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

Modelling radiobiology

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy-from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed.

In vitro and in silico study of biological effects on cancer cells in the presence of metallic materials during radiotherapy

X-ray therapy aims to eliminate tumours while minimizing side effects. Intense mucositis is sometimes induced when irradiating the oral cavity with a dental metal crown (DMC). However, the underlying mechanisms of such inducing radiosensitization by DMC remain uncertain. This study explored the radiosensitizing mechanisms around DMCs in an interdisciplinary approach with cell experiments and Monte Carlo simulation with the PHITS code. Clonogenic survival and nuclear 53BP1 foci of a cell line derived from cervical cancer cells (HeLa cells) were measured post-irradiation with therapeutic X-rays near high-Z materials such as Pb or Au plates, and the experimental sensitizer enhancement ratio (SER) was obtained. Meanwhile, the dose enhancement ratio (DER) and relative biological effectiveness for DNA damage yields were calculated using the PHITS code, by considering the corresponding experimental condition. The experiments show the experimental SER values for cell survival and 53BP1 foci near metals are 1.2-1.4, which agrees well with the calculated DER values. These suggest that the radiosensitizing effects near metal are predominantly attributed to the dose increase. In addition, as a preclinical evaluation, the spatial distributions of DER near DMC are calculated using Computed Tomography Digital Imaging and Communications in Medicine (CT-DICOM) data and a simple tooth model. As a result, the DER values evaluated using the CT-DICOM data were lower than those from a simple tooth model. These findings highlight the challenge of evaluating radiosensitizing effects near DMCs using Digital Imaging and Communications in Medicine (DICOM) images due to volume-averaging effects and emphasize the need for a high-resolution (<1 mm) dose assessment method unaffected by these effects.

Prediction of Cell Survival Rate Based on Physical Characteristics of Heavy Ion Radiation

The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910-2, the local regression 9.2110-2, and the random forest 1.96 × 10-2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power.

An updated variable RBE model for proton therapy

Objective.While a constant relative biological effectiveness (RBE) of 1.1 forms the basis for clinical proton therapy, variable RBE models are increasingly being used in plan evaluation. However, there is substantial variation across RBE models, and several newin vitrodatasets have not yet been included in the existing models. In this study, an updatedin vitroproton RBE database was collected and used to examine current RBE model assumptions, and to propose an up-to-date RBE model as a tool for evaluating RBE effects in clinical settings.Approach.A proton database (471 data points) was collected from the literature, almost twice the size of the previously largest model database. Each data point included linear-quadratic model parameters and linear energy transfer (LET). Statistical analyses were performed to test the validity of commonly applied assumptions of phenomenological RBE models, and new model functions were proposed forRBEmaxandRBEmin(RBE at the lower and upper dose limits). Previously published models were refitted to the database and compared to the new model in terms of model performance and RBE estimates.Main results.The statistical analysis indicated that the intercept of theRBEmaxfunction should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept.RBEminincreased with increasing LET, while a dependency ofRBEminon the reference radiation fractionation sensitivity (α/βx) did not significantly improve model performance. Evaluating the models, the new model gave overall lowest RMSE and highest R2 score. RBE estimates in the distal part of a spread-out-Bragg-peak in water (α/βx= 2.1 Gy) were 1.24-1.51 for original models, 1.25-1.49 for refits and 1.42 for the new model.Significance.An updated RBE model based on the currently largest database among published phenomenological models was proposed. Overall, the new model showed better performance compared to refitted published RBE models.

Ionization Detail Parameters for DNA Damage Evaluation in Charged Particle Radiotherapy: Simulation Study Based on Cell Survival Database

Details of excitation and ionization acts hide a description of the biological effects of charged particle traversal through living tissue. Nanodosimetry enables the introduction of novel quantities that characterize and quantify the particle track structure while also serving as a foundation for assessing biological effects based on this quantification. This presents an opportunity to enhance the planning of charged particle radiotherapy by taking into account the ionization detail. This work uses Monte Carlo simulations with Geant4-DNA code for a wide variety of charged particles and their radiation qualities to analyze the distribution of ionization cluster sizes within nanometer-scale volumes, similar to DNA diameter. By correlating these results with biological parameters extracted from the PIDE database for the V79 cell line, a novel parameter R2 based on ionization details is proposed for the evaluation of radiation quality in terms of biological consequences, i.e., radiobiological cross section for inactivation. By incorporating the probability p of sub-lethal damage caused by a single ionization, we address limitations associated with the usually proposed nanodosimetric parameter Fk for characterizing the biological effects of radiation. We show that the new parameter R2 correlates well with radiobiological data and can be used to predict biological outcomes.

