RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation.

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and ß coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/ß values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not ß. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and ß coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Dose and DNA damage modelling of diffusing alpha-emitters radiation therapy using Geant4.

PURPOSE: Diffusing alpha-emitters radiation therapy (DaRT) is a brachytherapy technique using α-particles to treat solid tumours. The high linear energy transfer (LET) and short range of α-particles make them good candidates for the targeted treatment of cancer. Treatment planning of DaRT requires a good understanding of the dose from α-particles and the other particles released in the 224Ra decay chain. METHODS: The Geant4 Monte Carlo toolkit has been used to simulate a DaRT seed to better understand the dose contribution from all particles and simulate the DNA damage due to this treatment. RESULTS: Close to the seed α-particles deliver the majority of dose, however at radial distances greater than 4 mm, the contribution of ß-particles is greater. The RBE has been estimated as a function of number of double strand breaks (DSBs) and complex DSBs. A maximum seed spacing of 5.5 mm and 6.5 mm was found to deliver at least 20 Gy RBE weighted dose between the seeds for RBEDSB and RBEcDSB respectively. CONCLUSIONS: The DNA damage changes with radial distance from the seed and has been found to become less complex with distance, which is potentially easier for the cell to repair. Close to the seed α-particles contribute the majority of dose, however the contribution from other particles cannot be neglected and may influence the choice of seed spacing.

Monte carlo simulation study on the dose and dose-averaged linear energy transfer distributions in carbon ion radiotherapy.

Dose-averaged linear energy transfer (LETd) is conventionally evaluated from the relative biological effectiveness (RBE)-LETd fitted function used in the treatment planning system. In this study, we calculated the physical doses and their linear energy transfer (LET) distributions for patterns of typical CIRT beams using Monte Carlo (MC) simulation. The LETd was then deduced from the MC simulation and compared with that obtained from the conventional method. The two types of LETd agreed well with each other, except around the distal end of the spread-out Bragg peak. Furthermore, an MC simulation was conducted with the material composition of water and realistic materials. The profiles of physical dose and LETd were in good agreement for both techniques. These results indicate that the previous studies to analyze the minimum LETd in CIRT cases are valid for practical situations, and the material composition conversion to water little affects the dose distribution in the irradiation field.

Radium deposition in human brain tissue: A Geant4-DNA Monte Carlo toolkit study.

NASA has encouraged studies on 226Ra deposition in the human brain to investigate the effects of exposure to alpha particles with high linear energy transfer, which could mimic some of the exposure astronauts face during space travel. However, this approach was criticized, noting that radium is a bone-seeker and accumulates in the skull, which means that the radiation dose from alpha particles emitted by 226Ra would be heavily concentrated in areas close to cranial bones rather than uniformly distributed throughout the brain. In the high background radiation areas of Ramsar, Iran, extremely high levels of 226Ra in soil contribute to a large proportion of the inhabitants' radiation exposure. A prospective study on Ramsar residents with a calcium-rich diet was conducted to improve the dose uniformity due to 226Ra throughout the cerebral and cerebellar parenchyma. The study found that exposure of the human brain to alpha particles did not significantly affect working memory but was significantly associated with increased reaction times. This finding is crucial because astronauts on deep space missions may face similar cognitive impairments due to exposure to high charge and energy particles. The current study was aimed to evaluate the validity of the terrestrial model using the Geant4 Monte Carlo toolkit to simulate the interactions of alpha particles and representative cosmic ray particles, acknowledging that these radiation types are only a subset of the complete space radiation environment.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy.

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETddistributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETddistribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETdmap generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETdmaps generated by the proposed method are 0.096 ± 0.019 keVµm-1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keVµm-1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETdmaps from dose maps and has the potential to improve proton therapy planning by providing accurate LETdinformation.

Spectral composition of secondary electrons based on the Kiefer-Straaten ion track structure model.

The major part of energy deposition of ionizing radiation is caused by secondary electrons, independent of the primary radiation type. However, their spatial concentration and their spectral properties strongly depend on the primary radiation type and finally determine the pattern of molecular damage e.g. to biological targets as the DNA, and thus the final effect of the radiation exposure. To describe the physical and to predict the biological consequences of charged ion irradiation, amorphous track structure approaches have proven to be pragmatic and helpful. There, the local dose deposition in the ion track is equated by considering the emission and slowing down of the secondary electrons from the primary particle track. In the present work we exploit the model of Kiefer and Straaten and derive the spectral composition of secondary electrons as function of the distance to the track center. The spectral composition indicates differences to spectra of low linear energy transfer (LET) photon radiation, which we confirm by a comparison with Monte Carlo studies. We demonstrate that the amorphous track structure approach provides a simple tool for evaluating the spectral electron properties within the track structure. Predictions of the LET of electrons across the track structure as well as the electronic dose build-up effect are derived. Implications for biological effects and corresponding predicting models based on amorphous track structure are discussed.

