Effects of Differing Underlying Assumptions in In Silico Models on Predictions of DNA Damage and Repair.

The induction and repair of DNA double-strand breaks (DSBs) are critical factors in the treatment of cancer by radiotherapy. To investigate the relationship between incident radiation and cell death through DSB induction many in silico models have been developed. These models produce and use custom formats of data, specific to the investigative aims of the researchers, and often focus on particular pairings of damage and repair models. In this work we use a standard format for reporting DNA damage to evaluate combinations of different, independently developed, models. We demonstrate the capacity of such inter-comparison to determine the sensitivity of models to both known and implicit assumptions. Specifically, we report on the impact of differences in assumptions regarding patterns of DNA damage induction on predicted initial DSB yield, and the subsequent effects this has on derived DNA repair models. The observed differences highlight the importance of considering initial DNA damage on the scale of nanometres rather than micrometres. We show that the differences in DNA damage models result in subsequent repair models assuming significantly different rates of random DSB end diffusion to compensate. This in turn leads to disagreement on the mechanisms responsible for different biological endpoints, particularly when different damage and repair models are combined, demonstrating the importance of inter-model comparisons to explore underlying model assumptions.

Absence of Tissue-Sparing Effects in Partial Proton FLASH Irradiation in Murine Intestine.

Ultra-high dose rate irradiation has been reported to protect normal tissues more than conventional dose rate irradiation. This tissue sparing has been termed the FLASH effect. We investigated the FLASH effect of proton irradiation on the intestine as well as the hypothesis that lymphocyte depletion is a cause of the FLASH effect. A 16 × 12 mm2 elliptical field with a dose rate of ~120 Gy/s was provided by a 228 MeV proton pencil beam. Partial abdominal irradiation was delivered to C57BL/6j and immunodeficient Rag1-/-/C57 mice. Proliferating crypt cells were counted at 2 days post exposure, and the thickness of the muscularis externa was measured at 280 days following irradiation. FLASH irradiation did not reduce the morbidity or mortality of conventional irradiation in either strain of mice; in fact, a tendency for worse survival in FLASH-irradiated mice was observed. There were no significant differences in lymphocyte numbers between FLASH and conventional-dose-rate mice. A similar number of proliferating crypt cells and a similar thickness of the muscularis externa following FLASH and conventional dose rate irradiation were observed. Partial abdominal FLASH proton irradiation at 120 Gy/s did not spare normal intestinal tissue, and no difference in lymphocyte depletion was observed. This study suggests that the effect of FLASH irradiation may depend on multiple factors, and in some cases dose rates of over 100 Gy/s do not induce a FLASH effect and can even result in worse outcomes.

Nano-scale simulation of neuronal damage by galactic cosmic rays.

The effects of realistic, deep space radiation environments on neuronal function remain largely unexplored.In silicomodeling studies of radiation-induced neuronal damage provide important quantitative information about physico-chemical processes that are not directly accessible through radiobiological experiments. Here, we present the first nano-scale computational analysis of broad-spectrum galactic cosmic ray irradiation in a realistic neuron geometry. We constructed thousands ofin silicorealizations of a CA1 pyramidal neuron, each with over 3500 stochastically generated dendritic spines. We simulated the entire 33 ion-energy beam spectrum currently in use at the NASA Space Radiation Laboratory galactic cosmic ray simulator (GCRSim) using the TOol for PArticle Simulation (TOPAS) and TOPAS-nBio Monte Carlo-based track structure simulation toolkits. We then assessed the resulting nano-scale dosimetry, physics processes, and fluence patterns. Additional comparisons were made to a simplified 6 ion-energy spectrum (SimGCRSim) also used in NASA experiments. For a neuronal absorbed dose of 0.5 Gy GCRSim, we report an average of 250 ± 10 ionizations per micrometer of dendritic length, and an additional 50 ± 10, 7 ± 2, and 4 ± 2 ionizations per mushroom, thin, and stubby spine, respectively. We show that neuronal energy deposition by proton andα-particle tracks declines approximately hyperbolically with increasing primary particle energy at mission-relevant energies. We demonstrate an inverted exponential relationship between dendritic segment irradiation probability and neuronal absorbed dose for each ion-energy beam. We also find that there are no significant differences in the average physical responses between the GCRSim and SimGCRSim spectra. To our knowledge, this is the first nano-scale simulation study of a realistic neuron geometry using the GCRSim and SimGCRSim spectra. These results may be used as inputs to theoretical models, aid in the interpretation of experimental results, and help guide future study designs.