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 β 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/β 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 β. 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 β 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.

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.

Effects of cellular radioresponse on therapeutic helium-, carbon-, oxygen-, and neon-ion beams: a simulation study

Objective. Helium, oxygen, and neon ions in addition to carbon ions will be used for hypofractionated multi-ion therapy to maximize the therapeutic effectiveness of charged-particle therapy. To use new ions in cancer treatments based on the dose-fractionation protocols established in carbon-ion therapy, this study examined the cell-line-specific radioresponse to therapeutic helium-, oxygen-, and neon-ion beams within wide dose ranges.Approach. Response of cells to ions was described by the stochastic microdosimetric kinetic model. First, simulations were made for the irradiation of one-field spread-out Bragg peak beams in water with helium, carbon, oxygen, and neon ions to achieve uniform survival fractions at 37%, 10%, and 1% for human salivary gland tumor (HSG) cells, the reference cell line for the Japanese relative biological effectiveness weighted dose system, within the target region defined at depths from 90 to 150 mm. The HSG cells were then replaced by other cell lines with different radioresponses to evaluate differences in the biological dose distributions of each ion beam with respect to those of carbon-ion beams.Main results. For oxygen- and neon-ion beams, the biological dose distributions within the target region were almost equivalent to those of carbon-ion beams, differing by less than 5% in most cases. In contrast, for helium-ion beams, the biological dose distributions within the target region were largely different from those of carbon-ion beams, more than 10% in several cases.Significance.From the standpoint of tumor control evaluated by the clonogenic cell survival, this study suggests that the dose-fractionation protocols established in carbon-ion therapy could be reasonably applied to oxygen- and neon-ion beams while some modifications in dose prescription would be needed when the protocols are applied to helium-ion beams. This study bridges the gap between carbon-ion therapy and hypofractionated multi-ion therapy.

High-LET charged particles: radiobiology and application for new approaches in radiotherapy

The number of patients treated with charged-particle radiotherapy as well as the number of treatment centers is increasing worldwide, particularly regarding protons. However, high-linear energy transfer (LET) particles, mainly carbon ions, are of special interest for application in radiotherapy, as their special physical features result in high precision and hence lower toxicity, and at the same time in increased efficiency in cell inactivation in the target region, i.e., the tumor. The radiobiology of high-LET particles differs with respect to DNA damage repair, cytogenetic damage, and cell death type, and their increased LET can tackle cells' resistance to hypoxia. Recent developments and perspectives, e.g., the return of high-LET particle therapy to the US with a center planned at Mayo clinics, the application of carbon ion radiotherapy using cost-reducing cyclotrons and the application of helium is foreseen to increase the interest in this type of radiotherapy. However, further preclinical research is needed to better understand the differential radiobiological mechanisms as opposed to photon radiotherapy, which will help to guide future clinical studies for optimal exploitation of high-LET particle therapy, in particular related to new concepts and innovative approaches. Herein, we summarize the basics and recent progress in high-LET particle radiobiology with a focus on carbon ions and discuss the implications of current knowledge for charged-particle radiotherapy. We emphasize the potential of high-LET particles with respect to immunogenicity and especially their combination with immunotherapy.

The effect of fitting the reference photon dose-response on the clonogenic survival predicted with the Mayo Clinic Florida microdosimetric kinetic model in case of accelerated ions

The Mayo Clinic Florida microdosimetric kinetic model (MCF MKM) is a recently developed clonogenic survival model. Since the MCF MKM relies on novel strategies to a priori determine the cell-specific model parameters, the only experiment-specific input values are the α and ß terms of the linear-quadratic model (LQM) of clonogenic survival for the reference photon exposure. Because the two LQM terms are anti-correlated, the fitting process of the reference photon survival curve was found to significantly influence the MCF MKM calculations. This article reports this effect for two clinically relevant cell lines (human brain glioblastoma A-172, human healthy foreskin fibroblasts AG01522) and ions (1H and 12C ions).