Linear energy transfer (LET) distribution outside small radiotherapy field edges produced by 6 MV X-rays.

In modern radiotherapy with photons, the absorbed dose outside the radiation field is generally investigated. But it is well known that the biological damage depends not only on the absorbed dose but also on LET. This work investigated the dose-average LET (LΔ,D) outside several small radiotherapy fields to provide information that can help for better evaluating the biological effect in organs at risk close to the tumour volume. The electron fluences produced in liquid water by a 6 MV X-rays Varian iX linac were calculated using the EGSnrc Monte Carlo code. With the electron spectra, LΔ,D calculations were made for eight open small square fields and the reference field at water depths of 0.15 cm, 1.35 cm, 9.85 cm and 19.85 cm and several off-axis distances. The variation of LΔ,D from the centre of the beam to 2 cm outside the field's edge depends on the field size and water depth. Using radiobiological data reported in the literature for chromosomal aberrations as an endpoint for the induction of dicentrics determined in Human Lymphocytes, we estimated the maximum low-dose relative biological effectiveness, (RBEM) finding an increase of up to 100% from the centre of the beam to 2 cm from the field's edge.

Energy imparted and ionisation yield at the nanometre scale: results for extended beams.

The metrological problem of interpreting ionisation-based micro- and nanodosimetric measurements in terms of quantities proportional to energy imparted becomes particularly relevant when the sensitive volume (SV) size is in the nanometre range. At these scales, a constant W-value cannot be assumed, and the stochastics of the energy transfer per single collision could play a more important role. This problem was recently analysed by our group by means of track-structure Monte Carlo simulations with the Geant4-DNA code, finding a strong correlation between the energy imparted and ionisation yield also for SV diameters of 1 nm. As the previous study was limited to primary beams of radius zero crossing the sensitive sphere along its diameter, it is the aim of the present work to extend the analysis to beams with a radius larger than the dimensions of the SV, to better assess the role played by secondary electrons.

A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose.

Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS.Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%-20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models.Main results.The PBPs ranges were within ±0.3mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient.Significance. Aprocedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.

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.

A method to implement inter-track interactions in Monte Carlo simulations with TOPAS-nBio and their influence on simulated radical yields following water radiolysis.

Objective.In FLASH radiotherapy (dose rates ≥40 Gy s-1), a reduced normal tissue toxicity has been observed, while maintaining the same tumor control compared to conventional radiotherapy (dose rates ≤0.03 Gy s-1). This protecting effect could not be fully explained yet. One assumption is that interactions between the chemicals of different primary ionizing particles, so-called inter-track interactions, trigger this outcome. In this work, we included inter-track interactions in Monte Carlo track structure simulations and investigated the yield of chemicals (G-value) produced by ionizing particles.Approach.For the simulations, we used the Monte Carlo toolkit TOPAS, in which inter-track interactions cannot be implemented without further effort. Thus, we developed a method enabling the simultaneous simulation ofNoriginal histories in one event allowing chemical species to interact with each other. To investigate the effect of inter-track interactions we analyzed theG-value of different chemicals using various radiation sources. We used electrons with an energy of 60 eV in different spatial arrangements as well as a 10 MeV and 100 MeV proton source. For electrons we setNbetween 1 and 60, for protons between 1 and 100.Main results.In all simulations, the totalG-value decreases with increasingN. In detail, theG-value for•OH , H3O and eaqdecreases with increasingN, whereas theG-value of OH-, H2O2and H2increases slightly. The reason is that with increasingN, the concentration of chemical radicals increases allowing for more chemical reactions between the radicals resulting in a change of the dynamics of the chemical stage.Significance.Inter-track interactions resulting in a variation of the yield of chemical species, may be a factor explaining the FLASH effect. To verify this hypothesis, further simulations are necessary in order to evaluate the impact of varyingG-values on the yield of DNA damages.

Geant4-DNA simulation of human cancer cells irradiation with helium ion beams.

