A phenomenological biological dose model for proton therapy based on linear energy transfer spectra

Novel radiation quality metrics accounting for proton energy spectra for RBE proton models

BACKGROUND

For proton therapy, a relative biological effectiveness (RBE) of 1.1 is widely applied clinically. However, due to abundant evidence of variable RBE in vitro, and as suggested in studies of patient outcomes, RBE might increase by the end of the proton tracks, as described by several proposed variable RBE models. Typically, the dose averaged linear energy transfer (LET d $\text{LET}_d$ ) has been used as a radiation quality metric (RQM) for these models. However, the optimal choice of RQM has not been fully explored.

PURPOSE

This study aims to propose novel RQMs that effectively weight protons of different energies, and assess their predictive power for variable RBE in proton therapy. The overall objective is to identify an RQM that better describes the contribution of individual particles to the RBE of proton beams.

METHODS

High-throughput experimental set-ups of in vitro cell survival studies for proton RBE determination are simulated utilizing the SHIELD-HIT12A Monte Carlo particle transport code. For every data point, the proton energy spectra are simulated, allowing the calculation of novel RQMs by applying different power levels to the spectra of LET or effective Q $Q$ (Q eff $Q_\mathrm{eff}$ ) values. A phenomenological linear-quadratic-based RBE model is then applied to the in vitro data, using various RQMs as input variables, and the model performance is evaluated by root-mean-square-error (RMSE) for the logarithm of cell surviving fractions of each data point.

RESULTS

Increasing the power level, that is, putting an even higher weight on higher LET particles when constructing the RQM is generally associated with an increased model performance, with dose averagedLET 3 $\text{LET}^3$ (i.e., dose averaged cubed LET,cLET d $\mathrm{cLET}_d$ ) resulting in a RMSE value 0.31, compared to 0.45 for a model based on (linearly weighted)LET d $\text{LET}_d$ , with similar trends also observed for track averaged andQ eff $Q_\mathrm{eff}$ -based RQMs.

CONCLUSIONS

The results indicate that improved proton variable RBE models can be constructed assuming a non-linear RBE(LET) relationship for individual protons. If similar trends hold also for an in vitro-environment, variable RBE effects are likely better described bycLET d $\mathrm{cLET}_d$ or tracked averaged cubed LET (cLET t $\mathrm{cLET}_t$ ), or correspondingQ eff $Q_\mathrm{eff}$ -based RQM, rather than linearly weightedLET d $\text{LET}_d$ orLET t $\text{LET}_t$ which is conventionally applied today.

Impact of Relative Biologic Effectiveness for Proton Therapy for Head and Neck and Skull-Base Tumors: A Technical and Clinical Review

Proton therapy has emerged as a crucial tool in the treatment of head and neck and skull-base cancers, offering advantages over photon therapy in terms of decreasing integral dose and reducing acute and late toxicities, such as dysgeusia, feeding tube dependence, xerostomia, secondary malignancies, and neurocognitive dysfunction. Despite its benefits in dose distribution and biological effectiveness, the application of proton therapy is challenged by uncertainties in its relative biological effectiveness (RBE). Overcoming the challenges related to RBE is key to fully realizing proton therapy's potential, which extends beyond its physical dosimetric properties when compared with photon-based therapies. In this paper, we discuss the clinical significance of RBE within treatment volumes and adjacent serial organs at risk in the management of head and neck and skull-base tumors. We review proton RBE uncertainties and its modeling and explore clinical outcomes. Additionally, we highlight technological advancements and innovations in plan optimization and treatment delivery, including linear energy transfer/RBE optimizations and the development of spot-scanning proton arc therapy. These advancements show promise in harnessing the full capabilities of proton therapy from an academic standpoint, further technological innovations and clinical outcome studies, however, are needed for their integration into routine clinical practice.

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.

The dirty and clean dose concept: Towards creating proton therapy treatment plans with a photon-like dose response

BACKGROUND

Applying tolerance doses for organs at risk (OAR) from photon therapy introduces uncertainties in proton therapy when assuming a constant relative biological effectiveness (RBE) of 1.1.

PURPOSE

This work introduces the novel dirty and clean dose concept, which allows for creating treatment plans with a more photon-like dose response for OAR and, thus, less uncertainties when applying photon-based tolerance doses.

METHODS

The concept divides the 1.1-weighted dose distribution into two parts: the clean and the dirty dose. The clean and dirty dose are deposited by protons with a linear energy transfer (LET) below and above a set LET threshold, respectively. For the former, a photon-like dose response is assumed, while for the latter, the RBE might exceed 1.1. To reduce the dirty dose in OAR, a MaxDirtyDose objective was added in treatment plan optimization. It requires setting two parameters: LET threshold and max dirty dose level. A simple geometry consisting of one target volume and one OAR in water was used to study the reduction in dirty dose in the OAR depending on the choice of the two MaxDirtyDose objective parameters during plan optimization. The best performing parameter combinations were used to create multiple dirty dose optimized (DDopt) treatment plans for two cranial patient cases. For each DDopt plan, 1.1-weighted dose, variable RBE-weighted dose using the Wedenberg RBE model and dose-average LETd distributions as well as resulting normal tissue complication probability (NTCP) values were calculated and compared to the reference plan (RefPlan) without MaxDirtyDose objectives.

RESULTS

In the water phantom studies, LET thresholds between 1.5 and 2.5 keV/µm yielded the best plans and were subsequently used. For the patient cases, nearly all DDopt plans led to a reduced Wedenberg dose in critical OAR. This reduction resulted from an LET reduction and translated into an NTCP reduction of up to 19 percentage points compared to the RefPlan. The 1.1-weighted dose in the OARs was slightly increased (patient 1: 0.45 Gy(RBE), patient 2: 0.08 Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1-weighted dose in healthy brain tissue was observed (patient 1: 0.81 Gy(RBE), patient 2: 0.53 Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3 Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp).

CONCLUSIONS

The novel dirty and clean dose concept allows for creating biologically more robust proton treatment plans with a more photon-like dose response. The reduced uncertainties in RBE can, therefore, mitigate uncertainties introduced by using photon-based tolerance doses for OAR.