Treatment planning of carbon ion radiotherapy for prostate cancer based on cellular experiments with PC3 human prostate cancer cells

[Purpose] Treatment plans for carbon ion radiotherapy (CIRT) in Japan are designed to uniformly deliver the prescribed clinical dose based on the radiosensitivity of human salivary gland (HSG) cells to the planning target volume (PTV). However, sensitivity to carbon beams varies between cell lines, that is, it should be checked that the clinical dose distribution based on the cell radiosensitivity of the treatment site is uniform within the PTV. [Methods] We modeled the linear energy transfer (LET) dependence of the linear-quadratic (LQ) coefficients specific to prostate cancer, which accounts for the majority of CIRT. This was achieved by irradiating prostate cancer cells (PC3) with X-rays from a 4 MV-Linac and carbon beams with different LETs of 11.1-214.3 keV/μm. By using the radiosensitivity of PC3 cells derived from cellular experiments, we reconstructed prostate-cancer-specific clinical dose distributions on patient computed tomography (CT). [Results] The LQ coefficient, α, of PC3 cells was larger than that of HSG cells at low (<50 keV/μm) LET and smaller at high (>50 keV/μm) LET, which was validated by cellular experiments performed on rectangular SOBPs. The reconstructed dose distribution on patient CT was sloped when 1 fraction incident from the one side of the patient was considered, but remained uniform from the sum of 12 fractions of the left-right opposing beams (as is used in clinical practice). [Conclusion] Our study reveals the inhomogeneity of clinical doses in single-field plans calculated using the PC3 radiosensitivity data. However, this inhomogeneity is compensated by using the combination of left-right opposing beams.

The Mayo Clinic Florida Microdosimetric Kinetic Model of Clonogenic Survival: Application to Various Repair-Competent Rodent and Human Cell Lines

The relative biological effectiveness (RBE) calculations used during the planning of ion therapy treatments are generally based on the microdosimetric kinetic model (MKM) and the local effect model (LEM). The Mayo Clinic Florida MKM (MCF MKM) was recently developed to overcome the limitations of previous MKMs in reproducing the biological data and to eliminate the need for ion-exposed in vitro data as input for the model calculations. Since we are considering to implement the MCF MKM in clinic, this article presents (a) an extensive benchmark of the MCF MKM predictions against corresponding in vitro clonogenic survival data for 4 rodent and 10 cell lines exposed to ions from ¹H to ²³⁸U, and (b) a systematic comparison with published results of the latest version of the LEM (LEM IV). Additionally, we introduce a novel approach to derive an approximate value of the MCF MKM model parameters by knowing only the animal species and the mean number of chromosomes. The overall good agreement between MCF MKM predictions and in vitro data suggests the MCF MKM can be reliably used for the RBE calculations. In most cases, a reasonable agreement was found between the MCF MKM and the LEM IV.

The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data

Objective. To develop a new model (Mayo Clinic Florida microdosimetric kinetic model, MCF MKM) capable of accurately describing the in vitro clonogenic survival at low and high linear energy transfer (LET) using single-event microdosimetric spectra in a single target. Methodology. The MCF MKM is based on the ‘post-processing average’ implementation of the non-Poisson microdosimetric kinetic model and includes a novel expression to compute the particle-specific quadratic-dependence of the cell survival with respect to dose (β of the linear-quadratic model). A new methodology to a priori calculate the mean radius of the MCF MKM subnuclear domains is also introduced. Lineal energy spectra were simulated with the Particle and Heavy Ion Transport code System (PHITS) for 1H, 4He, 12C, 20Ne, 40Ar, 56Fe, and 132Xe ions and used in combination with the MCF MKM to calculate the ion-specific LET-dependence of the relative biological effectiveness (RBE) for Chinese hamster lung fibroblasts (V79 cell line) and human salivary gland tumor cells (HSG cell line). The results were compared with in vitro data from the Particle Irradiation Data Ensemble (PIDE) and in silico results of different models. The possibility of performing experiment-specific predictions to explain the scatter in the in vitro RBE data was also investigated. Finally, a sensitivity analysis on the model parameters is also included. Main results. The RBE values predicted with the MCF MKM were found to be in good agreement with the in vitro data for all tested conditions. Though all MCF MKM model parameters were determined a priori, the accuracy of the MCF MKM was found to be comparable or superior to that of other models. The model parameters determined a priori were in good agreement with the ones obtained by fitting all available in vitro data. Significance. The MCF MKM will be considered for implementation in cancer radiotherapy treatment planning with accelerated ions.