PURPOSE: This study aimed to develop a computational environment for the accurate simulation of human cancer cell irradiation using Geant4-DNA. New cell geometrical models were developed and irradiated by alpha particle beams to induce DNA damage. The proposed approach may help further investigation of the benefits of external alpha irradiation therapy. METHODS: The Geant4-DNA Monte Carlo (MC) toolkit allows the simulation of cancer cell geometries that can be combined with accurate modelling of physical, physicochemical and chemical stages of liquid water irradiation, including radiolytic processes. Geant4-DNA is used to calculate direct and non-direct DNA damage yields, such as single and double strand breaks, produced by the deposition of energy or by the interaction of DNA with free radicals. RESULTS: In this study, the "molecularDNA" example application of Geant4-DNA was used to quantify early DNA damage in human cancer cells upon irradiation with alpha particle beams, as a function of linear energy transfer (LET). The MC simulation results are compared to experimental data, as well as previously published simulation data. The simulation results agree well with the experimental data on DSB yields in the lower LET range, while the experimental data on DSB yields are lower than the results obtained with the "molecularDNA" example in the higher LET range. CONCLUSION: This study explored and demonstrated the possibilities of the Geant4-DNA toolkit together with the "molecularDNA" example to simulate the helium beam irradiation of cancer cell lines, to quantify the early DNA damage, or even the following DNA damage response.

An integrated Monte Carlo track-structure simulation framework for modeling inter and intra-track effects on homogenous chemistry.

Objective. The TOPAS-nBio Monte Carlo track structure simulation code, a wrapper of Geant4-DNA, was extended for its use in pulsed and longtime homogeneous chemistry simulations using the Gillespie algorithm.Approach. Three different tests were used to assess the reliability of the implementation and its ability to accurately reproduce published experimental results: (1) a simple model with a known analytical solution, (2) the temporal evolution of chemical yields during the homogeneous chemistry stage, and (3) radiolysis simulations conducted in pure water with dissolved oxygen at concentrations ranging from 10µM to 1 mM with [H2O2] yields calculated for 100 MeV protons at conventional and FLASH dose rates of 0.286 Gy s-1and 500 Gy s-1, respectively. Simulated chemical yield results were compared closely with data calculated using the Kinetiscope software which also employs the Gillespie algorithm.Main results. Validation results in the third test agreed with experimental data of similar dose rates and oxygen concentrations within one standard deviation, with a maximum of 1% difference for both conventional and FLASH dose rates. In conclusion, the new implementation of TOPAS-nBio for the homogeneous long time chemistry simulation was capable of recreating the chemical evolution of the reactive intermediates that follow water radiolysis.Significance. Thus, TOPAS-nBio provides a reliable all-in-one chemistry simulation of the physical, physico-chemical, non-homogeneous, and homogeneous chemistry and could be of use for the study of FLASH dose rate effects on radiation chemistry.

GEANT4-DNA simulation of temperature-dependent and pH-dependent yields of chemical radiolytic species.

Objective.GEANT4-DNA can simulate radiation chemical yield (G-value) for radiolytic species such as the hydrated electron (eaq-) with the independent reaction times (IRT) method, however, only at room temperature and neutral pH. This work aims to modify the GEANT4-DNA source code to enable the calculation ofG-values for radiolytic species at different temperatures and pH values.Approach.In the GEANT4-DNA source code, values of chemical parameters such as reaction rate constant, diffusion coefficient, Onsager radius, and water density were replaced by corresponding temperature-dependent polynomials. The initial concentration of hydrogen ion (H+)/hydronium ion (H3O+) was scaled for a desired pH using the relationship pH = -log10[H+]. To validate our modifications, two sets of simulations were performed. (A) A water cube with 1.0 km sides and a pH of 7 was irradiated with an isotropic electron source of 1 MeV. The end time was 1µs. The temperatures varied from 25 °C to 150 °C. (B) The same setup as (A) was used, however, the temperature was set to 25 °C while the pH varied from 5 to 9. The results were compared with published experimental and simulated work.Main results.The IRT method in GEANT4-DNA was successfully modified to simulateG-values for radiolytic species at different temperatures and pH values. Our temperature-dependent results agreed with experimental data within 0.64%-9.79%, and with simulated data within 3.52%-12.47%. The pH-dependent results agreed well with experimental data within 0.52% to 3.19% except at a pH of 5 (15.99%) and with simulated data within 4.40%-5.53%. The uncertainties were below ±0.20%. Overall our results agreed better with experimental than simulation data.Significance.Modifications in the GEANT4-DNA code enabled the calculation ofG-values for radiolytic species at different temperatures and pH values.

Single proton LET characterization with the Timepix detector and artificial intelligence for advanced proton therapy treatment planning.

