Uncertainties In Relative Biological Effectiveness Research Articles

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.

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

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. 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. 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. In the water phantom studies, LET thresholds between 1.5 and 2.5keV/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: 0.45Gy(RBE), patient 2: 0.08Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1-weighted dose in healthy brain tissue was observed (patient 1: 0.81Gy(RBE), patient 2: 0.53Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp). 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.

RBE-weighted dose and its impact on the risk of acute coronary event for breast cancer patients treated with intensity modulated proton therapy.

PurposeTo evaluate the relative biological effectiveness (RBE)‐weighted dose to the heart and to estimate RBE uncertainties when assuming a constant RBE of 1.1, for breast cancer patients receiving intensity‐modulated proton therapy (IMPT). Further, to study the impact of RBE uncertainties on the risk of an acute coronary event (ACE).Material and methodsWe analyzed 20 patients who received IMPT to either the left breast (n = 10) or left chest wall (n = 10) and regional lymph nodes. The Monte Carlo simulation engine, MCsquare, was used to simulate the dose‐averaged linear energy transfer (LETd) map. The RBE‐weighted dose to the heart and its substructures was calculated using three different RBE models. The risk of ACE was estimated per its linear relationship with mean heart dose (MHD) as established by Darby et al.ResultsThe median MHD increased from 1.33 GyRBE assuming an RBE of 1.1 to 1.64, 1.87, and 1.99 GyRBE when using the RBE‐weighted dose models. The median values (and ranges) of the excess absolute risk of ACE were 0.4% (0.1%–0.8%) when assuming an RBE of 1.1, and 0.6% (0.2%–1.0%), 0.6% (0.2%–1.1%), and 0.7% (0.2%–1.1%) with the RBE‐weighted models. For our patient cohort, the maximum excess absolute risk of ACE increased by 0.3% with the RBE‐weighted doses compared to the constant RBE of 1.1, reaching an excess absolute ACE risk of 1.1%. The interpatient LETd variation was small for the relevant high‐dose regions of the heart.ConclusionAll three RBE models predicted a higher biological dose compared to the clinical standard dose assuming a constant RBE of 1.1. An underestimation of the biological dose results in underestimation of the ACE risk. Analyzing the voxel‐by‐voxel biological dose and the LET map alongside clinical outcomes is warranted in the development of a more accurate normal‐tissue complication probability model.

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.

Clinical implementation in proton therapy of multi-field optimization by a hybrid method combining conventional PTV with robust optimization

To implement a robust multi-field optimization (MFO) technique compatible with the application of a Monte Carlo (MC) algorithm and to evaluate its robustness. Nine patients (three brain, five head-and-neck, one spine) underwent proton treatment generated by a novel robust MFO technique. A hybrid (hMFO) approach was implemented, planning dose coverage on isotropic PTV compensating for setup errors, whereas range calibration uncertainties are incorporated into PTV robust optimization process. hMFO was compared with single-field optimization (SFO) and full robust multi-field optimization (fMFO), both on the nominal plan and the worst-case scenarios assessed by robustness analysis. The SFO and the fMFO plans were normalized to hMFO on CTV to obtain iso-D95 coverage, and then the organs at risk (OARs) doses were compared. On the same OARs, in the normalized nominal plans the potential impact of variable relative biological effectiveness (RBE) was investigated. hMFO reduces the number of scenarios computed for robust optimization (from twenty-one in fMFO to three), making it practicable with the application of a MC algorithm. After normalizing on D95 CTV coverage, nominal hMFO plans were superior compared to SFO in terms of OARs sparing (p < 0.01), without significant differences compared to fMFO. The improvement in OAR sparing with hMFO with respect to SFO was preserved in worst-case scenarios (p < 0.01), confirming that hMFO is as robust as SFO to physical uncertainties, with no significant differences when compared to the worst case scenarios obtained by fMFO. The dose increase on OARs due to variable RBE was comparable to the increase due to physical uncertainties (i.e. 4–5 Gy(RBE)), but without significant differences between these techniques. hMFO allows improving plan quality with respect to SFO, with no significant differences with fMFO and without affecting robustness to setup, range and RBE uncertainties, making clinically feasible the application of MC-based robust optimization.