A systematic review of clinical studies on variable proton Relative Biological Effectiveness (RBE).

Recently, a number of clinical studies have explored links between possible Relative Biological Effectiveness (RBE) elevations and patient toxicities and/or image changes following proton therapy. Our objective was to perform a systematic review of such studies. We applied a "Problem [RBE], Intervention [Protons], Population [Patients], Outcome [Side effect]" search strategy to the PubMed database. From our search, we retrieved studies which: (a) performed novel voxel-wise analyses of patient effects versus physical dose and LET (n = 13), and (b) compared image changes between proton and photon cohorts with regard to proton RBE (n = 9). For each retrieved study, we extracted data regarding: primary tumour type; size of patient cohort; type of image change studied; image-registration method (deformable or rigid); LET calculation method, and statistical methodology. We compared and contrasted their methods in order to discuss the weight of clinical evidence for variable proton RBE. We concluded that clinical evidence for variable proton RBE remains statistically weak at present. Our principal recommendation is that proton centres and clinical trial teams collaborate to standardize follow-up protocols and statistical analysis methods, so that larger patient cohorts can ultimately be considered for RBE analyses.

Challenges in the quantification approach to a radiation relevant adverse outcome pathway for lung cancer.

PURPOSE: Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed. MATERIALS AND METHODS: Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets. RESULTS: Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from 94-5+5 to 192-18+15 keV/µm. CONCLUSION: Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of 'deposition of energy' is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an 'all hazards' approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.

Cellular Response to Proton Irradiation: A Simulation Study with TOPAS-nBio.

The cellular response to ionizing radiation continues to be of significant research interest in cancer radiotherapy, and DNA is recognized as the critical target for most of the biologic effects of radiation. Incident particles can cause initial DNA damages through physical and chemical interactions within a short time scale. Initial DNA damages can undergo repair via different pathways available at different stages of the cell cycle. The misrepair of DNA damage results in genomic rearrangement and causes mutations and chromosome aberrations, which are drivers of cell death. This work presents an integrated study of simulating cell response after proton irradiation with energies of 0.5-500 MeV (LET of 60-0.2 keV/µm). A model of a whole nucleus with fractal DNA geometry was implemented in TOPAS-nBio for initial DNA damage simulations. The default physics and chemistry models in TOPAS-nBio were used to describe interactions of primary particles, secondary particles, and radiolysis products within the nucleus. The initial DNA double-strand break (DSB) yield was found to increase from 6.5 DSB/Gy/Gbp at low-linear energy transfer (LET) of 0.2 keV/µm to 21.2 DSB/Gy/Gbp at high LET of 60 keV/µm. A mechanistic repair model was applied to predict the characteristics of DNA damage repair and dose response of chromosome aberrations. It was found that more than 95% of the DSBs are repaired within the first 24 h and the misrepaired DSB fraction increases rapidly with LET and reaches 15.8% at 60 keV/µm with an estimated chromosome aberration detection threshold of 3 Mbp. The dicentric and acentric fragment yields and the dose response of micronuclei formation after proton irradiation were calculated and compared with experimental results.

Perspectives on the model-based approach to proton therapy trials: A retrospective study of a lung cancer randomized trial.