Objective.Protons have advantageous dose distributions and are increasingly used in cancer therapy. At the depth of the Bragg peak range, protons produce a mixed radiation field consisting of low- and high-linear energy transfer (LET) components, the latter of which is characterized by an increased ionization density on the microscopic scale associated with increased biological effectiveness. Prediction of the yield and LET of primary and secondary charged particles at a certain depth in the patient is performed by Monte Carlo simulations but is difficult to verify experimentally.Approach.Here, the results of measurements performed with Timepix detector in the mixed radiation field produced by a therapeutic proton beam in water are presented and compared to Monte Carlo simulations. The unique capability of the detector to perform high-resolution single particle tracking and identification enhanced by artificial intelligence allowed to resolve the particle type and measure the deposited energy of each particle comprising the mixed radiation field. Based on the collected data, biologically important physics parameters, the LET of single protons and dose-averaged LET, were computed.Main results.An accuracy over 95% was achieved for proton recognition with a developed neural network model. For recognized protons, the measured LET spectra generally agree with the results of Monte Carlo simulations. The mean difference between dose-averaged LET values obtained from measurements and simulations is 17%. We observed a broad spectrum of LET values ranging from a fraction of keVµm-1to about 10 keVµm-1for most of the measurements performed in the mixed radiation fields.Significance.It has been demonstrated that the introduced measurement method provides experimental data for validation of LETDor LET spectra in any treatment planning system. The simplicity and accessibility of the presented methodology make it easy to be translated into a clinical routine in any proton therapy facility.

Modeling of dose and linear energy transfer homogeneity in cell nuclei exposed to alpha particles under various setup conditions.

PURPOSE: Different alpha exposure setups are often used to study the relation between biological responses and LET. This study aimed to estimate the dose heterogeneity and uncertainty in four exposure setups using Geant4 and PARTRAC codes. The importance of the irradiation system characteristics was shown in the context of reporting experimental results, especially in radiobiological studies at the molecular level. MATERIALS AND METHODS: Geant4 was used to estimate the dose distributions in cells grown on a disk exposed to alpha particles penetrating from above and below. The latter setup was simulated without and with a collimator. PARTRAC was used for the validation of Geant4 simulations based on distributions of the number of alpha particles penetrating a round nucleus and the deposited energy. RESULTS: The LET distributions obtained for simulated setups excluding the collimator were wide and non-Gaussian. Using a collimator resulted in a Gaussian LET distribution, but strongly reduced dose rate and dose homogeneity. Comparison between PARTRAC and Geant4 calculations for the cell nucleus exposed to alpha radiation showed an excellent agreement. CONCLUSIONS: The interpretation of results from radiobiological experiments with alpha particles should always cover the characteristics of the experimental setup, which can be done precisely with computational methods.

Effects of incoming particle energy and cluster size on the G-value of hydrated electrons.

In hydrated electron (e-aq) dosimetry, absorbed radiation dose to water is measured by monitoring the concentration of radiation-induced e-aq. However, to obtain accurate dose, the radiation chemical yield of e-aq, G(e-aq), is needed for the radiation quality/setup under investigation. The aim of this study was to investigate the time-evolution of the G-values for the main generated reactive species during water radiolysis using GEANT4-DNA. The effects of cluster size and linear energy transfer (LET) on G(e-aq) were examined. Validity of GEANT4-DNA for calculation of G(e-aq) for clinically relevant energies was studied. Three scenarios were investigated with different phantom sizes and incoming electron energies (1 keV to 1 MeV). The time evolution of G(e-aq) was in good agreement with published data and did not change with decreasing phantom size. The time-evolution of the G-values increases with increasing LET for all radiolytic species. The particle tracks formed with high-energy electrons are separated and the resulting reactive species develop independently in time. With decreasing energy, the mean separation distance between reactive species decreases. The particle tracks might not initially overlap but will overlap shortly thereafter due to diffusion of reactive species, increasing the probability of e-aq recombination with other species. This also explains the decrease of G(e-aq) with cluster size and LET. Finally, if all factors are kept constant, as the incoming electron energy increases to clinically relevant energies, G(e-aq) remains similar to its value at 1 MeV, hence GEANT4-DNA can be used for clinically relevant energies.

Proposing a Clinical Model for RBE Based on Proton Track-End Counts.