Impact of uncertainties in range and RBE on small field proton therapy

The objective of this paper is to evaluate the clinical impact of biological uncertainties in small field proton therapy due to the assumption of using a constant relative biological effectiveness (RBE) value of 1.1 (RBE-fixed) compared to a variable RBE (RBE-weighted). In this context the impact of the applied range margin was investigated.Eight patients with arteriovenous malformation (AVM) treated with proton radiosurgery were selected due to the small target volume. Dose distributions were compared for RBE-weighted and RBE-fixed. The impact of RBE was assessed using Monte Carlo (MC) dose calculations for stereotactic doses and doses of 2 Gy(RBE). Four different α/β ratios were investigated. Additionally, dose distributions were recalculated with reduced range margins.Applying variable RBE values for stereotactic doses resulted in an increase in the mean dose of 1.6% for a low α/β of 2 Gy, but a decrease of 2.6% for an α/β of 10 Gy. However, the mean dose increased to 17.1% and 2.1% for doses of 2 Gy(RBE) and α/β of 2 Gy and 10 Gy, respectively. Reducing range margins from 3.5% + 1 mm to 2.5% + 1 mm resulted in negligible difference in the mean RBE within the target, or 0.1% for stereotactic doses and 0.3% for doses of 2 Gy(RBE). Larger differences were seen for a range reduction to 0% + 1 mm, i.e. 1.1% and 3.0% for stereotactic doses and doses of 2 Gy(RBE), respectively.Because potential RBE effects are typically more pronounced in the distal part of a field, a bigger clinical impact of RBE uncertainties in small fields is expected. Our study shows that this could be significant for tissues with low α/β and a small dose per fraction. The uncertainty in RBE due to the uncertainty associated with the α/β ratio seems larger than the impact of the applied range uncertainty margin on RBE.

Application of variance-based uncertainty and sensitivity analysis to biological modeling in carbon ion treatment plans.

In ion beam therapy, biological models to estimate the relative biological effectiveness (RBE) and subsequently the RBE-weighted dose (RWD) are needed in treatment planning and plan evaluation. The required biological parameters as well as their dependency on ion species and ion energy can typically not be determined directly in experiments for invivo situations. For that reason they are often derived from invitro data and biological modeling and subject to large uncertainties. We present a model-independent Monte Carlo (variance-) based uncertainty and Sensitivity Analysis (SA) approach to quantify the impact of different input uncertainties on a simulated carbon ion treatment plan. The influences of different input uncertainties are examined by variance-based SA methods. In this Monte Carlo approach, a function is evaluated 103 -105 times. For each of those runs, all inputs are changed simultaneously, using random numbers according to their associated uncertainties. Variance-based statistic formalisms then rank the input parameter/uncertainty pairs according to their impact on the result of the function. The method of SA includes an uncertainty analysis and was applied to a two-field spot scanning carbon ion treatment plan for two commonly used biological models and two representative tissue parameter sets. Based on an exemplary patient case, the application of variance-based SA for biological measures, relevant in (carbon) ion therapy, is demonstrated. A voxel-wise calculation for 2.9·105 voxels takes ~6h. A structure-based SA, which adds an uncertainty band to a RWD-volume histogram (RW-DVH) and shows how to decrease the uncertainty in the most effective way, can be calculated in 0.1-1.5h (depending on the size of the structure). The uncertainties in RBE, RWD or RW-DVH are broken down to the impact of different uncertainties in the (biological) model input. Biological uncertainties have a higher impact on the resulting RBE and RWD than uncertainties in the physical dose. Excluding the physical dose from the SA only slightly decreased the overall uncertainty, emphasizing the necessity to include biological uncertainties into treatment plan evaluation. Variance-based SA is a powerful tool to evaluate the impact of uncertainties in (carbon) ion therapy. The number of input parameters that can be examined at once is only limited by computation time. A Monte Carlo-derived, comprehensive uncertainty quantification and a corresponding sensitivity analysis are implemented and provide new information for treatment plan evaluation. A possible future application is a SA-based biologically robust treatment plan optimization using the additional uncertainty information as presented here.

