of RBE on the linear energy transfer (LET) as well as on the αγ /βγ ratio for several ion species in a wide energy range. Additionally, the systematic quantitative analysis revealed precision capabilities and limits of the model. At lower LET values, the LEM IV tends to underestimate the RBE with an increasing underestimation with increasing atomic number of the ion. At higher LET values, the LEM IV overestimates the RBE for protons or helium ions, whereas the predictions for heavier ions match experimental data well. The LEM IV is able to predict general RBE characteristics for several ion species in a broad energy range. The accuracy of the predictions is reasonable considering the small number of input parameters needed by the model. The detailed quantification of possible systematic deviations, however, enables to identify not only strengths but also limitations of the model. The gained knowledge can be used to develop model adjustments to further improve the model accuracy, which is on the way.

Healthy Tissue Damage Following Cancer Ion Therapy: A Radiobiological Database Predicting Lymphocyte Chromosome Aberrations Based on the BIANCA Biophysical Model.

Chromosome aberrations are widely considered among the best biomarkers of radiation health risk due to their relationship with late cancer incidence. In particular, aberrations in peripheral blood lymphocytes (PBL) can be regarded as indicators of hematologic toxicity, which is a major limiting factor of radiotherapy total dose. In this framework, a radiobiological database describing the induction of PBL dicentrics as a function of ion type and energy was developed by means of the BIANCA (BIophysical ANalysis of Cell death and chromosome Aberrations) biophysical model, which has been previously applied to predict the effectiveness of therapeutic-like ion beams at killing tumour cells. This database was then read by the FLUKA Monte Carlo transport code, thus allowing us to calculate the Relative Biological Effectiveness (RBE) for dicentric induction along therapeutic C-ion beams. A comparison with previous results showed that, while in the higher-dose regions (e.g., the Spread-Out Bragg Peak, SOBP), the RBE for dicentrics was lower than that for cell survival. In the lower-dose regions (e.g., the fragmentation tail), the opposite trend was observed. This work suggests that, at least for some irradiation scenarios, calculating the biological effectiveness of a hadrontherapy beam solely based on the RBE for cell survival may lead to an underestimation of the risk of (late) damage to healthy tissues. More generally, following this work, BIANCA has gained the capability of providing RBE predictions not only for cell killing, but also for healthy tissue damage.

Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions.

Treatment plans in proton therapy are more sensitive to uncertainties than in conventional photon therapy. In addition to setup uncertainties, proton therapy is affected by uncertainties in proton range and relative biological effectiveness (RBE). While to date a constant RBE of 1.1 is commonly assumed, the actual RBE is known to increase toward the distal end of the spread-out Bragg peak. Several models for variable RBE predictions exist. We present a framework to evaluate the combined impact and interactions of setup, range, and RBE uncertainties in a comprehensive, variance-based sensitivity analysis (SA). The variance-based SA requires a large number (104 -105 ) of RBE-weighted dose (RWD) calculations. Based on a particle therapy extension of the research treatment planning system CERR we implemented a fast, graphics processing unit (GPU) accelerated pencil beam modeling of patient and range shifts. For RBE predictions, two biological models were included: The mechanistic repair-misrepair-fixation (RMF) model and the phenomenological Wedenberg model. All input parameters (patient position, proton range, RBE model parameters) are sampled simultaneously within their assumed probability distributions. Statistical formalisms rank the input parameters according to their influence on the overall uncertainty of RBE-weighted dose-volume histogram (RW-DVH) quantiles and the RWD in every voxel, resulting in relative, normalized sensitivity indices (S=0: noninfluential input, S=1: only influential input). Results are visualized as RW-DVHs with error bars and sensitivity maps. The approach is demonstrated for two representative brain tumor cases and a prostate case. The full SA including RWD calculations took 39, 11, and 55min, respectively. Range uncertainty was an important contribution to overall uncertainty at the distal end of the target, while the relatively smaller uncertainty inside the target was governed by biological uncertainties. Consequently, the uncertainty of the RW-DVH quantile D98 for the target was governed by range uncertainty while the uncertainty of the mean target dose was dominated by the biological parameters. The SA framework is a powerful and flexible tool to evaluate uncertainty in RWD distributions and DVH quantiles, taking into account physical and RBE uncertainties and their interactions. The additional information might help to prioritize research efforts to reduce physical and RBE uncertainties and could also have implications for future approaches to biologically robust planning and optimization.