PURPOSE: The goal of this study was to assess whether a model-based approach applied retrospectively to a completed randomized controlled trial (RCT) would have significantly altered the selection of patients of the original trial, using the same selection criteria and endpoint for testing the potential clinical benefit of protons compared to photons. METHODS AND MATERIALS: A model-based approach, based on three widely used normal tissue complication probability (NTCP) models for radiation pneumonitis (RP), was applied retrospectively to a completed non-small cell lung cancer RCT (NCT00915005). It was assumed that patients were selected by the model-based approach if their expected ΔNTCP value was above a threshold of 5%. The endpoint chosen matched that of the original trial, the first occurrence of severe (grade ≥3) RP. RESULTS: Our analysis demonstrates that NTCP differences between proton and photon therapy treatments may be too small to support a model-based trial approach for lung cancer using RP as the normal tissue endpoint. The analyzed lung trial showed that less than 19% (32/165) of patients enrolled in the completed trial would have been enrolled in a model-based trial, prescribing photon therapy to all other patients. The number of patients enrolled was also found to be dependent on the type of NTCP model used for evaluating RP, with the three models enrolling 3%, 13% or 19% of patients. This result does show limitations in NTCP models which would affect the success of a model-based trial approach. No conclusion regarding the development of RP in patients randomized by the model-based approach could statistically be made. CONCLUSIONS: Uncertainties in the outcome models to predict NTCP are the inherent drawback of a model-based approach to clinical trials. The impact of these uncertainties on enrollment in model-based trials depends on the predicted difference between the two treatment arms and on the set threshold for patient stratification. Our analysis demonstrates that NTCP differences between proton and photon therapy treatments may be too small to support a model-based trial approach for specific treatment sites, such as lung cancer, depending on the chosen normal tissue endpoint.

End-of-Range Radiobiological Effect on Rib Fractures in Patients Receiving Proton Therapy for Breast Cancer.

PURPOSE: A prospective trial of proton therapy for breast cancer revealed an increased rib fracture rate of 7%, which is higher than the expected rate based on the literature on photon therapies. We aim to evaluate the hypothesis that the increased relative biological effectiveness (RBE) at the distal edge of proton beams is the cause. METHODS AND MATERIALS: We combined the cohort from the prospective clinical trial and a retrospective cohort from a database. Monte Carlo simulations were performed to recalculate the physical dose and dose-averaged linear energy transfer (LETd). The first 10 ribs and fracture areas in patients with fractures were contoured and deformably registered. The LETd-weighted dose was used as a surrogate for biological effectiveness and compared with the conventional fixed RBE of 1.1. Dose to 0.5 cm3 of the ribs (D0.5) was selected to analyze the dose-response relationship using logistic regression. We chose an alpha/beta ratio of 3 to calculate the biological effective dose in Gy3(RBE). RESULTS: Thirteen of 203 patients in the cohorts exhibited a total of 25 fractures. The LETd in fractured areas is increased (6.1 ± 2.0 keV/µm, mean ± standard deviation), suggesting possible end-of-range radiobiological effects with increased RBE. The D0.5 of the fractured ribs is 80.3 ± 9.4 Gy3(RBE) with a generic factor of 1.1 and is relatively low compared with historical photon results. On the other hand, the D0.5 of the fractured ribs is 100.0 ± 12.5 Gy3(RBE) using the LETd-based model with a dose-response curve that is more consistent with historical photon data. CONCLUSIONS: The increased rib fracture rate seen in our trial is probably associated with the increased LETd and RBE at the distal edge of proton beams. This phenomenon warrants further investigation and possible integration of LETd into treatment planning and optimization in proton therapy.

A parameter sensitivity study for simulating DNA damage after proton irradiation using TOPAS-nBio.

