Variable RBE in proton therapy: comparison of different model predictions and their influence on clinical-like scenarios.

BACKGROUND: In proton radiation therapy a constant relative biological effectiveness (RBE) of 1.1 is usually assumed. However, biological experiments have evidenced RBE dependencies on dose level, proton linear energy transfer (LET) and tissue type. This work compares the predictions of three of the main radio-biological models proposed in the literature by Carabe-Fernandez, Wedenberg, Scholz and coworkers. METHODS: Using the chosen models, a spread-out Bragg peak (SOBP) as well as two exemplary clinical cases (single field and two fields) for cranial proton irradiation, all delivered with state-of-the-art pencil-beam scanning, have been analyzed in terms of absorbed dose, dose-averaged LET (LET D ), RBE-weighted dose (D RBE) and biological range shift distributions. RESULTS: In the systematic comparison of RBE predictions by the three models we could show different levels of agreement depending on (α/ß) x and LET values. The SOBP study emphasizes the variation of LET D and RBE not only as a function of depth but also of lateral distance from the central beam axis. Application to clinical-like scenario shows consistent discrepancies from the values obtained for a constant RBE of 1.1, when using a variable RBE scheme for proton irradiation in tissues with low (α/ß) x , regardless of the model. Biological range shifts of 0.6- 2.4 mm (for high (α/ß) x ) and 3.0 - 5.4 mm (for low (α/ß) x ) were found from the fall-off analysis of individual profiles of RBE-weighted fraction dose along the beam penetration depth. CONCLUSIONS: Although more experimental evidence is needed to validate the accuracy of the investigated models and their input parameters, their consistent trend suggests that their main RBE dependencies (dose, LET and (α/ß) x ) should be included in treatment planning systems. In particular, our results suggest that simpler models based on the linear-quadratic formalism and LETD might already be sufficient to reproduce important RBE dependencies for re-evaluation of plans optimized with the current RBE = 1.1 approximation. This approach would be a first step forward to consider RBE variations in proton therapy, thus enabling a more robust choice of biological dose delivery. The latter could in turn impact clinical outcome, especially in terms of reduced toxicities for tumors adjacent to organs at risk.

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

PURPOSE: 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. METHODS AND MATERIALS: 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. RESULTS: 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/ß)X = 2 Gy. CONCLUSIONS: 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.

Modelling of cell killing due to sparsely ionizing radiation in normoxic and hypoxic conditions and an extension to high LET radiation.

PURPOSE: An approach for describing cell killing with sparsely ionizing radiation in normoxic and hypoxic conditions based on the initial number of randomly distributed DNA double-strand breaks (DSB) is proposed. An extension of the model to high linear energy transfer (LET) radiation is also presented. MATERIALS AND METHODS: The model is based on the probabilities that a given DNA giant loop has one DSB or at least two DSB. A linear combination of these two classes of damage gives the mean number of lethal lesions. When coupled with a proper modelling of the spatial distribution of DSB from ion tracks, the formalism can be used to predict cell response to high LET radiation in aerobic conditions. RESULTS: Survival data for sparsely ionizing radiation of cell lines in normoxic/hypoxic conditions were satisfactorily fitted with the proposed parametrization. It is shown that for dose ranges up to about 10 Gy, the model describes tested experimental survival data as good as the linear-quadratic model does. The high LET extension yields a reasonable agreement with data in aerobic conditions. CONCLUSIONS: A new survival model has been introduced that is able to describe the most relevant features of cellular dose-response postulating two damage classes.

Dynamic Target Definition: a novel approach for PTV definition in ion beam therapy.

PURPOSE: To present a beam arrangement specific approach for PTV definition in ion beam therapy. MATERIALS AND METHODS: By means of a Monte Carlo error propagation analysis a criteria is formulated to assess whether a voxel is safely treated. Based on this a non-isotropical expansion rule is proposed aiming to minimize the impact of uncertainties on the dose delivered. RESULTS: The method is exemplified in two cases: a Head and Neck case and a Prostate case. In both cases the modality used is proton beam irradiation and the sources of uncertainties taken into account are positioning (set up) errors and range uncertainties. It is shown how different beam arrangements have an impact on plan robustness which leads to different target expansions necessary to assure a predefined level of plan robustness. The relevance of appropriate beam angle arrangements as a way to minimize uncertainties is demonstrated. CONCLUSIONS: A novel method for PTV definition in on beam therapy is presented. The method show promising results by improving the probability of correct dose CTV coverage while reducing the size of the PTV volume. In a clinical scenario this translates into an enhanced tumor control probability while reducing the volume of healthy tissue being irradiated.

Air Kerma Rate estimation by means of in-situ gamma spectrometry: a Bayesian approach.

Bayesian inference is used to determine the Air Kerma Rate based on in-situ gamma spectrum measurement performed with an NaI(Tl) scintillation detector. The procedure accounts for uncertainties in the measurement and in the mass energy transfer coefficients needed for the calculation. The WinBUGS program (Spiegelhalter et al., 1999) was used. The results show that the relative uncertainties in the Air Kerma estimate are of about 1%, and that the choice of unfolding procedure may lead to an estimate systematic error of 3%.