LET-weighted doses effectively reduce biological variability in proton radiotherapy planning

Variations in proton relative biological effectiveness (RBE) with linear energy transfer (LET) remain one of the largest sources of uncertainty in proton radiotherapy. This work seeks to identify metrics which can be applied to mitigate these effects in treatment optimisation, and quantify their effectiveness. Three different metrics—dose, dose × LET and an LET-weighted dose defined as where is the dose-averaged LET—were compared with in vitro experimental studies of proton RBE and clinical treatment plans incorporating RBE models. In each system the biological effects of protons were plotted against these metrics to quantify the degree of variation introduced by unaccounted-for RBE uncertainties. As expected, the LET-dependence of RBE introduces significant variability in the biological effects of protons when plotted against dose alone. Plotting biological effects against dose × LET significantly over-estimated the impact of LET on cell survival, and typically produced even larger spreads in biological effect. LET-weighted dose was shown to have superior correlation to biological effect in both experimental data and clinical plans. For prostate and medulloblastoma treatment plans, the average RBE-associated variability in biological effect is ±5% of the prescribed dose, but is reduced to less than 1% using LET-weighting. While not a replacement for full RBE models, simplified metrics such as this LET-weighted dose can be used to account for the majority of variability which arises from the LET-dependence of RBE with reduced need for biological parameterisation. These metrics may be used to identify regions in normal tissues which may see unexpectedly high effects due to end-of-range elevations of RBE, or as part of a more general tool for biological optimisation in proton therapy.

The influence of breathing motion and a variable relative biological effectiveness in proton therapy of left-sided breast cancer

Background: Proton breast radiotherapy has been suggested to improve target coverage as well as reduce cardiopulmonary and integral dose compared with photon therapy. This study aims to assess this potential when accounting for breathing motion and a variable relative biological effectiveness (RBE).Methods: Photon and robustly optimized proton plans were generated to deliver 50 Gy (RBE) in 25 fractions (RBE = 1.1) to the CTV (whole left breast) for 12 patients. The plan evaluation was performed using the constant RBE and a variable RBE model. Robustness against breathing motion, setup, range and RBE uncertainties was analyzed using CT data obtained at free-breathing, breath-hold-at-inhalation and breath-hold-at-exhalation.Results: All photon and proton plans (RBE = 1.1) met the clinical goals. The variable RBE model predicted an average RBE of 1.18 for the CTVs (range 1.14–1.21) and even higher RBEs in organs at risk (OARs). However, the dosimetric impact of this latter aspect was minor due to low OAR doses. The normal tissue complication probability (NTCP) for the lungs was low for all patients (<1%), and similar for photons and protons. The proton plans were generally considered robust for all patients. However, in the most extreme scenarios, the lowest dose received by 98% of the CTV dropped from 96 to 99% of the prescribed dose to around 92–94% for both protons and photons. Including RBE uncertainties in the robustness analysis resulted in substantially higher worst-case OAR doses.Conclusions: Breathing motion seems to have a minor effect on the plan quality for breast cancer. The variable RBE might impact the potential benefit of protons, but could probably be neglected in most cases where the physical OAR doses are low. However, to be able to identify outlier cases at risk for high OAR doses, the biological evaluation of proton plans taking into account the variable RBE is recommended.