Variable RBE in proton radiotherapy: a comparative study with the predictive Mayo Clinic Florida microdosimetric kinetic model and phenomenological models of cell survival

Objectives. (1) To examine to what extent the cell- and exposure- specific information neglected in the phenomenological proton relative biological effectiveness (RBE) models could influence the computed RBE in proton therapy. (2) To explore similarities and differences in the formalism and the results between the linear energy transfer (LET)-based phenomenological proton RBE models and the microdosimetry-based Mayo Clinic Florida microdosimetric kinetic model (MCF MKM). (3) To investigate how the relationship between the RBE and the dose-mean proton LET is affected by the proton energy spectrum and the secondary fragments.Approach. We systematically compared six selected phenomenological proton RBE models with the MCF MKM in track-segment simulations, monoenergetic proton beams in a water phantom, and two spread-out Bragg peaks. A representative comparison within vitrodata for human glioblastoma cells (U87 cell line) is also included.Main results. Marked differences were observed between the results of the phenomenological proton RBE models, as reported in previous studies. The dispersion of these models' results was found to be comparable to the spread in the MCF MKM results obtained by varying the cell-specific parameters neglected in the phenomenological models. Furthermore, while single cell-specific correlation between RBE and the dose-mean proton LET seems reasonable above 2 keVμm-1, caution is necessary at lower LET values due to the relevant contribution of secondary fragments. The comparison within vitrodata demonstrates comparable agreement between the MCF MKM predictions and the results of the phenomenological models.Significance. The study highlights the importance of considering cell-specific characteristics and detailed radiation quality information for accurate RBE calculations in proton therapy. Furthermore, these results provide confidence in the use of the MCF MKM for clonogenic survival RBE calculations in proton therapy, offering a more mechanistic approach compared to phenomenological models.

Influence of beam pruning techniques on LET and RBE in proton arc therapy

Introduction

Proton arc therapy (PAT) is an emerging treatment modality that holds promise to improve target volume coverage and reduce linear energy transfer (LET) in organs at risk. We aimed to investigate if pruning the highest energy layers in each beam direction could increase the LET in the target and reduce LET in tissue and organs at risk (OAR) surrounding the target volume, thus reducing the relative biological effectiveness (RBE)-weighted dose and sparing healthy tissue.

Methods

PAT plans for a germinoma, an ependymoma and a rhabdomyosarcoma patient were created in the Eclipse treatment planning system with a prescribed dose of 54 Gy(RBE) using a constant RBE of 1.1 (RBE1.1). The PAT plans was pruned for high energy spots, creating several PAT plans with different amounts of pruning while maintaining tumor coverage, denoted PX-PAT plans, where X represents the amount of pruning. All plans were recalculated in the FLUKA Monte Carlo software, and the LET, physical dose, and variable RBE-weighted dose from the phenomenological Rørvik (ROR) model and an LET weighted dose (LWD) model were evaluated.

Results and discussion

For the germinoma case, all plans but the P6-PAT reduced the mean RBE-weighted dose to the surrounding healthy tissue compared to the PAT plan. The LET was increasingly higher within the PTV for each pruning iteration, where the mean LET from the P6-PAT plan was 1.5 keV / μm higher than for the PAT plan, while the P4- and P5-PAT plans provided an increase of 0.4 and 0.7 keV / μm , respectively. The other plans increased the LET by a smaller margin compared to the PAT plan. Likewise, the LET values to the healthy tissue were reduced for each degree of pruning. Similar results were found for the ependymoma and the rhabdomyosarcoma case. We demonstrated a PAT pruning technique that can increase both LET and RBE in the target volume and at the same time decreased values in healthy tissue, without affecting the target volume dose coverage.

GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size

Objective.Shortcomings of dose-averaged linear energy transfer (LETD), the quantity which is most commonly used to quantify proton relative biological effectiveness, have long been recognized. Microdosimetric spectra may overcome the limitations of LETDbut are extremely computationally demanding to calculate. A systematic library of lineal energy spectra for monoenergetic protons could enable rapid determination of microdosimetric spectra in a clinical environment. The objective of this work was to calculate and validate such a library of lineal energy spectra.Approach. SuperTrack, a GPU-accelerated CUDA/C++ based application, was developed to superimpose tracks calculated using Geant4 onto targets of interest and to compute microdosimetric spectra. Lineal energy spectra of protons with energies from 0.1 to 100 MeV were determined in spherical targets of diameters from 1 nm to 10μm and in bounding voxels with side lengths of 5μm and 3 mm.Main results.Compared to an analogous Geant4-based application, SuperTrack is up to 3500 times more computationally efficient if each track is resampled 1000 times. Dose spectra of lineal energy and dose-mean lineal energy calculated with SuperTrack were consistent with values published in the literature and with comparison to a Geant4 simulation. Using SuperTrack, we developed the largest known library of proton microdosimetric spectra as a function of primary proton energy, target size, and bounding volume size.Significance. SuperTrack greatly increases the computational efficiency of the calculation of microdosimetric spectra. The elevated lineal energy observed in a 3 mm side length bounding volume suggests that lineal energy spectra determined experimentally or computed in small bounding volumes may not be representative of the lineal energy spectra in voxels of a dose calculation grid. The library of lineal energy spectra calculated in this work could be integrated with a treatment planning system for rapid determination of lineal energy spectra in patient geometries.