Investigating the impact of alpha/beta and LETd on relative biological effectiveness in scanned proton beams: An in vitro study based on human cell lines.

PurposeA relative biological effectiveness (RBE) of 1.1 is commonly used in clinical proton therapy, irrespective of tissue type and depth. This in vitro study was conducted to quantify the RBE of scanned protons as a function of the dose‐averaged linear energy transfer (LETd) and the sensitivity factor (α/ß)X. Additionally, three phenomenological models (McNamara, Rørvik, and Jones) and one mechanistic model (repair‐misrepair‐fixation, RMF) were applied to the experimentally derived data.MethodsFour human cell lines (FaDu, HaCat, Du145, SKMel) with differential (α/ß)X ratios were irradiated in a custom‐designed irradiation setup with doses between 0 and 6 Gy at proximal, central, and distal positions of a 80 mm spread‐out Bragg peak (SOBP) centered at 80 mm (setup A: proton energies 66.5–135.6 MeV) and 155 mm (setup B: proton energies 127.2–185.9 MeV) depth, respectively. LETd values at the respective cell positions were derived from Monte Carlo simulations performed with the treatment planning system (TPS, RayStation). Dosimetric measurements were conducted to verify dose homogeneity and dose delivery accuracy. RBE values were derived for doses that resulted in 90 % (RBE90) and 10 % (RBE10) of cell survival, and survival after a 0.5 Gy dose (RBE0.5Gy), 2 Gy dose (RBE2Gy), and 6 Gy dose (RBE6Gy).ResultsLETd values at sample positions were 1.9, 2.1, 2.5, 2.8, 4.1, and 4.5 keV/µm. For the cell lines with high (α/ß)X ratios (FaDu, HaCat), the LETd did not impact on the RBE. For low (α/ß)X cell lines (Du145, SKMel), LQ‐derived survival curves indicated a clear correlation of LETd and RBE. RBE90 values up to 2.9 and RBE10 values between 1.4 and 1.8 were obtained. Model‐derived RBE predictions slightly overestimated the RBE for the high (α/ß)X cell lines, although all models except the Jones model provided RBE values within the experimental uncertainty. For low (α/ß)X cell lines, no agreement was found between experiments and model predictions, that is, all models underestimated the measured RBE.ConclusionsThe sensitivity parameter (α/ß)X was observed to be a major influencing factor for the RBE of protons and its sensitivity toward LETd changes. RBE prediction models are applicable for high (α/ß)X cell lines but do not estimate RBE values with sufficient accuracy in low (α/ß)X cell lines.

Determining RBE for development of lung fibrosis induced by fractionated irradiation with carbon ions utilizing fibrosis index and high-LET BED model

Background and purposesCarbon ion radiotherapy (CIRT) with raster scanning technology is a promising treatment for lung cancer and thoracic malignancies. Determining normal tissue tolerance of organs at risk is of utmost importance for the success of CIRT. Here we report the relative biological effectiveness (RBE) of CIRT as a function of dose and fractionation for development of pulmonary fibrosis using well established fibrosis index (FI) model. Materials and MethodsDose series of fractionated clinical quality CIRT versus conventional photon irradiation to the whole thorax were compared in C57BL6 mice. Quantitative assessment of pulmonary fibrosis was performed by applying the FI to computed tomography (CT) data acquired 24-weeks post irradiation. RBE was calculated as the ratio of photon to CIRT dose required for the same level of FI. Further RBE predictions were performed using the derived equation from high-linear energy transfer biologically effective dose (high-LET BED) model. ResultsThe averaged lung fibrosis RBE of 5-fraction CIRT schedule was determined as 2.75 ± 0.55. The RBE estimate at the half maximum effective dose (RBEED50) was estimated at 2.82 for clinically relevant fractional sizes of 1–6 Gy. At the same dose range, an RBE value of 2.81 ± 0.40 was predicted by the high-LET BED model. The converted biologically effective dose (BED) of CIRT for induction of half maximum FI (BEDED50) was identified to be 58.12 Gy3.95. In accordance, an estimated RBE of 2.88 was obtained at the BEDED50 level. The LQ model radiosensitivity parameters for 5-fraction was obtained as αH = 0.3030 ± 0.0037 Gy−1 and βH = 0.0056 ± 0.0007 Gy−2. ConclusionThis is the first report of RBE estimation for CIRT with the endpoint of pulmonary fibrosis in-vivo. We proposed in present study a novel way to mathematically modeling RBE by integrating RBEmax and α/βL based on conventional high-LET BED conception. This model well predicted RBE in the clinically relevant dose range but is sensitive to the uncertainties of α/β estimates from the reference photon irradiation (α/βL). These findings will assist to eliminate current uncertainties in prediction of CIRT induced normal tissue complications and builds a solid foundation for development of more accurate in-vivo data driven RBE estimates.