Lateral variations of radiobiological properties of therapeutic fields of 1H, 4He, 12C and 16O ions studied with Geant4 and microdosimetric kinetic model

As known, in cancer therapy with ion beams the relative biological effectiveness (RBE) of ions changes in the course of their propagation in tissues. Such changes are caused not only by increasing the linear energy transfer (LET) of beam particles with the penetration depth towards the Bragg peak, but also by nuclear reactions induced by beam nuclei leading to the production of various secondary particles. Although the changes of RBE along the beam axis have been studied quite well, much less attention has been paid to the evolution of RBE in the transverse direction, perpendicular to the beam axis. In order to fill this gap, we simulated radiation fields of 1H, 4He, 12C and 16O nuclei of 20 mm in diameter by means of a Geant4-based Monte Carlo model for heavy-ion therapy connected with the modified microdosimetric kinetic model to describe the response of normal ( Gy) and early-responding ( Gy) tissues. Depth and radial distributions of saturation-corrected dose-mean lineal energy, RBE and RBE-weighted dose are investigated for passive beam shaping and active beam scanning. The field of 4He has a small lateral spread as compared with 1H field, and it is characterised by a modest lateral variation of RBE suggesting the use of fixed RBE values across the field transverse cross section at each depth. Reduced uncertainties of RBE on the boundary of a 4He treatment field can be advantageous in a specific case of an organ at risk located in lateral proximity to the target volume. It is found that the lateral distributions of RBE calculated for 12C and 16O fields demonstrate fast variations in the radial direction due to changes of dose and composition of secondary fragments in the field penumbra. Nevertheless, the radiation fields of all four projectiles at radii larger than 20 mm can be characterized by a common RBE value defined by tissue radiosensitivity. These findings can help, in particular, in accessing the transverse homogeneity of radiation fields of ions used in studies in vitro.

Incorporation of relative biological effectiveness uncertainties into proton plan robustness evaluation

Background: The constant relative biological effectiveness (RBE) of 1.1 is typically assumed in proton therapy. This study presents a method of incorporating the variable RBE and its uncertainties into the proton plan robustness evaluation.Material and methods: The robustness evaluation was split into two parts. In part one, the worst-case physical dose was estimated using setup and range errors, including the fractionation dependence. The results were fed into part two, in which the worst-case RBE-weighted doses were estimated using a Monte Carlo method for sampling the input parameters of the chosen RBE model. The method was applied to three prostate, breast and head and neck (H&N) plans for several fractionation schedules using two RBE models. The uncertainties in the model parameters, linear energy transfer and α/β were included. The resulting DVH error bands were compared with the use of a constant RBE without uncertainties.Results: All plans were evaluated as robust using the constant RBE. Applying the proposed methodology using the variable RBE models broadens the DVH error bands for all structures studied. The uncertainty in α/β was the dominant factor. The variable RBE also shifted the nominal DVHs towards higher doses for most OARs, whereas the direction of this shift for the clinical target volumes (CTVs) depended on the treatment site, RBE model and fractionation schedule. The average RBE within the CTV, using one of the RBE models and 2 Gy(RBE) per fraction, varied between 1.11–1.26, 1.06–1.16 and 1.14–1.25 for the breast, H&N and prostate patients, respectively.Conclusions: A method of incorporating RBE uncertainties into the robustness evaluation has been proposed. By disregarding the variable RBE and its uncertainties, the variation in the RBE-weighted CTV and OAR doses may be underestimated. This could be an essential factor to take into account, especially in normal tissue complication probabilities based comparisons between proton and photon plans.

Scaling Human Cancer Risks from Low LET to High LET when Dose-Effect Relationships are Complex.

Health risks from space radiations, particularly from densely ionizing radiations, represent an important challenge for long-ranged manned space missions. Reliable methods are needed for scaling low-LET to high-LET radiation risks for humans, based on animal or in vitro studies comparing these radiations. The current standard metric, relative biological effectiveness (RBE) compares iso-effect doses of two radiations. By contrast, a proposed new metric, radiation effects ratio (RER), compares effects of two radiations at the same dose. This definition of RER allows direct scaling of low-LET to high-LET radiation risks in humans at the dose or doses of interest. By contrast to RBE, RER can be used without need for detailed information about dose response shapes for compared radiations. This property of RER allows animal carcinogenesis experiments to be simplified by reducing the number of tested radiation doses. For simple linear dose-effect relationships, RBE = RER. However, for more complex dose-effect relationships, such as those with nontargeted effects at low doses, RER can be lower than RBE. We estimated RBE and RER values and uncertainties using heavy ion (12C, 28Si, 56Fe) and gamma-ray-induced tumors in a mouse model for intestinal cancer (APC1638N/+), and used both RBE and RER to estimate low-LET to high-LET risk scaling factors. The data showed clear evidence of nontargeted effects at low doses. In situations, such as the ones discussed here where nontargeted effects dominate at low doses, RER was lower than RBE by factors around 2.8-3.5 at 0.03 Gy and 1.3-1.4 at 0.3 Gy. It follows that low-dose high-LET human cancer risks scaled from low-LET human risks using RBE may be correspondingly overestimated.