Datasets of in vitro clonogenic assays showing low dose hyper-radiosensitivity and induced radioresistance

Low dose hyper-radiosensitivity and induced radioresistance are primarily observed in surviving fractions of cell populations exposed to ionizing radiation, plotted as the function of absorbed dose. Several biophysical models have been developed to quantitatively describe these phenomena. However, there is a lack of raw, openly available experimental data to support the development and validation of quantitative models. The aim of this study was to set up a database of experimental data from the public literature. Using Google Scholar search, 46 publications with 101 datasets on the dose-dependence of surviving fractions, with clear evidence of low dose hyper-radiosensitivity, were identified. Surviving fractions, their uncertainties, and the corresponding absorbed doses were digitized from graphs of the publications. The characteristics of the cell line and the irradiation were also recorded, along with the parameters of the linear-quadratic model and/or the induced repair model if they were provided. The database is available in STOREDB, and can be used for meta-analysis, for comparison with new experiments, and for development and validation of biophysical models.

An empirical model of proton RBE based on the linear correlation between x-ray and proton radiosensitivity

BACKGROUND

Proton relative biological effectiveness (RBE) is known to depend on physical factors of the proton beam, such as its linear energy transfer (LET), as well as on cell-line specific biological factors, such as their ability to repair DNA damage. However, in a clinical setting, proton RBE is still considered to have a fixed value of 1.1 despite the existence of several empirical models that can predict proton RBE based on how a cell's survival curve (linear-quadratic model [LQM]) parameters α and β vary with the LET of the proton beam. Part of the hesitation to incorporate variable RBE models in the clinic is due to the great noise in the biological datasets on which these models are trained, often making it unclear which model, if any, provides sufficiently accurate RBE predictions to warrant a departure from RBE = 1.1.

PURPOSE

Here, we introduce a novel model of proton RBE based on how a cell's intrinsic radiosensitivity varies with LET, rather than its LQM parameters.

METHODS AND MATERIALS

We performed clonogenic cell survival assays for eight cell lines exposed to 6 MV x-rays and 1.2, 2.6, or 9.9 keV/µm protons, and combined our measurements with published survival data (n = 397 total cell line/LET combinations). We characterized how radiosensitivity metrics of the form DSF% , (the dose required to achieve survival fraction [SF], e.g., D10% ) varied with proton LET, and calculated the Bayesian information criteria associated with different LET-dependent functions to determine which functions best described the underlying trends. This allowed us to construct a six-parameter model that predicts cells' proton survival curves based on the LET dependence of their radiosensitivity, rather than the LET dependence of the LQM parameters themselves. We compared the accuracy of our model to previously established empirical proton RBE models, and implemented our model within a clinical treatment plan evaluation workflow to demonstrate its feasibility in a clinical setting.

RESULTS

Our analyses of the trends in the data show that DSF% is linearly correlated between x-rays and protons, regardless of the choice of the survival level (e.g., D10% , D37% , or D50% are similarly correlated), and that the slope and intercept of these correlations vary with proton LET. The model we constructed based on these trends predicts proton RBE within 15%-30% at the 68.3% confidence level and offers a more accurate general description of the experimental data than previously published empirical models. In the context of a clinical treatment plan, our model generally predicted higher RBE-weighted doses than the other empirical models, with RBE-weighted doses in the distal portion of the field being up to 50.7% higher than the planned RBE-weighted doses (RBE = 1.1) to the tumor.

CONCLUSIONS

We established a new empirical proton RBE model that is more accurate than previous empirical models, and that predicts much higher RBE values in the distal edge of clinical proton beams.