Formulation of Time-Dependent Cell Survival with Saturable Repairability of Radiation Damage

This study aims to provide a model that compounds historically proposed ideas regarding cell survival irradiated with X rays or particles. The parameters used in this model have simple meanings and are closely related to cell death-related phenomena. The model is adaptable to a wide range of doses and dose rates and thus can consistently explain previously published cell survival data. The formulas of the model were derived by using five basic ideas: 1. "Poisson's law"; 2. "DNA affected damage"; 3. "repair"; 4. "clustered affected damage"; and 5. "saturation of reparability". The concept of affected damage is close to but not the same as the effect caused by the double-strand break (DSB). The parameters used in the formula are related to seven phenomena: 1. "linear coefficient of radiation dose"; 2. "probability of making affected damage"; 3. "cell-specific repairability", 4. "irreparable damage by adjacent affected damage"; 5. "recovery of temporally changed repairability"; 6. "recovery of simple damage which will make the affected damage"; 7. "cell division". By using the second parameter, this model includes cases where a single hit results in repairable-lethal and double-hit results in repairable-lethal. The fitting performance of the model for the experimental data was evaluated based on the Akaike information criterion, and practical results were obtained for the published experiments irradiated with a wide range of doses (up to several 10 Gy) and dose rates (0.17 Gy/h to 55.8 Gy/h). The direct association of parameters with cell death-related phenomena has made it possible to systematically fit survival data of different cell types and different radiation types by using crossover parameters.

Hypoxia adapted relative biological effectiveness models for proton therapy: a simulation study

In proton therapy, a constant relative biological effectiveness (RBE) factor of 1.1 is applied although the RBE has been shown to depend on factors including the Linear Energy Transfer (LET). The biological effectiveness of radiotherapy has also been shown to depend on the level of oxygenation, quantified by the oxygen enhancement ratio (OER). To estimate the biological effectiveness across different levels of oxygenation the RBE-OER-weighted dose (ROWD) can be used. To investigate the consistency between different approaches to estimate ROWD, we implemented and compared OER models in a Monte Carlo (MC) simulation tool. Five OER models were explored: Wenzl and Wilkens 2011 (WEN), Tinganelliet al2015 (TIN), Strigariet al2018 (STR), Dahleet al2020 (DAH) and Meinet al2021 (MEI). OER calculations were combined with a proton RBE model and the microdosimetric kinetic model for ROWD calculations. ROWD and OER were studied for a water phantom scenario and a head and neck cancer case using hypoxia PET data for the OER calculation. The OER and ROWD estimates from the WEN, MEI and DAH showed good agreement while STR and TIN gave higher OER values and lower ROWD. The WEN, STR and DAH showed some degree of OER-LET dependency while this was negligible for the MEI and TIN models. The ROWD for all implemented models is reduced in hypoxic regions with an OER of 1.0-2.1 in the target volume. While some variations between the models were observed, all models display a large difference in the estimated dose from hypoxic and normoxic regions. This shows the potential to increase the dose or LET in hypoxic regions or reduce the dose to normoxic regions which again could lead to normal tissue sparing. With reliable hypoxia imaging, RBE-OER weighting could become a useful tool for proton therapy plan optimization.

A case-control study of linear energy transfer and relative biological effectiveness related to symptomatic brainstem toxicity following pediatric proton therapy

BACKGROUND AND PURPOSE

A fixed relative biological effectiveness (RBE) of 1.1 (RBE_1.1) is used clinically in proton therapy even though the RBE varies with properties such as dose level and linear energy transfer (LET). We therefore investigated if symptomatic brainstem toxicity in pediatric brain tumor patients treated with proton therapy could be associated with a variable LET and RBE.

MATERIALS AND METHODS

36 patients treated with passive scattering proton therapy were selected for a case-control study from a cohort of 954 pediatric brain tumor patients. Nine children with symptomatic brainstem toxicity were each matched to three controls based on age, diagnosis, adjuvant therapy, and brainstem RBE_1.1 dose characteristics. Differences across cases and controls related to the dose-averaged LET (LET_d) and variable RBE-weighted dose from two RBE models were analyzed in the high-dose region.

RESULTS

LET_d metrics were marginally higher for cases vs. controls for the majority of dose levels and brainstem substructures. Considering areas with doses above 54 Gy(RBE_1.1), we found a moderate trend of 13% higher median LET_d in the brainstem for cases compared to controls (P = .08), while the difference in the median variable RBE-weighted dose for the same structure was only 2% (P = .6).

CONCLUSION

Trends towards higher LET_d for cases compared to controls were noticeable across structures and LET_d metrics for this patient cohort. While case-control differences were minor, an association with the observed symptomatic brainstem toxicity cannot be ruled out.

Quantifying Systematic RBE-Weighted Dose Uncertainty Arising from Multiple Variable RBE Models in Organ at Risk

PURPOSE

Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models.

METHODS

Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming( α β ) x of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties.

RESULTS

The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique.

CONCLUSIONS

Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different( α β ) x revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique is recommended for brain tumor when CTV and OARs overlap.

Proton Therapy for Prostate Cancer: Challenges and Opportunities

Simple Summary

Reported clinical outcomes of proton therapy (PT) for localized prostate cancer are similar to photon-based external beam radiotherapy. Apparently, the dosimetric advantages of PT have yet to be translated to clinical benefits. The suboptimal clinical outcomes of PT might be attributable to inadequate dose prescription, as indicated by the ASCENDE-RT trial. Moreover, uncertainties involved in the treatment planning and delivery processes, as well as technological limitations in PT treatment systems, may lead to discrepancies between planned doses and actual doses delivered to patients. In this article, we reviewed the current status of PT for prostate cancer and discussed different clinical implementations that could potentially improve the clinical outcome of PT for prostate cancer. Various technological advancements under which uncertainties in dose calculations can be minimized, including MRI-guided PT, dual-energy photon-counting CT and high-resolution Monte Carlo-based treatment planning systems, are highlighted.

