'Survival': a simulation toolkit introducing a modular approach for radiobiological evaluations in ion beam therapy

Integrating microdosimetricin vitroRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool

Objective.In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.Approach.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

Computational approaches in the estimation of radiobiological damage for human-malignant cells irradiated with clinical proton and carbon beams

PURPOSE

The use of Monte Carlo (MC) simulations capable of reproducing radiobiological effects of ionising radiation on human cell lines is of great importance, especially for cases involving protons and heavier ion beams. In the latter, huge uncertainties can arise mainly related to the effects of the secondary particles produced in the beam-tissue interaction. This paper reports on a detailed MC study performed using Geant4-based approach on three cancer cell lines, the HTB-177, CRL-5876 and MCF-7, that were previously irradiated with therapeutic proton and carbon ion beams.

METHODS

A Geant4-based approach used jointly with analytical calculations has been developed to provide a more realistic estimation of the radiobiological damage produced by proton and carbon beams in tissues, reproducing available data obtained from in vitro cell irradiations. The MC "Hadrontherapy" Geant4 application and the Local Effect Model: LEM I, LEM II and LEM III coupled with the different numerical approaches: RapidRusso (RR) and RapidScholz (RS) were used in the study.

RESULTS

Experimental survival curves are compared with those evaluated using the highlighted Geant4 MC-based approach via chi-square statistical analysis, for the combinations of radiobiological models and numerical approaches, as outlined above.

CONCLUSION

This study has presented a comparison of the survival data from MC simulations to experimental survival data for three cancer cell lines. An overall best level of agreement was obtained for the HTB-177 cells.

In vitro modified microdosimetric kinetic model-based predictions for B14-150 cells survival in 450 MeV/u carbon ion beam with aluminum ridge filter for biologically optimized spread-out Bragg peak

The relative biological efficiency of particle irradiation could be predicted with a wide variety of radiobiological models for various end-points. We validate the forecast of modified Microdosimetric Kinetic Model in vitro using combined data of reference Co-60 radiation and carbon ion plateau data for specific cell line to optimize the survival function in spread-out Bragg Peak obtained with an especially designed ridge filter. We used Geant4 Monte-Carlo software to simulate the fragment contribution along Bragg curve inside water phantom, open-source toolkit Survival to predict the expected linear-quadratic model parameters for each fragment, and in-house software to form the total survival curve in spread-out Bragg Peak. The irradiation was performed at U-70 synchrotron with an especially designed Aluminum ridge filter under the control of PTW and in-house ionization chambers. The cell clonogenic assay was conducted with the B14-150 cell line. The data analysis was accomplished using scipy and CERN ROOT. The clonogenic assay represents the survival in spread-out Bragg Peak at different points and qualitatively follows the modeled survival curve very well. The quantitative difference is within 3σ, and the deviation might be explained by the uncertainties of physical modeling using Monte-Carlo methods. Overall, the obtained results are promising for further usage in radiobiological studies or carbon ion radiotherapy. Shaping the survival curve in the region of interest (i.e., spread-out Bragg Peak) is a comprehensive task that requires high-performance computing approaches. Nevertheless, the method's potential application is related to the development of next-generation treatment planning systems for ion beams. This can open a wide range of improvements in patient treatment outcome, provide new optimized fractionation regimes or optimized dose delivery schemes, and serve as an entrance point to the translational science approach.

Update of the particle irradiation data ensemble (PIDE) for cell survival

The particle irradiation data ensemble (PIDE) is the largest database of cell survival data measured after exposure to ion beams and photon reference radiation. We report here on the updated version of the PIDE database and demonstrate how to investigate generic properties of radiation dose response using these sets of raw data. The database now contains information of over 1100 pairs of photon and ion dose response curves. It provides the originally published raw data of cell survival in addition to given linear quadratic (LQ) model parameters. If available, the raw data were used to derive LQ model parameters in the same way for all experiments. To demonstrate the extent of the database and the variability among experiments we focus on the dose response curves after ion and photon radiation separately in a first step. Furthermore, we discuss the capability and the limitations of the database for analyzing properties of low and high linear energy transfer (LET) radiation response based on multiple experiments. PIDE is freely available to the research community under www.gsi.de/bio-pide.

A systematic review on the usage of averaged LET in radiation biology for particle therapy

Linear Energy Transfer (LET) is widely used to express the radiation quality of ion beams, when characterizing the biological effectiveness. However, averaged LET may be defined in multiple ways, and the chosen definition may impact the resulting reported value. We review averaged LET definitions found in the literature, and quantify which impact using these various definitions have for different reference setups. We recorded the averaged LET definitions used in 354 publications quantifying the relative biological effectiveness (RBE) of hadronic beams, and investigated how these various definitions impact the reported averaged LET using a Monte Carlo particle transport code. We find that the kind of averaged LET being applied is, generally, poorly defined. Some definitions of averaged LET may influence the reported averaged LET values up to an order of magnitude. For publications involving protons, most applied dose averaged LET when reporting RBE. The absence of what target medium is used and what secondary particles are included further contributes to an ill-defined averaged LET. We also found evidence of inconsistent usage of averaged LET definitions when deriving LET-based RBE models. To conclude, due to commonly ill-defined averaged LET and to the inherent problems of LET-based RBE models, averaged LET may only be used as a coarse indicator of radiation quality. We propose a more rigorous way of reporting LET values, and suggest that ideally the entire particle fluence spectra should be recorded and provided for future RBE studies, from which any type of averaged LET (or other quantities) may be inferred.

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

PURPOSE

The Geant4 Monte Carlo simulation toolkit was used to reproduce radiobiological parameters measured by irradiating three different cancerous cell lines with monochromatic and clinical proton beams.

METHODS

The experimental set-up adopted for irradiations was fully simulated with a dedicated open-source Geant4 application. Cells survival fractions was calculated coupling the Geant4 simulations with two analytical radiobiological models: one based on the LEM (Local Effect Model) approach and the other on a semi-empirical parameterisation. Results was evaluated and compared with experimental data.

RESULTS AND CONCLUSIONS

The results demonstrated the Geant4 ability to reproduce radiobiological quantities for different cell lines.