Monte Carlo (MC) track structure simulation tools are commonly used for predicting radiation induced DNA damage by modeling the physical and chemical reactions at the nanometer scale. However, the outcome of these MC simulations is particularly sensitive to the adopted parameters which vary significantly across studies. In this study, a previously developed full model of nuclear DNA was used to describe the DNA geometry. The TOPAS-nBio MC toolkit was used to investigate the impact of physics and chemistry models as well as three key parameters (the energy threshold for direct damage, the chemical stage time length, and the probability of damage between hydroxyl radical reactions with DNA) on the induction of DNA damage. Our results show that the difference in physics and chemistry models alone can cause differences up to 34% and 16% in the DNA double strand break (DSB) yield, respectively. Additionally, changing the direct damage threshold, chemical stage length, and hydroxyl damage probability can cause differences of up to 28%, 51%, and 71% in predicted DSB yields, respectively, for the configurations in this study.

The microdosimetric extension in TOPAS: development and comparison with published data.

Microdosimetric energy depositions have been suggested as a key variable for the modeling of the relative biological effectiveness (RBE) in proton and ion radiation therapy. However, microdosimetry has been underutilized in radiation therapy. Recent advances in detector technology allow the design of new mico- and nano-dosimeters. At the same time Monte Carlo (MC) simulations have become more widely used in radiation therapy. In order to address the growing interest in the field, a microdosimetric extension was developed in TOPAS. The extension provides users with the functionality to simulate microdosimetric spectra as well as the contribution of secondary particles to the spectra, calculate microdosimetric parameters, and determine RBE with a biological weighting function approach or with the microdosimetric kinetic (MK) model. Simulations were conducted with the extension and the results were compared with published experimental data and other simulation results for three types of microdosimeters, a spherical tissue equivalent proportional counter (TEPC), a cylindrical TEPC and a solid state microdosimeter. The corresponding microdosimetric spectra obtained with TOPAS from the plateau region to the distal tail of the Bragg curve generally show good agreement with the published data.

Geometrical structures for radiation biology research as implemented in the TOPAS-nBio toolkit.

Computational simulations, such as Monte Carlo track structure simulations, offer a powerful tool for quantitatively investigating radiation interactions within cells. The modelling of the spatial distribution of energy deposition events as well as diffusion of chemical free radical species, within realistic biological geometries, can help provide a comprehensive understanding of the effects of radiation on cells. Track structure simulations, however, generally require advanced computing skills to implement. The TOPAS-nBio toolkit, an extension to TOPAS (TOol for PArticle Simulation), aims to provide users with a comprehensive framework for radiobiology simulations, without the need for advanced computing skills. This includes providing users with an extensive library of advanced, realistic, biological geometries ranging from the micrometer scale (e.g. cells and organelles) down to the nanometer scale (e.g. DNA molecules and proteins). Here we present the geometries available in TOPAS-nBio.

A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation.

Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage repair to predict the sensitivity of cells to X-ray, proton or carbon ion exposures in vitro against over 800 published experiments. By specifying the phenotypic characteristics of cells, the model was able to effectively stratify X-ray radiosensitivity (R 2 = 0.74) without the use of any cell-specific fitting parameters. This model was extended to charged particle exposures by integrating Monte Carlo calculated dose distributions, and successfully fit to cellular proton radiosensitivity using a single dose-related parameter (R 2 = 0.66). Using these parameters, the model was also shown to be predictive of carbon ion RBE (R 2 = 0.77). This model can effectively predict cellular sensitivity to a range of radiations, and has the potential to support developments of personalised radiotherapy independent of radiation type.

Correction: Dependence of gold nanoparticle radiosensitization on cell geometry.

Correction for 'Dependence of gold nanoparticle radiosensitization on cell geometry' by Wonmo Sung, et al., Nanoscale, 2017, 9, 5843-5853.

Dependence of gold nanoparticle radiosensitization on cell geometry.