Abstract

The dosimetric advantages of proton therapy (PT) treatment plans are demonstrably superior to photon-based external beam radiotherapy (EBRT) for localized prostate cancer, but the reported clinical outcomes are similar. This may be due to inadequate dose prescription, especially in high-risk disease, as indicated by the ASCENDE-RT trial. Alternatively, the lack of clinical benefits with PT may be attributable to improper dose delivery, mainly due to geometric and dosimetric uncertainties during treatment planning, as well as delivery procedures that compromise the dose conformity of treatments. Advanced high-precision PT technologies, and treatment planning and beam delivery techniques are being developed to address these uncertainties. For instance, external magnetic resonance imaging (MRI)-guided patient setup rooms are being developed to improve the accuracy of patient positioning for treatment. In-room MRI-guided patient positioning systems are also being investigated to improve the geometric accuracy of PT. Soon, high-dose rate beam delivery systems will shorten beam delivery time to within one breath hold, minimizing the effects of organ motion and patient movements. Dual-energy photon-counting computed tomography and high-resolution Monte Carlo-based treatment planning systems are available to minimize uncertainties in dose planning calculations. Advanced in-room treatment verification tools such as prompt gamma detector systems will be used to verify the depth of PT. Clinical implementation of these new technologies is expected to improve the accuracy and dose conformity of PT in the treatment of localized prostate cancers, and lead to better clinical outcomes. Improvement in dose conformity may also facilitate dose escalation, improving local control and implementation of hypofractionation treatment schemes to improve patient throughput and make PT more cost effective.

The Organ Sparing Potential of Different Biological Optimization Strategies in Proton Therapy

Purpose

Variable relative biological effectiveness (RBE) models allow for differences in linear energy transfer (LET), physical dose, and tissue type to be accounted for when quantifying and optimizing the biological damage of protons. These models are complex and fraught with uncertainties, and therefore, simpler RBE optimization strategies have also been suggested. Our aim was to compare several biological optimization strategies for proton therapy by evaluating their performance in different clinical cases.

Methods and Materials

Two different optimization strategies were compared: full variable RBE optimization and differential RBE optimization, which involve applying fixed RBE for the planning target volume (PTV) and variable RBE in organs at risk (OARs). The optimization strategies were coupled to 2 variable RBE models and 1 LET-weighted dose model, with performance demonstrated on 3 different clinical cases: brain, head and neck, and prostate tumors.

Results

In cases with low (α/β)x in the tumor, the full RBE optimization strategies had a large effect, with up to 10% reduction in RBE-weighted dose to the PTV and OARs compared with the reference plan, whereas smaller variations (<5%) were obtained with differential optimization. For tumors with high (α/β)x, the differential RBE optimization strategy showed a greater reduction in RBE-weighted dose to the OARs compared with the reference plan and the full RBE optimization strategy.

Conclusions

Differences between the optimization strategies varied across the studied cases, influenced by both biological and physical parameters. Whereas full RBE optimization showed greater OAR sparing, awareness of underdosage to the target must be carefully considered.

Exploratory Investigation of Dose-Linear Energy Transfer (LET) Volume Histogram (DLVH) for Adverse Events Study in Intensity Modulated Proton Therapy (IMPT)

PURPOSE

We proposed a novel tool-a dose linear energy transfer (LET)-volume histogram (DLVH)-and performed an exploratory study to investigate rectal bleeding in prostate cancer treated with intensity modulated proton therapy.

METHODS AND MATERIALS

The DLVH was constructed with dose and LET as 2 axes, and the normalized volume of the structure was contoured in the dose-LET plane as isovolume lines. We defined the DLVH index, DLv%(d,l) (ie, v% of the structure) to have a dose of ≥d Gy and an LET of ≥l keV/μm, similar to the dose-volume histogram index Dv%. Nine patients with prostate cancer with rectal bleeding (Common Terminology Criteria for Adverse Events grade ≥2) were included as the adverse event group, and 48 patients with no complications were considered the control group. A P value map was constructed by comparison of the DLVH indices of all patients between the 2 groups using the Mann-Whitney U test. Dose-LET volume constraints (DLVCs) were derived based on the P value map with a manual selection procedure facilitated by Spearman's correlation tests. The obtained DLVCs were further cross-validated using a multivariate support vector machine (SVM)-based normal tissue complication probability (NTCP) model with an independent testing data set composed of 8 adverse event and 13 control patients.

RESULTS

We extracted 2 DLVC constraints. One DLVC was obtained, Vdose/LETboundary:2.5keVμmat 75 Gy to 3.2keVμmat8.65Gy <1.27% (DLVC1), revealing a high LET volume effect. The second DLVC, V(72.2Gy,0keVμm) < 2.23% (DVLC2), revealed a high dose volume effect. The SVM-based NTCP model with 2 DLVCs provided slightly superior performance than using dose only, with an area under the curve of 0.798 versus 0.779 for the testing data set.

CONCLUSIONS

Our results demonstrated the importance of rectal "hot spots" in both high LET (DLVC1) and high dose (DLVC2) in inducing rectal bleeding. The SVM-based NTCP model confirmed the derived DLVCs as good predictors for rectal bleeding when intensity modulated proton therapy is used to treat prostate cancer.

A Critical Review of LET-Based Intensity-Modulated Proton Therapy Plan Evaluation and Optimization for Head and Neck Cancer Management

In this review article, we review the 3 important aspects of linear-energy-transfer (LET) in intensity-modulated proton therapy (IMPT) for head and neck (H&N) cancer management. Accurate LET calculation methods are essential for LET-guided plan evaluation and optimization, which can be calculated either by analytical methods or by Monte Carlo (MC) simulations. Recently, some new 3D analytical approaches to calculate LET accurately and efficiently have been proposed. On the other hand, several fast MC codes have also been developed to speed up the MC simulation by simplifying nonessential physics models and/or using the graphics processor unit (GPU)–acceleration approach. Some concepts related to LET are also briefly summarized including (1) dose-weighted versus fluence-weighted LET; (2) restricted versus unrestricted LET; and (3) microdosimetry versus macrodosimetry. LET-guided plan evaluation has been clinically done in some proton centers. Recently, more and more studies using patient outcomes as the biological endpoint have shown a positive correlation between high LET and adverse events sites, indicating the importance of LET-guided plan evaluation in proton clinics. Various LET-guided plan optimization methods have been proposed to generate proton plans to achieve biologically optimized IMPT plans. Different optimization frameworks were used, including 2-step optimization, 1-step optimization, and worst-case robust optimization. They either indirectly or directly optimize the LET distribution in patients while trying to maintain the same dose distribution and plan robustness. It is important to consider the impact of uncertainties in LET-guided optimization (ie, LET-guided robust optimization) in IMPT, since IMPT is sensitive to uncertainties including both the dose and LET distributions. We believe that the advancement of the LET-guided plan evaluation and optimization will help us exploit the unique biological characteristics of proton beams to improve the therapeutic ratio of IMPT to treat H&N and other cancers.