Predictions of space radiation fatality risk for exploration missions

In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. population. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits.

Liver and kidney procurement quality in the Netherlands — An analysis of quality forms

In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. population. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits.

SU-E-T-354: Peak Temperature Ratio of TLD Glow Curves to Investigate the Spatial Dependence of LET in a Clinical Proton Beam

Purpose: Use the ratio of the two high temperature peaks (HTR) in TLD 700 glow curves to investigate spatial dependence of the linear energy transfer (LET) in proton beams. Studies show that the relative biological effectiveness (RBE) depends upon the physical dose as well as its spatial distribution. Although proton therapy uses a spatially invariant RBE of 1.1, studies suggest that the RBE increases in the distal edge of a spread out Bragg peak (SOBP) due to the increased LET. Methods: Glow curve studies in TLD 700 show that the 280 C temperature peak is more sensitive to LET radiation than the 210 C temperature peak. Therefore, the areas under the individual temperature peaks for TLDs irradiated in a proton beam normalized to the peak ratio for 6 MV photons are used to determine the HTR to obtain information on its LET. TLD 700 chips with dimensions 0.31×0.31×0.038 cc are irradiated with 90 MeV protons at varying depths in a specially designed blue wax phantom to investigate LET spatial dependence. Results: Five TLDs were placed at five different depths of the percent depth dose curve (PDD) of range 16.2 cm: center of the SOPB and approximately at the 99% distalmore » edge, 90%, 75% and 25% of the PDD, respectively. HTR was 1.3 at the center of the SOBP and varied from 2.2 to 3.9 which can be related to an LET variation from 0.5 to 18 KeV/μ via calibration with radiation beams of varying LET. Conclusion: HTR data show a spatially invariant LET slightly greater than the 6 MV radiations in the SOBP, but a rapidly increasing LET at the end of the proton range. These results indicate a spatial variation in RBE with potential treatment consequences when selecting treatment margins to minimize the uncertainties in proton RBE.« less

Systematic analysis of RBE and related quantities using a database of cell survival experiments with ion beam irradiation

For tumor therapy with light ions and for experimental aspects in particle radiobiology the relative biological effectiveness (RBE) is an important quantity to describe the increased effectiveness of particle radiation. By establishing and analysing a database of ion and photon cell survival data, some remarkable properties of RBE-related quantities were observed. The database consists of 855 in vitro cell survival experiments after ion and photon irradiation. The experiments comprise curves obtained in different labs, using different ion species, different irradiation modalities, the whole range of accessible energies and linear energy transfers (LETs) and various cell types. Each survival curve has been parameterized using the linear-quadratic (LQ) model. The photon parameters, α and β, appear to be slightly anti-correlated, which might point toward an underlying biological mechanism. The RBE values derived from the survival curves support the known dependence of RBE on LET, on particle species and dose. A positive correlation of RBE with the ratio α/β of the photon LQ parameters is found at low doses, which unexpectedly changes to a negative correlation at high doses. Furthermore, we investigated the course of the β coefficient of the LQ model with increasing LET, finding typically a slight initial increase and a final falloff to zero. The observed fluctuations in RBE values of comparable experiments resemble overall RBE uncertainties, which is of relevance for treatment planning. The database can also be used for extensive testing of RBE models. We thus compare simulations with the local effect model to achieve this goal.