The radiosensitization effect of gold nanoparticles (GNPs) has been demonstrated both in vitro and in vivo in radiation therapy. The purpose of this study was to systematically assess the biological effectiveness of GNPs distributed in the extracellular media for realistic cell geometries. TOPAS-nBio simulations were used to determine the nanometre-scale radial dose distributions around the GNPs, which were subsequently used to predict the radiation dose response of cells surrounded by GNPs. MDA-MB-231 human breast cancer cells and F-98 rat glioma cells were used as models to assess different cell geometries by changing (1) the cell shape, (2) the nucleus location within the cell, (3) the size of GNPs, and (4) the photon energy. The results show that the sensitivity enhancement ratio (SER) was increased up to a factor of 1.2 when the location of the nucleus is close to the cell membrane for elliptical-shaped cells. Heat-maps of damage-likelihoods show that most of the lethal events occur in the regions of the nuclei closest to the membrane, potentially causing highly clustered damage patterns. The effect of the GNP size on radiosensitization was limited when the GNPs were located outside the cell. The improved modelling of the cell geometry was shown to be crucial because the dose enhancement caused by GNPs falls off rapidly with distance from the GNPs. We conclude that radiosensitization can be achieved for kV photons even without cellular uptake of GNPs when the nucleus is shifted towards the cell membrane. Furthermore, damage was found to concentrate in a small region of the nucleus in close proximity to the extracellular, GNP-laden region.

A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data.

Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies along the treatment field, i.e. with linear energy transfer (LET) and with tissue type. A recent review study collected over 70 experimental reports on proton RBE, providing a comprehensive dataset for predicting RBE for cell survival. Using this dataset we developed a model to predict proton RBE based on dose, dose average LET (LETd) and the ratio of the linear-quadratic model parameters for the reference radiation (α/ß)x, as the tissue specific parameter. The proposed RBE model is based on the linear quadratic model and was derived from a nonlinear regression fit to 287 experimental data points. The proposed model predicts that the RBE increases with increasing LETd and decreases with increasing (α/ß)x. This agrees with previous theoretical predictions on the relationship between RBE, LETd and (α/ß)x. The model additionally predicts a decrease in RBE with increasing dose and shows a relationship between both α and ß with LETd. Our proposed phenomenological RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. Previously published phenomenological models, based on a limited data set, may have to be revised.

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints.

The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.

Feasibility study of a dual detector configuration concept for simultaneous megavoltage imaging and dose verification in radiotherapy.

PURPOSE: To test the feasibility of a dual detector concept for comprehensive verification of external beam radiotherapy. Specifically, the authors test the hypothesis that a portal imaging device coupled to a 2D dosimeter provides a system capable of simultaneous imaging and dose verification, and that the presence of each device does not significantly detract from the performance of the other. METHODS: The dual detector configuration comprised of a standard radiotherapy electronic portal imaging device (EPID) positioned directly on top of an ionization-chamber array (ICA) with 2 cm solid water buildup material (between EPID and ICA) and 5 cm solid backscatter material. The dose response characteristics of the ICA and the imaging performance of the EPID in the dual detector configuration were compared to the performance in their respective reference clinical configurations. The reference clinical configurations were 6 cm solid water buildup material, an ICA, and 5 cm solid water backscatter material as the reference dosimetry configuration, and an EPID with no additional buildup or solid backscatter material as the reference imaging configuration. The dose response of the ICA was evaluated by measuring the detector's response with respect to off-axis position, field size, and transit object thickness. Clinical dosimetry performance was evaluated by measuring a range of clinical intensity-modulated radiation therapy (IMRT) beams in transit and nontransit geometries. The imaging performance of the EPID was evaluated quantitatively by measuring the contrast-to-noise ratio (CNR) and spatial resolution. Images of an anthropomorphic phantom were also used for qualitative assessment. RESULTS: The measured off-axis and field size response with the ICA in both transit and nontransit geometries for both dual detector configuration and reference dosimetry configuration agreed to within 1%. Transit dose response as a function of object thickness agreed to within 0.5%. All IMRT test patterns and clinical IMRT beams had gamma pass rates of ≥98% at 2%/2 mm criteria. In terms of imaging performance, the measured CNR and spatial resolution (f50) were 263.23 ± 24.85 and 0.4025 ± 1.25 × 10(-3) for dual detector configuration and 324 ± 26.65 and 0.4141 ± 1.14 × 10(-3) for reference imaging configuration, respectively. The CNR and spatial resolution were quantitatively worse in the dual detector configuration due to the additional backscatter. The difference in imaging performance was not visible in qualitative assessment of phantom images. CONCLUSIONS: Combining a commercially available ICA dosimetry device with a conventional EPID did not significantly detract from the performance of either device. Further improvements in imaging performance may be achieved with an optimized design. This study demonstrates the feasibility of a dual detector concept for simultaneous imaging and dosimetry in radiation therapy.