Proton RBE models: commonalities and differences

Uncertainties in the relative biological effectiveness (RBE) of protons remains a major barrier to the biological optimisation of proton therapy. While a constant value of 1.1 is widely used in treatment planning, extensive preclinical in vitro and in vivo data suggests that proton RBE is variable, depending on proton energy, target tissue, and endpoint. A number of phenomenological models have been developed to try and explain this variation, but agreement between these models is often poor. This has been attributed to both the models' underlying assumptions and the data to which they are fit. In this brief note, we investigate the underlying trends in these models by comparing their predictions as a function of relevant biological and physical parameters, to determine where models are in conceptual agreement or disagreement. By doing this, it can be seen that the primary differences between models arise from how they handle biological parameters (i.e. [Formula: see text] and [Formula: see text] from the linear-quadratic model for photon exposures). By contrast, when specifically explored for linear energy transfer-dependence, all models show extremely good correlation. These observations suggest that there is a pressing need for more systematic exploration of biological variation in RBE across different cells in well-controlled conditions to help distinguish between these different models and identify the true behaviour.

Variation in relative biological effectiveness for cognitive structures in proton therapy of pediatric brain tumors

BACKGROUND

Clinically, a constant value of 1.1 is used for the relative biological effectiveness (RBE) of protons, whereas in vitro the RBE has been shown to vary depending on physical dose, tissue type, and linear energy transfer (LET). As the LET increases at the distal end of the proton beam, concerns exist for an elevated RBE in normal tissues. The aim of this study was therefore to investigate the heterogeneity of RBE to brain structures associated with cognition (BSCs) in pediatric suprasellar tumors.

MATERIAL AND METHODS

Intensity-modulated proton therapy (IMPT) plans for 10 pediatric craniopharyngioma patients were re-calculated using 11 phenomenological and two plan-based variable RBE models. Based on LET, tissue dependence and number of data points used to fit the models, the three RBE models considered the most relevant for the studied endpoint were selected. Thirty BSCs were investigated in terms of RBE and dose/volume parameters.

RESULTS

For a representative patient, the median (range) dose-weighted mean RBE (RBE_d) across all BSCs from the plan-based models was among the lowest (1.09 (1.02-1.52) vs. the phenomenological models at 1.21 (0.78-2.24)). Omitting tissue dependency resulted in RBE_d at 1.21 (1.04-2.24). Across all patients, the narrower RBE model selection gave median RBE_d values from 1.22 to 1.30.

CONCLUSION

For all BSCs, there was a systematic model-dependent variation in RBE_d, mirroring the uncertainty in biological effects of protons. According to a refined selection of in vitro models, the RBE variation across BSCs was in effect underestimated when using a fixed RBE of 1.1.

Incorporation of the LETd-weighted biological dose in the evaluation of breast intensity-modulated proton therapy plans

PURPOSE

To evaluate the LETd-weighted biological dose to OARs in proton therapy for breast cancer and to study the relationship of the LETd-weighted biological dose relative to the standard dose (RBE = 1.1) and thereby to provide estimations of the biological dose uncertainties with the standard dose calculations (RBE = 1.1) commonly used in clinical practice.

METHOD

This study included 20 patients who received IMPT treatment to the whole breast/chest wall and regional lymph nodes. The LETd distributions were calculated along with the physical dose using an open-source Monte Carlo simulation package, MCsquare. Using the McMahon linear model, the LETd-weighted biological dose was computed from the physical dose and LETd. OAR doses were compared between the Dose (RBE = 1.1) and the LETd-weighted biological dose, on brachial plexus, rib, heart, esophagus, and Ipsilateral lung.

RESULTS

On average, the LETd-weighted biological dose compared to the Dose (RBE = 1.1) was higher by 8% for the brachial plexus D0.1 cc, 13% for the ribs D0.5 cc, 24% for mean heart dose, and 10% for the esophagus D0.1 cc, respectively. The LETd-weighted doses to the Ipsilateral lung V5, V10, and V20 were comparable to the Dose (RBE = 1.1). No statistically significant difference in biological dose enhancement to OARs was observed between the intact breast group and the CW group, with the exception of the ribs: the CW group experienced slightly greater biological dose enhancement (13% vs. 12%, p = 0.04) to the ribs than the intact breast group.

CONCLUSION

Enhanced biological dose was observed compared to standard dose with assumed RBE of 1.1 for the heart, ribs, esophagus, and brachial plexus in breast/CW and regional nodal IMPT plans. Variable RBE models should be considered in the evaluation of the IMPT breast plans, especially for OARs located near the end of range of a proton beam. Clinical outcome studies are needed to validate model predictions for clinical toxicities.

Spatial Agreement of Brainstem Dose Distributions Depending on Biological Model in Proton Therapy for Pediatric Brain Tumors

Purpose

During radiation therapy for pediatric brain tumors, the brainstem is a critical organ at risk, possibly with different radio-sensitivity across its substructures. In proton therapy, treatment planning is currently performed using a constant relative biological effectiveness (RBE) of 1.1 (RBE_1.1), whereas preclinical studies point toward spatial variability of this factor. To shed light on this biological uncertainty, we investigated the spatial agreement between isodose maps produced by different RBE models, with emphasis on (smaller) substructures of the brainstem.

Methods and Materials

Proton plans were recalculated using Monte Carlo simulations in 3 anonymized pediatric patients with brain tumors (a craniopharyngioma, a low-grade glioma, and a posterior fossa ependymoma) to obtain dose and linear energy transfer distributions. Doses and volume metrics for the brainstem and its substructures were calculated using a constant RBE_1.1, 4 phenomenological RBE models with varying (α/β)_x parameters, and with a simpler linear energy transfer-dependent model. The spatial agreement between the dose distributions of constant RBE_1.1 versus the variable RBE models was compared using the Dice similarity coefficient.