AT THE PHYSICS-BIOLOGY INTERFACE: THE NEUTRON AFFAIR.

We present predictions of neutron relative biological effectiveness (RBE) for cell irradiations with neutron beams at PTB-Braunschweig. A neutron RBE model is adopted to evaluate initial DNA damage induction given the neutron-induced charged particle field. RBE values are predicted for cell exposures to quasi-monoenergetic beams (0.56 MeV, 1.2 MeV) and to a broad energy distribution neutron field with dose-averaged energy of 5.75 MeV. Results are compared to what obtained with our RBE predictions for neutrons at similar energies, when a 30-cm sphere is irradiated in an isotropic neutron field. RBE values for experimental conditions are higher for the lowest neutron energies, because, as expected, target geometry determines the weight of the low-effectiveness photon component of the neutron dose. These results highlight the importance of characterizing neutron fields in terms of physical interactions, to fully understand neutron-induced biological effects, contributing to risk estimation and to the improvement of radiation protection standards.

WE‐H‐BRA‐07: Mechanistic Modelling of the Relative Biological Effectiveness of Heavy Charged Particles

Purpose Uncertainty in the Relative Biological Effectiveness (RBE) of heavy charged particles compared to photons remains one of the major uncertainties in particle therapy. As RBEs depend strongly on clinical variables such as tissue type, dose, and radiation quality, more accurate individualised models are needed to fully optimise treatments. MethodsWe have developed a model of DNA damage and repair following X‐ray irradiation in a number of settings, incorporating mechanistic descriptions of DNA repair pathways, geometric effects on DNA repair, cell cycle effects and cell death. Our model has previously been shown to accurately predict a range of biological endpoints including chromosome aberrations, mutations, and cell death. This model was combined with nanodosimetric models of individual ion tracks to calculate the additional probability of lethal damage forming within a single track. These lethal damage probabilities can be used to predict survival and RBE for cells irradiated with ions of different Linear Energy Transfer (LET). ResultsBy combining the X‐ray response model with nanodosimetry information, predictions of RBE can be made without cell‐line specific fitting. The model's RBE predictions were found to agree well with empirical proton RBE models (Mean absolute difference between models of 1.9% and 1.8% for cells with α/β ratios of 9 and 1.4, respectively, for LETs between 0 and 15 keV/µm). The model also accurately recovers the impact of high‐LET carbon ion exposures, showing both the reduced efficacy of ions at extremely high LET, as well as the impact of defects in non‐homologous end joining on RBE values in Chinese Hamster Ovary cells.ConclusionOur model is predicts RBE without the inclusion of empirical LET fitting parameters for a range of experimental conditions. This approach has the potential to deliver improved personalisation of particle therapy, with future developments allowing for the calculation of individualised RBEs.SJM is supported by a Marie Curie International Outgoing Fellowship from the European Commission's FP7 program (EC FP7 MC‐IOF‐623630)

Relative Biological Effectiveness of HZE Particles for Chromosomal Exchanges and Other Surrogate Cancer Risk Endpoints.