The potential impact of relative biological effectiveness uncertainty on charged particle treatment prescriptions

There continues to be uncertainty regarding the relative biological effectiveness (RBE) values that should be used in charged particle radiotherapy (CPT) prescriptions using protons and heavier ions. This uncertainty could potentially offset the physical dose advantage gained by exploiting the Bragg peak effect and it needs to be clearly understood by clinicians and physicists. This paper introduces a combined radiobiological and physical sparing factor (S). This factor includes the ratio of the most relevant physical doses in tumour and normal tissues in combination with their respective RBE values and can be extended to contain the uncertainties in RBE. S factors can be used to study, in a simplified way for tentative modelling, those clinical situations in which high-linear energy transfer (LET) irradiations are likely to prove preferable over their low-LET counterparts for a matched tumour iso-effect. In cases where CPT achieves an excellent degree of normal tissue sparing, the radiobiological factors become less important and any uncertainties in the tumour and healthy tissue RBE values are correspondingly less problematic. When less normal tissue sparing can be achieved, however, the RBE uncertainties assume greater relevance and will affect the reliability of the dose-prescription methodology. More research is required to provide accurate RBE estimation, focusing attention on the associated statistical uncertainties and potential differences in RBE between different tissue types.

Accuracy of RBE: experimental and theoretical considerations

The concept of the relative biological effectiveness (RBE) is essential for treatment planning in carbon ion therapy and for understanding the biological effects of high-LET radiation. As this quantity depends on many factors, both its experimental determination and the assessment of its uncertainty are not trivial. For the limiting case of zero dose, where the RBE takes its maximum value RBE(alpha), we present in this article a simple empirical-based approach to estimate its uncertainty. A Gaussian error calculus is applied to equally take into account both uncertainties from experiments with high- and low-LET radiation. From a theoretical point of view, we then infer, using a simple Monte Carlo model, the distribution of RBE(alpha) values. This illustrates why the conventional error propagation approach is inappropriate in some cases. In these cases, likewise also the error estimates have to be obtained with a more sophisticated approach. Uncertainties of RBE, visualized by error bars, are of importance for treatment planning and also for setting up a precision goal for predicting biophysical models such as the local effect model.

Uncertainty Analysis of Relative Biological Effectiveness of Alpha-Radiation for Human Lung Exposure

Assessment of relative biological effectiveness (RBE) for α radiation in the cases of inhalation of radon progeny and incorporation of plutonium in lung is based on simulation of lung cancer radiation risk for alpha and external reference types of radiation. Specific radiation risk models developed on the results of direct epidemiological studies are used for simulation. These include published risk models for nuclear workers of the Mayak facilities in the former Soviet Union exposed to incorporated plutonium (Kreisheimer et al., 2003; Gilbert et al., 2004) and underground miners exposed to radon progenies (BEIR VI, 1999). Additionally, a lung cancer risk model is developed for a case of population indoor radon exposure. Lung cancer risk related to external exposure is estimated using the risk model developed for the analyses of Japanese atomic bomb survivors (Preston et al., 2003). Uncertainties of risk models parameters are considered and the uncertainties of RBE are estimated using the results of lifetime lung cancer risk simulation, which is done implementing a Monte Carlo approach. Estimated median value of RBE in case of indoor radon exposure is 1.5 with 90% range 0.4–7. In the case of the two models developed by BEIR VI for lung cancer risk due to radon exposure in underground miners, the median values of RBE are 2.1 and 4.4 with 90% ranges 0.3–17 and 0.7–45, respectively. The two different models for lung cancer risk related to plutonium exposure resulted in close estimates of RBE: median value of 12 and 13 with 90% range 4–104 and 4–136, respectively. This report makes use of data obtained from the Radiation Effects Research Foundation (RERF), Hiroshima and Nagasaki, Japan. RERF is a private, nonprofit foundation funded by the Japanese Ministry of Health, Labor, and Welfare and the U.S. Department of Energy, the latter through the National Academy of Sciences. The conclusions in this report are those of the authors and do not necessarily reflect the scientific judgment of RERF or its funding agencies.