4He dose- and track-averaged linear energy transfer: Monte Carlo algorithms and experimental verification

Objective. In the present hadrontherapy scenario, there is a growing interest in exploring the capabilities of different ion species other than protons and carbons. The possibility of using different ions paves the way for new radiotherapy approaches, such as the multi-ions treatment, where radiation could vary according to target volume, shape, depth and histologic characteristics of the tumor. For these reasons, in this paper, the study and understanding of biological-relevant quantities was extended for the case of 4He ion. Approach. Geant4 Monte Carlo based algorithms for dose- and track-averaged LET (Linear Energy Transfer) calculations, were validated for 4He ions and for the case of a mixed field characterised by the presence of secondary ions from both target and projectile fragmentation. The simulated dose and track averaged LETs were compared with the corresponding dose and frequency mean values of the lineal energy, y D ¯ and y ¯ F , derived from experimental microdosimetric spectra. Two microdosimetric experimental campaigns were carried out at the Italian eye proton therapy facility of the Laboratori Nazionali del Sud of Istituto Nazionale di Fisica Nucleare (INFN-LNS, Catania, I) using two different microdosimeters: the MicroPlus probe and the nano-TEPC (Tissue Equivalent Proportional Counter). Main results. A good agreement of L ¯ d Total and L ¯ t Total with y ¯ D and y ¯ T experimentally measured with both microdosimetric detectors MicroPlus and nano-TEPC in two configurations: full energy and modulated 4He ion beam, was found. Significance. The results of this study certify the use of a very effective tool for the precise calculation of LET, given by a Monte Carlo approach which has the advantage of allowing detailed simulation and tracking of nuclear interactions, even in complex clinical scenarios.

An ion-independent phenomenological relative biological effectiveness (RBE) model for proton therapy

BACKGROUND

A relative biological effectiveness (RBE) of 1.1 is used for proton therapy though clinical evidence of varying RBE was raised. Clinical studies on RBE variability have been conducted for decades for carbon radiation, which could advance the understanding of the clinical proton RBE given an ion-independent RBE model. In this work, such a model, linear and simple, using the beam quantity Q = Z²/E (Z = ion charge, E = kinetic energy per nucleon) was tested and compared to the commonly used, proton-specific and linear energy transfer (LET) based Wedenberg RBE model.

MATERIAL AND METHODS

The Wedenberg and Q models, both predicting RBE_max and RBE_min (i.e., RBE at vanishing and very high dose, respectively), are compared in terms of ion-dependence and prediction power. An experimental in-vitro data ensemble covering 115 publications for various ions was used as dataset.

RESULTS

The model parameter of the Q model was observed to be similar for different ions (in contrast to LET). The Q model was trained without any prior knowledge of proton data. For proton RBE, the differences between experimental data and corresponding predictions of the Wedenberg or the Q model were highly comparable.

CONCLUSIONS

A simple linear RBE model using Q instead of LET was proposed and tested to be able to predict proton RBE using model parameter trained based on only RBE data of other particles in a clinical proton energy range for a large in-vitro dataset. Adding (pre)clinical knowledge from carbon ion therapy may, therefore, reduce the dominating biological uncertainty in proton RBE modelling. This would translate in reduced RBE related uncertainty in proton therapy treatment planning.

On the calculation of the relative biological effectiveness of ion radiation therapy using a biological weighting function, the microdosimetric kinetic model (MKM) and subsequent corrections (non-Poisson MKM and modified MKM)

Objective. To investigate similarities and differences in the formalism, processing, and the results of relative biological effectiveness (RBE) calculations with a biological weighting function (BWF), the microdosimetric kinetic model (MKM) and subsequent modifications (non-Poisson MKM, modified MKM). This includes: (a) the extension of the V79-RBE10% BWF to model the RBE for other clonogenic survival levels; (b) a novel implementation of MKMs as weighting functions; (c) a benchmark against Chinese Hamster lung fibroblast (V79) in vitro data; (d) a study on the effect of pre- or post- processing the average biophysical quantities used for the RBE calculations; (e) a possible modification of the modified MKM parameters to improve the model accuracy at high linear energy transfer (LET). Methodology. Lineal energy spectra were simulated for two spherical targets (diameter = 0.464 or 1.0 μm) using PHITS for 1H, 4He, 12C, 20Ne, 40Ar, 56Fe and 132Xe ions. The results of the in silico calculations were compared with published in vitro data. Main results. All models appear to underestimate the RBE α of hydrogen ions. All MKMs generally overestimate the RBE50%, RBE10% and RBE1% for ions with an LET greater than ∼200 keV μm−1. This overestimation is greater for small surviving fractions and is likely due to the assumption of a radiation-independent quadratic term of clonogenic survival (ß). The overall RBE trends seem to be best described by the novel ‘post-processing average’ implementation of the non-Poisson MKM. In case of calculations with the non-Poisson MKM, pre- or post- processing the average biophysical quantities affects the computed RBE values significantly. Significance. This study presents a systematic analysis of the formalism and results of widely used microdosimetric models of clonogenic survival for ions relevant for cancer particle therapy and space radiation protection. Points for improvements were highlighted and will contribute to the development of upgraded biophysical models.