The biological effects of high charge and energy (HZE) particle exposures are of interest in space radiation protection of astronauts and cosmonauts, and estimating secondary cancer risks for patients undergoing Hadron therapy for primary cancers. The large number of particles types and energies that makeup primary or secondary radiation in HZE particle exposures precludes tumor induction studies in animal models for all but a few particle types and energies, thus leading to the use of surrogate endpoints to investigate the details of the radiation quality dependence of relative biological effectiveness (RBE) factors. In this report we make detailed RBE predictions of the charge number and energy dependence of RBE’s using a parametric track structure model to represent experimental results for the low dose response for chromosomal exchanges in normal human lymphocyte and fibroblast cells with comparison to published data for neoplastic transformation and gene mutation. RBE’s are evaluated against acute doses of γ-rays for doses near 1 Gy. Models that assume linear or non-targeted effects at low dose are considered. Modest values of RBE (<10) are found for simple exchanges using a linear dose response model, however in the non-targeted effects model for fibroblast cells large RBE values (>10) are predicted at low doses <0.1 Gy. The radiation quality dependence of RBE’s against the effects of acute doses γ-rays found for neoplastic transformation and gene mutation studies are similar to those found for simple exchanges if a linear response is assumed at low HZE particle doses. Comparisons of the resulting model parameters to those used in the NASA radiation quality factor function are discussed.

Integration of a model-independent interface for RBE predictions in a treatment planning system for active particle beam scanning

Especially for heavier ions such as carbon ions, treatment planning systems (TPSs) for ion radiotherapy depend on models predicting the relative biological effectiveness (RBE) of the particles involved. Such models are subject to intensive research and the choice of the optimal RBE model is a matter of debate. On the other hand TPSs are often strongly coupled to particular RBE models and transition even to extended models of the same family can be difficult. We present here a model-independent interface which allows the unbiased use of any RBE model capable of providing dose–effect curves (even sampled curves) for a TPS. The full decoupling between the RBE model and TPS is based on the beam-mixing model proposed by Lam which is, in contrast to the often-used Zaider–Rossi model, independent of the explicit form of the underlying dose–effect curves. This approach not only supports the refinement of RBE models without adaptations of the TPS—which we demonstrate by means of the local effect model (LEM)—but also allows the comparison of very different model approaches on a common basis. We exemplify this by a comparison between the LEM and a model from the literature for proton RBE prediction.

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra

The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damage simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β)X = 2 Gy. These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization.

Temporal Lobe Reactions After Carbon Ion Radiation Therapy: Comparison of Relative Biological Effectiveness–Weighted Tolerance Doses Predicted by Local Effect Models I and IV

To compare the relative biological effectiveness (RBE)-weighted tolerance doses for temporal lobe reactions after carbon ion radiation therapy using 2 different versions of the local effect model (LEM I vs LEM IV) for the same patient collective under identical conditions. In a previous study, 59 patients were investigated, of whom 10 experienced temporal lobe reactions (TLR) after carbon ion radiation therapy for low-grade skull-base chordoma and chondrosarcoma at Helmholtzzentrum für Schwerionenforschung (GSI) in Darmstadt, Germany in 2002 and 2003. TLR were detected as visible contrast enhancements on T1-weighted MRI images within a median follow-up time of 2.5 years. Although the derived RBE-weighted temporal lobe doses were based on the clinically applied LEM I, we have now recalculated the RBE-weighted dose distributions using LEM IV and derived dose-response curves with Dmax,V-1 cm³ (the RBE-weighted maximum dose in the remaining temporal lobe volume, excluding the volume of 1 cm³ with the highest dose) as an independent dosimetric variable. The resulting RBE-weighted tolerance doses were compared with those of the previous study to assess the clinical impact of LEM IV relative to LEM I. The dose-response curve of LEM IV is shifted toward higher values compared to that of LEM I. The RBE-weighted tolerance dose for a 5% complication probability (TD5) increases from 68.8 ± 3.3 to 78.3 ± 4.3 Gy (RBE) for LEM IV as compared to LEM I. LEM IV predicts a clinically significant increase of the RBE-weighted tolerance doses for the temporal lobe as compared to the currently applied LEM I. The limited available photon data do not allow a final conclusion as to whether RBE predictions of LEM I or LEM IV better fit better clinical experience in photon therapy. The decision about a future clinical application of LEM IV therefore requires additional analysis of temporal lobe reactions in a comparable photon-treated collective using the same dosimetric variable as in the present study.