Results

The spatial agreement between the variable RBE dose distributions and RBE_1.1 decreased with increasing isodose levels in all patient cases. The patient with ependymoma showed the greatest variation in dose and dose volumes, where V_50Gy(RBE) in the brainstem increased from 32% (RBE_1.1) to 35% to 49% depending on the applied model, corresponding to a spatial agreement (Dice similarity coefficient) between 0.79 and 0.95. The remaining patients showed similar trends, however, with lower absolute values due to lower brainstem doses.

Conclusions

All phenomenological RBE models fully enclosed the isodose volumes of the constant RBE_1.1, and the volumes based on variable RBE spatially agreed. The spatial agreement was dependent on the isodose level, where higher isodose levels showed larger expansions and less agreement between the variable RBE models and RBE_1.1.

RBE for proton radiation therapy - a Nordic view in the international perspective

BACKGROUND

This paper presents an insight into the critical discussions and the current strategies of the Nordic countries for handling the variable proton relative biological effectiveness (RBE) as presented at The Nordic Collaborative Workshop for Particle Therapy that took place at the Skandion Clinic on 14th and 15th of November 2019.

MATERIAL AND METHODS

In the current clinical practice at the two proton centres in operation at the date, Skandion Clinic, and the Danish Centre for Particle Therapy, a constant proton RBE of 1.1 is applied. The potentially increased effectiveness at the end of the particle range is however considered at the stage of treatment planning at both places based on empirical observations and knowledge. More elaborated strategies to evaluate the plans and mitigate the problem are intensely investigated internationally as well at the two centres. They involve the calculation of the dose-averaged linear energy transfer (LET_d) values and the assessment of their distributions corroborated with the distribution of the dose and the location of the critical clinical structures.

RESULTS

Methods and tools for LET_d calculations are under different stages of development as well as models to account for the variation of the RBE with LET_d, dose per fraction, and type of tissue. The way they are currently used for evaluation and optimisation of the plans and their robustness are summarised. A critical but not exhaustive discussion of their potential future implementation in the clinical practice is also presented.

CONCLUSIONS

The need for collaboration between the clinical proton centres in establishing common platforms and perspectives for treatment planning evaluation and optimisation is highlighted as well as the need of close interaction with the research academic groups that could offer a complementary perspective and actively help developing methods and tools for clinical implementation of the more complex metrics for considering the variable effectiveness of the proton beams.

RBE variation in prostate carcinoma cells in active scanning proton beams: In-vitro measurements in comparison with phenomenological models

PURPOSE

In-vitro radiobiological studies are essential for modelling the relative biological effectiveness (RBE) in proton therapy. The purpose of this study was to experimentally determine the RBE values in proton beams along the beam path for human prostate carcinoma cells (Du-145). RBE-dose and RBE-LET_d (dose-averaged linear energy transfer) dependencies were investigated and three phenomenological RBE models, i.e. McNamara, Rørvik and Wilkens were benchmarked for this cell line.

METHODS

Cells were placed at multiple positions along the beam path, employing an in-house developed solid phantom. The experimental setup reflected the clinical prostate treatment scenario in terms of field size, depth, and required proton energies (127.2-180.1 MeV) and the physical doses from 0.5 to 6 Gy were delivered. The reference irradiation was performed with 200 kV X-ray beams. Respective (α/β) values were determined using the linear quadratic model and LET_d was derived from the treatment planning system at the exact location of cells.

RESULTS AND CONCLUSION

Independent of the cell survival level, all experimental RBE values were consistently higher in the target than the generic clinical RBE value of 1.1; with the lowest RBE value of 1.28 obtained at the beginning of the SOBP. A systematic RBE decrease with increasing dose was observed for the investigated dose range. The RBE values from all three applied models were considerably smaller than the experimental values. A clear increase of experimental RBE values with LET_d parameter suggests that proton LET must be taken into consideration for this low (α/β) tissue.

The FLUKA Monte Carlo code coupled with an OER model for biologically weighted dose calculations in proton therapy of hypoxic tumors

INTRODUCTION

The increased radioresistance of hypoxic cells compared to well-oxygenated cells is quantified by the oxygen enhancement ratio (OER). In this study we created a FLUKA Monte Carlo based tool for inclusion of both OER and relative biological effectiveness (RBE) in biologically weighted dose (ROWD) calculations in proton therapy and applied this to explore the impact of hypoxia.

METHODS

The RBE-weighted dose was adapted for hypoxia by making RBE model parameters dependent on the OER, in addition to the linear energy transfer (LET). The OER depends on the partial oxygen pressure (pO₂) and LET. To demonstrate model performance, calculations were done with spread-out Bragg peaks (SOBP) in water phantoms with pO₂ ranging from strongly hypoxic to normoxic (0.01-30 mmHg) and with a head and neck cancer proton plan optimized with an RBE of 1.1 and pO₂ estimated voxel-by-voxel using [¹⁸F]-EF5 PET. An RBE of 1.1 and the Rørvik RBE model were used for the ROWD calculations.

RESULTS

The SOBP in water had decreasing ROWD with decreasing pO₂. In the plans accounting for oxygenation, the median target doses were approximately a factor 1.1 lower than the corresponding plans which did not consider the OER. Hypoxia adapted target ROWDs were considerably more heterogeneous than the RBE_1.1-weighted doses.

CONCLUSION

We realized a Monte Carlo based tool for calculating the ROWD. Read-in of patient pO₂ and estimation of ROWD with flexibility in choice of RBE model was achieved, giving a tool that may be useful in future clinical applications of hypoxia-guided particle therapy.

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

PURPOSE

A 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 (LET_d ) 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.

METHODS

Four 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. LET_d 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 (RBE_0.5Gy ), 2 Gy dose (RBE_2Gy ), and 6 Gy dose (RBE_6Gy ).