Experimental Setup for Irradiation of Cell Cultures at L2A2

Laser–plasma proton sources and their applications to preclinical research has become a very active field of research in recent years. In addition to their small dimensions as compared to classical ion accelerators, they offer the possibility to study the biological effects of ultra-short particle bunches and the correspondingly high dose rates. We report on the design of an experimental setup for the irradiation of cell cultures at the L2A2 laboratory at the University of Santiago de Compostela, making use of a 1.2 J Ti: Sapphire laser with a 10 Hz repetition rate. Our setup comprises a proton energy separator consisting of two antiparallel magnetic fields realized by a set of permanent magnets. It allows for selecting a narrow energy window around an adaptable design value of 5 MeV out of the initially broad spectrum typical for Target Normal Sheath Acceleration (TNSA). At the same time, unwanted electrons and X-rays are segregated from the protons. This part of the setup is located inside the target vessel of the L2A2 laser. A subsequent vacuum flange sealed with a thin kapton window allows for particle passage to external sample irradiation. A combination of passive detector materials and real-time monitors is applied for measurement of the deposited radiation dose. A critical point of this interdisciplinary project is the manipulation of biological samples under well-controlled, sterile conditions. Cell cultures are prepared in sealed flasks with an ultra-thin entrance window and analysed at the nearby Fundación Pública Galega Medicina Xenómica and IDIS. The first trials will be centred at the quantification of DNA double-strand breaks as a function of radiation dose.

Microdosimetric characterization of a clinical proton therapy beam: comparison between simulated lineal energy distributions in spherical water targets and experimental measurements with a silicon detector

Objective Treatment planning based on computer simulations were proposed to account for the increase in the relative biological effectiveness (RBE) of proton radiotherapy beams near to the edges of the irradiated volume. Since silicon detectors could be used to validate the results of these simulations, it is important to explore the limitations of this comparison. Approach Microdosimetric measurements with a MicroPlus Bridge V2 silicon detector (thickness = 10 µm) were performed along the Bragg peak of a clinical proton beam. The lineal energy distributions, the dose mean values, and the RBE calculated with a biological weighting function were compared with simulations with PHITS (microdosimetric target = 1 µm water sphere), and published clonogenic survival in vitro RBE data for the V79 cell line. The effect of the silicon-to-water conversion was also investigated by comparing three different methodologies (conversion based on a single value, novel bin-to-bin conversions based on SRIM and PSTAR). Main results Mainly due to differences in the microdosimetric targets, the experimental dose-mean lineal energy and RBE values at the distal edge were respectively up to 53% and 28% lower than the simulated ones. Furthermore, the methodology chosen for the silicon-to-water conversion was proven to affect the dose mean lineal energy and the RBE10 up to 32% and 11% respectively. The best methodology to compensate for this underestimation was the bin-to-bin silicon-to-water conversion based on PSTAR. Significance This work represents the first comparison between PHITS-simulated lineal energy distributions in water targets and corresponding experimental spectra measured with silicon detectors. Furthermore, the effect of the silicon-to-water conversion on the RBE was explored for the first time. The proposed methodology based on the PSTAR bin-to-bin conversion appears to provide superior results with respect to commonly used single scaling factors and is recommended for future studies.