Characterization of a novel EPID designed for simultaneous imaging and dose verification in radiotherapy.

PURPOSE: Standard amorphous silicon electronic portal imaging devices (a-Si EPIDs) are x-ray imagers used frequently in radiotherapy that indirectly detect incident x-rays using a metal plate and phosphor screen. These detectors may also be used as two-dimensional dosimeters; however, they have a well-characterized nonwater-equivalent dosimetric response. Plastic scintillating (PS) fibers, on the other hand, have been shown to respond in a water-equivalent manner to x-rays in the energy range typically encountered during radiotherapy. In this study, the authors report on the first experimental measurements taken with a novel prototype PS a-Si EPID developed for the purpose of performing simultaneous imaging and dosimetry in radiotherapy. This prototype employs an array of PS fibers in place of the standard metal plate and phosphor screen. The imaging performance and dosimetric response of the prototype EPID were evaluated experimentally and compared to that of the standard EPID. METHODS: Clinical 6 MV photon beams were used to first measure the detector sensitivity, linearity of dose response, and pixel noise characteristics of the prototype and standard EPIDs. Second, the dosimetric response of each EPID was evaluated relative to a reference water-equivalent dosimeter by measuring the off-axis and field size response in a nontransit configuration, along with the off-axis, field size, and transmission response in a transit configuration using solid water blocks. Finally, the imaging performance of the prototype and standard EPIDs was evaluated quantitatively by using an image quality phantom to measure the contrast to noise ratio (CNR) and spatial resolution of images acquired with each detector, and qualitatively by using an anthropomorphic phantom to acquire images representative of human anatomy. RESULTS: The prototype EPID's sensitivity was 0.37 times that of the standard EPID. Both EPIDs exhibited responses that were linear with delivered dose over a range of 1-100 monitor units. Over this range, the prototype and standard EPID central axis responses agreed to within 1.6%. Images taken with the prototype EPID were noisier than those taken with the standard EPID, with fractional uncertainties of 0.2% and 0.05% within the central 1 cm(2), respectively. For all dosimetry measurements, the prototype EPID exhibited a near water-equivalent response whereas the standard EPID did not. The CNR and spatial resolution of images taken with the standard EPID were greater than those taken with the prototype EPID. CONCLUSIONS: A prototype EPID employing an array of PS fibers has been developed and the first experimental measurements are reported. The prototype EPID demonstrated a much morewater-equivalent dose response than the standard EPID. While the imaging performance of the standard EPID was superior to that of the prototype, the prototype EPID has many design characteristics that may be optimized to improve imaging performance. This investigation demonstrates the feasibility of a new detector design for simultaneous imaging and dosimetry treatment verification in radiotherapy.

Characterization of optical transport effects on EPID dosimetry using Geant4.