RESULTS

LET_d 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 LET_d did not impact on the RBE. For low (α/ß)_X cell lines (Du145, SKMel), LQ-derived survival curves indicated a clear correlation of LET_d 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.

CONCLUSIONS

The sensitivity parameter (α/ß)_X was observed to be a major influencing factor for the RBE of protons and its sensitivity toward LET_d 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.

Inter-patient variations in relative biological effectiveness for cranio-spinal irradiation with protons

Cranio-spinal irradiation (CSI) using protons has dosimetric advantages compared to photons and is expected to reduce risk of adverse effects. The proton relative biological effectiveness (RBE) varies with linear energy transfer (LET), tissue type and dose, but a variable RBE has not replaced the constant RBE of 1.1 in clinical treatment planning. We examined inter-patient variations in RBE for ten proton CSI patients. Variable RBE models were used to obtain RBE and RBE-weighted doses. RBE was quantified in terms of dose weighted organ-mean RBE ([Formula: see text] = mean RBE-weighted dose/mean physical dose) and effective RBE of the near maximum dose (D2%), i.e. RBED2% = [Formula: see text], where subscripts RBE and phys indicate that the D2% is calculated based on an RBE model and the physical dose, respectively. Compared to the median [Formula: see text] of the patient population, differences up to 15% were observed for the individual [Formula: see text] values found for the thyroid, while more modest variations were seen for the heart (6%), lungs (2%) and brainstem (<1%). Large inter-patient variation in RBE could be correlated to large spread in LET and dose for these organs at risk (OARs). For OARs with small inter-patient variations, the results show that applying a population based RBE in treatment planning may be a step forward compared to using RBE of 1.1. OARs with large inter-patient RBE variations should ideally be selected for patient-specific biological or RBE robustness analysis if the physical doses are close to known dose thresholds.

Proton therapy for head and neck squamous cell carcinomas: A review of the physical and clinical challenges

The quality of radiation therapy has been shown to significantly influence the outcomes for head and neck squamous cell carcinoma (HNSCC) patients. The results of dosimetric studies suggest that intensity-modulated proton therapy (IMPT) could be of added value for HNSCC by being more effective than intensity-modulated (photon) radiation therapy (IMRT) for reducing side effects of radiation therapy. However, the physical properties of protons make IMPT more sensitive than photons to planning uncertainties. This could potentially have a negative effect on the quality of IMPT planning and delivery. For this review, the three French proton therapy centers collaborated to evaluate the differences between IMRT and IMPT. The review explored the effects of these uncertainties and their management for developing a robust and optimized IMPT treatment delivery plan to achieve clinical outcomes that are superior to those for IMRT. We also provide practical suggestions for the management of HNSCC carcinoma with IMPT. Because metallic dental implants can increase range uncertainties (3-10%), patient preparation for IMPT may require more systematic removal of in-field alien material than is done for IMRT. Multi-energy CT may be an alternative to calculate more accurately the dose distribution. The practical aspects that we describe are essential to guarantee optimal quality in radiation therapy in both model-based and randomized clinical trials.

In Silico Models of DNA Damage and Repair in Proton Treatment Planning: A Proof of Concept

There is strong in vitro cell survival evidence that the relative biological effectiveness (RBE) of protons is variable, with dependence on factors such as linear energy transfer (LET) and dose. This is coupled with the growing in vivo evidence, from post-treatment image change analysis, of a variable RBE. Despite this, a constant RBE of 1.1 is still applied as a standard in proton therapy. However, there is a building clinical interest in incorporating a variable RBE. Recently, correlations summarising Monte Carlo-based mechanistic models of DNA damage and repair with absorbed dose and LET have been published as the Manchester mechanistic (MM) model. These correlations offer an alternative path to variable RBE compared to the more standard phenomenological models. In this proof of concept work, these correlations have been extended to acquire RBE-weighted dose distributions and calculated, along with other RBE models, on a treatment plan. The phenomenological and mechanistic models for RBE have been shown to produce comparable results with some differences in magnitude and relative distribution. The mechanistic model found a large RBE for misrepair, which phenomenological models are unable to do. The potential of the MM model to predict multiple endpoints presents a clear advantage over phenomenological models.

Modelling variable proton relative biological effectiveness for treatment planning

Dose in proton radiotherapy is generally prescribed by scaling the physical proton dose by a constant value of 1.1. Relative biological effectiveness (RBE) is defined as the ratio of doses required by two radiation modalities to cause the same level of biological effect. The adoption of an RBE of 1.1. assumes that the biological efficacy of protons is similar to photons, allowing decades of clinical dose prescriptions from photon treatments and protocols to be utilized in proton therapy. There is, however, emerging experimental evidence that indicates that proton RBE varies based on technical, tissue and patient factors. The notion that a single scaling factor may be used to equate the effects of photons and protons across all biological endpoints and doses is too simplistic and raises concern for treatment planning decisions. Here, we review the models that have been developed to better predict RBE variations in tissue based on experimental data as well as using a mechanistic approach.

[Proton therapy for head and neck squamous cell carcinomas: From physics to clinic]

Intensity-modulated radiation therapy (IMRT) is presently the recommended technique for the treatment of locally advanced head and neck carcinomas. Proton therapy would allow to reduce the volume of irradiated normal tissue and, thus, to decrease the risk of late dysphagia, xerostomia, dysgeusia and hypothyroidism. An exhaustive research was performed with the search engine PubMed by focusing on the papers about the physical difficulties that slow down use of proton therapy for head and neck carcinomas. Range uncertainties in proton therapy (±3 %) paradoxically limit the use of the steep dose gradient in distality. Calibration uncertainties can be important in the treatment of head and neck cancer in the presence of materials of uncertain stoichiometric composition (such as with metal implants, dental filling, etc.) and complex heterogeneities. Dental management for example may be different with IMRT or proton therapy. Some uncertainties can be somewhat minimized at the time of optimization. Inter- and intrafractional variations and uncertainties in Hounsfield units/stopping power can be integrated in a robust optimization process. Additional changes in patient's anatomy (tumour shrinkage, changes in skin folds in the beam patch, large weight loss or gain) require rescanning. Dosimetric and small clinical studies comparing photon and proton therapy have well shown the interest of proton therapy for head and neck cancers. Intensity-modulated proton therapy is a promising treatment as it can reduce the substantial toxicity burden of patients with head and neck squamous cell carcinoma compared to IMRT. Robust optimization will allow to perform an optimal treatment and to use proton therapy in current clinical practice.