PURPOSE: In proton therapy, the clinical application of linear energy transfer (LET) optimization remains contentious, in part because of challenges associated with the definition and calculation of LET and its exact relationship with relative biological effectiveness (RBE) because of large variation in experimental in vitro data. This has raised interest in other metrics with favorable properties for biological optimization, such as the number of proton track ends in a voxel. In this work, we propose a novel model for clinical calculations of RBE, based on proton track end counts. METHODS AND MATERIALS: We developed an effective dose concept to translate between the total proton track-end count per unit mass in a voxel and a proton RBE value. Dose, track end, and dose-averaged LET (LETd) distributions were simulated using Monte Carlo models for a series of water phantoms, in vitro radiobiological studies, and patient treatment plans. We evaluated the correlation between track ends and regions of elevated biological effectiveness in comparison to LETd-based models of RBE. RESULTS: Track ends were found to correlate with biological effects in in vitro experiments with an accuracy comparable to LETd. In patient simulations, our track end model identified the same biological hotspots as predicted by LETd-based radiobiological models of proton RBE. CONCLUSIONS: These results suggest that, for clinical optimization and evaluation, an RBE model based on proton track end counts may match LETd-based models in terms of information provided while also offering superior statistical properties.

Technical note: In silico benchmarking of the linear energy transfer-based functionalities for carbon ion beams in a commercial treatment planning system.

BACKGROUND: The increasing number of studies dealing with linear energy transfer (LET)-based evaluation and optimization in the field of carbon ion radiotherapy (CIRT) indicates the rising demand for LET implementation in commercial treatment planning systems (TPS). Benchmarking studies could play a key role in detecting (and thus preventing) computation errors prior implementing such functionalities in a TPS. PURPOSE: This in silico study was conducted to benchmark the following two LET-related functionalities in a commercial TPS against Monte Carlo simulations: (1) dose averaged LET (LETd ) scoring and (2) physical dose filtration based on LET for future LET-based treatment plan evaluation and optimization studies. METHODS: The LETd scoring and LET-based dose filtering (in which the deposited dose can be separated into the dose below and above the user specified LET threshold) functionalities for carbon ions in the research version RayStation (RS) 9A-IonPG TPS (RaySearch Laboratories, Sweden) were benchmarked against GATE/Geant4 simulations. Pristine Bragg peaks (BPs) and cuboid targets, positioned at different depths in a homogeneous water phantom and a setup with heterogeneity were used for this study. RESULTS: For all setups (homogeneous and heterogeneous), the mean absolute (and relative) LETd difference was less than 1 keV/ µ $\umu$ m (3.5%) in the plateau and target and less than 2 keV/ µ $\umu$ m (8.3%) in the fragmentation tail. The maximum local differences were 4 and 6 keV/ µ $\umu$ m, respectively. The mean absolute (and relative) physical dose differences for both low- and high-LET doses were less than 1 cGy (1.5%) in the plateau, target and tail with a maximum absolute difference of 2 cGy. CONCLUSIONS: No computation error was found in the tested functionalities except for LETd in lateral direction outside the target, showing the limitation of the implemented monochrome model in the tested TPS version.

Calculating dose-averaged linear energy transfer in an analytical treatment planning system for carbon-ion radiotherapy.

BACKGROUND: Compelling evidence shows the association between the relative biological effectiveness (RBE) of carbon-ion radiotherapy (CIRT) and the dose averaged linear energy transfer (LETd). However, the ability to calculate the LETd in commercially available treatment planning systems (TPS) is lacking. PURPOSE: This study aims to develop a method of calculating the LETd of CIRT plans that could be robustly carried out in RayStation (V10B, Raysearch, Sweden). METHODS: The calculation used the fragment spectra in RayStation for the CIRT treatment planning. The dose-weighted averaging procedure was supported by the microdosimetric kinetic model (MKM). The MKM-based pencil beam dose engine (PBA, v4.2) for calculating RBE-weighted doses was reformulated to become a LET-weighted calculating engine. A separate module was then configured to inversely calculate the LETd from the absorbed dose of a plan and the associated fragment spectra. In this study, the ion and energy-specific LET table in the LETd module was further matched with the values decoded from the baseline data of the Syngo TPS (V13C, Siemens, Germany). The LETd distributions of several monoenergetic and modulated beams were calculated and validated against the values derived from the Syngo TPS and the published data. RESULTS: The differences in LETds of the monoenergetic beams between the new method and the traditional method were within 3% in the entrance and Bragg-peak regions. However, a larger difference was observed in the distal region. The results of the modulated beams were in good agreement with the works from the published literature. CONCLUSIONS: The method presented herein reformulates the MKM dose engine in the RayStation TPS to inversely calculate LETds. The robustness and accuracy were demonstrated.