Comprehensive comparison of local effect model IV predictions with the particle irradiation data ensemble

PURPOSE

The increased relative biological effectiveness (RBE) of ions is one of the key benefits of ion radiotherapy compared to conventional radiotherapy with photons. To account for the increased RBE of ions during the process of ion radiotherapy treatment planning, a robust model for RBE predictions is indispensable. Currently, at several ion therapy centers the local effect model I (LEM I) is applied to predict the RBE, which varies with biological and physical impacting factors. After the introduction of LEM I, several model improvements were implemented, leading to the current version, LEM IV, which is systematically tested in this study.

METHODS

As a comprehensive RBE model should give consistent results for a large variety of ion species and energies, the particle irradiation data ensemble (PIDE) is used to systematically validate the LEM IV. The database covers over 1100 photon and ion survival experiments in form of their linear-quadratic parameters for a wide range of ion types and energies. This makes the database an optimal tool to challenge the systematic dependencies of the RBE model. After appropriate filtering of the database, 571 experiments were identified and used as test data.

RESULTS

The study confirms that the LEM IV reflects the RBE systematics observed in measurements well. It is able to reproduce the dependence of RBE on the linear energy transfer (LET) as well as on the α_γ /β_γ ratio for several ion species in a wide energy range. Additionally, the systematic quantitative analysis revealed precision capabilities and limits of the model. At lower LET values, the LEM IV tends to underestimate the RBE with an increasing underestimation with increasing atomic number of the ion. At higher LET values, the LEM IV overestimates the RBE for protons or helium ions, whereas the predictions for heavier ions match experimental data well.

CONCLUSIONS

The LEM IV is able to predict general RBE characteristics for several ion species in a broad energy range. The accuracy of the predictions is reasonable considering the small number of input parameters needed by the model. The detailed quantification of possible systematic deviations, however, enables to identify not only strengths but also limitations of the model. The gained knowledge can be used to develop model adjustments to further improve the model accuracy, which is on the way.

Comparison between the results of a recently-developed biological weighting function (V79-RBE10BWF) and thein vitroclonogenic survival RBE10of other repair-competent asynchronized normoxic mammalian cell lines and ions not used for the development of the model

728 simulated microdosimetric lineal energy spectra (26 different ions between 1H and 238U, 28 energy points from 1 to 1000 MeV/n) were used in combination with a recently-developed biological weighting function (Parisi et al., 2020) and 571 published in vitro clonogenic survival curves in order to: 1) assess prediction intervals for the in silico results by deriving an empirical indication of the experimental uncertainty from the dispersion in the in vitro hamster lung fibroblast (V79) data used for the development of the biophysical model; 2) explore the possibility of modeling the relative biological effectiveness (RBE) of the 10% clonogenic survival of asynchronized normoxic repair-competent mammalian cell lines other than the one used for the development of the model (V79); 3) investigate the predictive power of the model through a comparison between in silico results and in vitro data for 10 ions not used for the development of the model. At first, different strategies for the assessment of the in silico prediction intervals were compared. The possible sources of uncertainty responsible for the dispersion in the in vitro data were also shortly reviewed. Secondly, also because of the relevant scatter in the in vitro data, no statistically-relevant differences were found between the RBE10 of the investigated different asynchronized normoxic repair-competent mammalian cell lines. The only exception (Chinese Hamster peritoneal fibroblasts, B14FAF28), is likely due to the limited dataset (all in vitro ion data were extracted from a single publication), systematic differences in the linear energy transfer (LET) calculations for the employed very-heavy ions, and the use of reference photon survival curves extracted from a different publication. Finally, the in silico predictions for the 10 ions not used for the model development were in good agreement with the corresponding in vitro data.

Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners

Radiation therapy (RT) is a constantly evolving therapeutic technique; improvements are continuously being introduced for both methodological and practical aspects. Among the features that have undergone a huge evolution in recent decades, dose calculation algorithms are still rapidly changing. This process is propelled by the awareness that the agreement between the delivered and calculated doses is of paramount relevance in RT, since it could largely affect clinical outcomes. The aim of this work is to provide an overall picture of the main dose calculation algorithms currently used in RT, summarizing their underlying physical models and mathematical bases, and highlighting their strengths and weaknesses, referring to the most recent studies on algorithm comparisons. This handy guide is meant to provide a clear and concise overview of the topic, which will prove useful in helping clinical medical physicists to perform their responsibilities more effectively and efficiently, increasing patient benefits and improving the overall quality of the management of radiation treatment.