PURPOSE: Current amorphous silicon electronic portal imaging devices (a-Si EPIDs) that are frequently used in radiotherapy applications employ a metal plate/phosphor screen configuration to optimize x-ray detection efficiency. The phosphor acts to convert x rays into an optical signal that is detected by an underlying photodiode array. The dosimetric response of EPIDs has been well characterized, in part through the development of computational models. Such models, however, have generally made simplifying assumptions with regards to the transport of optical photons within these detectors. The goal of this work was to develop and experimentally validate a new Monte Carlo (MC) model of an a-Si EPID that simulates both x-ray and optical photon transport in a self-contained manner. Using this model the authors establish a definitive characterization of the effects of optical transport on the dosimetric response of a-Si EPIDs employing gadolinium oxysulfide phosphor screens. METHODS: The Geant4 MC toolkit was used to develop a model of an a-Si EPID that employs standard electromagnetic and optical physics classes. The sensitivity of EPID response to uncertainties in optical transport parameters was evaluated by investigating their effects on the EPID point spread function (PSF). An optical blur kernel was also calculated to isolate the component of the PSF resulting purely from optical transport. A 6 MV photon source model was developed and integrated into the MC model to investigate EPID dosimetric response. Field size output factors and relative dose profiles were calculated for a set of open fields by separately scoring energy deposited in the phosphor and optical absorption events in the photodiode. These were then compared to quantify effects resulting from optical photon transport. The EPID model was validated against experimental measurements taken using a research EPID. RESULTS: Optical photon scatter within the phosphor screen noticeably broadened the PSF. Variations in optical transport parameters reported in the literature caused fluctuations in the PSF full width at half maximum (FWHM) and full width at tenth maximum (FWTM) of less than 3% and 5%, respectively, confirming model robustness. Greater deviations (up to 9.5% and 36% for FWHM and FWTM, respectively) were observed when optical parameters were largely different from reference values. When scoring energy deposition in the phosphor, measured and calculated output factors agreed within statistical uncertainties and at least 94% of the MC simulated profile data points passed 3%/3 mm γ-index criterion for all field sizes considered. Despite statistical uncertainties in optical simulations arising from computational limitations, no differences were observed between optical and energy deposition profiles. CONCLUSIONS: Simulations demonstrated noticeable blurring of the EPID PSF when scoring optical absorption events in the photodiode relative to energy deposition in the phosphor. However, modeling the standard electromagnetic transport alone should suffice when using MC methods to predict EPID dose-response to static, open 6 MV fields with a standard a-Si photodiode array. Therefore, using energy deposition in the phosphor as a surrogate for EPID dose-response is a valid approach that should not require additional corrections for optical transport effects in current a-Si EPIDs employing phosphor screens.

Predicted ionisation in mitochondria and observed acute changes in the mitochondrial transcriptome after gamma irradiation: a Monte Carlo simulation and quantitative PCR study.

It is a widely accepted that the cell nucleus is the primary site of radiation damage while extra-nuclear radiation effects are not yet systematically included into models of radiation damage. We performed Monte Carlo simulations assuming a spherical cell (diameter 11.5 µm) modelled after JURKAT cells with the inclusion of realistic elemental composition data based on published literature. The cell model consists of cytoplasm (density 1g/cm(3)), nucleus (diameter 8.5 µm; 40% of cell volume) as well as cylindrical mitochondria (diameter 1 µm; volume 0.5 µm(3)) of three different densities (1, 2 and 10 g/cm(3)) and total mitochondrial volume relative to the cell volume (10, 20, 30%). Our simulation predicts that if mitochondria take up more than 20% of a cell's volume, ionisation events will be the preferentially located in mitochondria rather than in the cell nucleus. Using quantitative polymerase chain reaction, we substantiate in JURKAT cells that human mitochondria respond to gamma radiation with early (within 30 min) differential changes in the expression levels of 18 mitochondrially encoded genes, whereby the number of regulated genes varies in a dose-dependent but non-linear pattern (10 Gy: 1 gene; 50 Gy: 5 genes; 100 Gy: 12 genes). The simulation data as well as the experimental observations suggest that current models of acute radiation effects, which largely focus on nuclear effects, might benefit from more systematic considerations of the early mitochondrial responses and how these may subsequently determine cell response to ionising radiation.