Using the Proton Energy Spectrum and Microdosimetry to Model Proton Relative Biological Effectiveness

PURPOSE

We introduce a methodology to calculate the microdosimetric quantity dose-mean lineal energy for input into the microdosimetric kinetic model (MKM) to model the relative biological effectiveness (RBE) of proton irradiation experiments.

METHODS AND MATERIALS

The data from 7 individual proton RBE experiments were included in this study. In each experiment, the RBE at several points along the Bragg curve was measured. Monte Carlo simulations to calculate the lineal energy probability density function of 172 different proton energies were carried out with use of Geant4 DNA. We calculated the fluence-weighted lineal energy probability density function (f_w(y)), based on the proton energy spectra calculated through Monte Carlo at each experimental depth, calculated the dose-mean lineal energy y_D¯ for input into the MKM, and then computed the RBE. The radius of the domain (r_d) was varied to reach the best agreement between the MKM-predicted RBE and experimental RBE. A generic RBE model as a function of dose-averaged linear energy transfer (LET_D) with 1 fitting parameter was presented and fit to the experimental RBE data as well to facilitate a comparison to the MKM.

RESULTS

Both the MKM and LET_D-based models modeled the RBE from experiments well. Values for r_d were similar to those of other cell lines under proton irradiation that were modeled with the MKM. Analysis of the performance of each model revealed that neither model was clearly superior to the other.

CONCLUSIONS

Our 3 key accomplishments include the following: (1) We developed a method that uses the proton energy spectra and lineal energy distributions of those protons to calculate dose-mean lineal energy. (2) We demonstrated that our application of the MKM provides theoretical validation of proton irradiation experiments that show that RBE is significantly greater than 1.1. (3) We showed that there is no clear evidence that the MKM is better than LET_D-based RBE models.

Exploration and application of phenomenological RBE models for proton therapy

The relative biological effectiveness (RBE) of protons varies with multiple physical and biological factors. Phenomenological RBE models have been developed to include such factors in the estimation of a variable RBE, in contrast to the clinically applied constant RBE of 1.1. In this study, eleven published phenomenological RBE models and two plan-based models were explored and applied to simulated patient cases. All models were analysed with respect to the distribution and range of linear energy transfer (LET) and reference radiation fractionation sensitivity ((α/β) _x ) of their respective experimental databases. Proton therapy plans for a spread-out Bragg peak in water and three patient cases (prostate adenocarcinoma, pituitary adenoma and thoracic sarcoma) were optimised using an RBE of 1.1 in the Eclipse^™ treatment planning system prior to recalculation and modelling in the FLUKA Monte Carlo code. Model estimated dose-volume parameters for the planning target volumes (PTVs) and organs at risk (OAR) were compared. The experimental in vitro databases for the various models differed greatly in the range of (α/β) _x values and dose-averaged LET (LET_d). There were significant variations between the model estimations, which arose from fundamental differences in the database definitions and model assumptions. The greatest variations appeared in organs with low (α/β) _x and high LET_d, e.g. biological doses given to late responding OARs located distal to the target in the treatment field. In general, the variation in maximum dose (D_2%) was larger than the variation in mean dose and other dose metrics, with D_2% of the left optic nerve ((α/β) _x   =  2.1 Gy) in the pituitary adenoma case showing the greatest discrepancies between models: 28-52 Gy(RBE), while D_2% for RBE_1.1 was 30 Gy(RBE). For all patient cases, the estimated mean RBE to the PTV was in the range 1.09-1.29 ((α/β) _x   =  1.5/3.1/10.6 Gy). There were considerable variations between the estimations of RBE and RBE-weighted doses from the different models. These variations were a consequence of fundamental differences in experimental databases, model assumptions and regression techniques. The results from the implementation of RBE models in dose planning studies should be evaluated in light of these deviations.

New insights in the relative radiobiological effectiveness of proton irradiation

BACKGROUND

Proton radiotherapy is a form of charged particle therapy that is preferentially applied for the treatment of tumors positioned near to critical structures due to their physical characteristics, showing an inverted depth-dose profile. The sparing of normal tissue has additional advantages in the treatment of pediatric patients, in whom the risk of secondary cancers and late morbidity is significantly higher. Up to date, a fixed relative biological effectiveness (RBE) of 1.1 is commonly implemented in treatment planning systems with protons in order to correct the physical dose. This value of 1.1 comes from averaging the results of numerous in vitro experiments, mostly conducted in the middle of the spread-out Bragg peak, where RBE is relatively constant. However, the use of a constant RBE value disregards the experimental evidence which clearly demonstrates complex RBE dependency on dose, cell- or tissue type, linear energy transfer and biological endpoints. In recent years, several in vitro studies indicate variations in RBE of protons which translate to an uncertainty in the biological effective dose delivery to the patient. Particularly for regions surrounding the Bragg peak, the more localized pattern of energy deposition leads to more complex DNA lesions. These RBE variations of protons bring the validity of using a constant RBE into question.

MAIN BODY

This review analyzes how RBE depends on the dose, different biological endpoints and physical properties. Further, this review gives an overview of the new insights based on findings made during the last years investigating the variation of RBE with depth in the spread out Bragg peak and the underlying differences in radiation response on the molecular and cellular levels between proton and photon irradiation. Research groups such as the Klinische Forschergruppe Schwerionentherapie funded by the German Research Foundation (DFG, KFO 214) have included work on this topic and the present manuscript highlights parts of the preclinical work and summarizes the research activities in this context.

SHORT CONCLUSION

In summary, there is an urgent need for more coordinated in vitro and in vivo experiments that concentrate on a realistic dose range of in clinically relevant tissues like lung or spinal cord.