Characterisation of the UK high energy proton research beamline for high and ultra-high dose rate (FLASH) irradiation

Objective. This work sets out the capabilities of the high energy proton research beamline developed in the Christie proton therapy centre for Ultra-High Dose Rate (UHDR) irradiation and FLASH experiments. It also characterises the lower limits of UHDR operation for this Pencil Beam Scanning (PBS) proton hardware.Approach. Energy dependent nozzle transmission was measured using a Faraday Cup beam collector. Spot size was measured at the reference plane using a 2D scintillation detector. Integrated depth doses (IDDs) were measured. EBT3 Gafchromic film was used to compare UHDR and conventional dose rate spots. Our beam monitor calibration methodolgy for UHDR is described. A microDiamond detector was used to determine dose rates at zref. Instantaneous depth dose rates were calculated for 70-245 MeV. PBS dose rate distributions were calculated using Folkerts and Van der Water definitions.Main results. Transmission of 7.05 ± 0.1% is achieveable corresponding to a peak instantaneous dose rate of 112.7 Gy s-1. Beam parameters are comparable in conventional and UHDR mode with a spot size ofσx= 4.6 mm,σy= 6.6 mm. Dead time in the beam monitoring electonics warrants a beam current dependent MU correction in the present configuration. Fast beam scanning of 26.4 m s-1(X) and 12.1 m s-1(Y) allows PBS dose rates of the order tens of Grays per second.Significance. UHDR delivery is possible for small field sizes and high energies enabling research into the FLASH effect with PBS protons at our facility. To our knowledge this is also the first thorough characterisation of UHDR irradiation using the hardware of this clinical accelerator at energies less than 250 MeV. The data set out in this publication can be used for designing experiments at this UK research facility and inform the possible future clinical translation of UHDR PBS proton therapy.

Assessing Equity of Access to Proton Beam Therapy: A Literature Review

Proton beam therapy (PBT) is one of the most advanced radiotherapy technologies, with growing evidence to support its use in specific clinical scenarios and exponential growth of demand and capacity worldwide over the past few decades. However, geographical inequalities persist in the distribution of PBT centres, which translate into variations in access and use of this technology. The aim of this work was to look at the factors that contribute to these inequalities, to help raise awareness among stakeholders, governments and policy makers. A literature search was conducted using the Population, Intervention, Comparison, Outcomes (PICO) criteria. The same search strategy was run in Embase and Medline and identified 242 records, which were screened for manual review. Of these, 24 were deemed relevant and were included in this analysis. Most of the 24 publications included in this review originated from the USA (22/24) and involved paediatric patients, teenagers and young adults (61% for children and/or teenagers and young adults versus 39% for adults). The most reported indicator of disparity was socioeconomic status (16/24), followed by geographical location (13/24). All the studies evaluated in this review showed disparities in the access to PBT. As paediatric patients make up a significant proportion of the PBT-eligible patients, equity of access to PBT also raises ethical considerations. Therefore, further research is needed into the equity of access to PBT to reduce the care gap.

An Update to the Malthus Model for Radiotherapy Utilisation in England

AIMS

The Malthus Programme predicts national and local radiotherapy demand by combining cancer incidence data with decision trees detailing the indications, and appropriate dose fractionation, for radiotherapy. Since the last model update in 2017, technological advancements and the COVID-19 pandemic have led to increasing hypofractionation of radiotherapy schedules. Indications for radiotherapy have also evolved, particularly in the context of oligometastatic disease. Here we present a brief update on the model for 2021. We have updated the decision trees for breast, prostate, lung and head and neck cancers, and incorporated recent cancer incidence data into our model, generating a current estimate of fraction demand for these four cancer sites across England.

MATERIALS AND METHODS

The decision tree update was based on evidence from practice-changing randomised controlled trials, published guidelines, audit data and expert opinion. Site- and stage-specific incidence data were taken from the National Disease Registration Service. We used the updated model to estimate the proportion of patients who would receive radiotherapy (appropriate rate of radiotherapy) and the fraction demand per million population at a national and Clinical Commissioning Group level in 2021.

RESULTS

The total predicted fraction demand has decreased by 11.4% across all four cancer sites in our new model, compared with the 2017 version. This reduction can be explained primarily by greater use of hypofractionated treatments (including stereotactic ablative radiotherapy) and a shift towards earlier stage presentation. The only large change in appropriate rate of radiotherapy was an absolute decrease of 3% for lung cancer.

CONCLUSIONS

Compared with our previous model, the current version predicts a reduction in fraction demand across England. This is driven principally by hypofractionation of radiotherapy regimens, using technology that requires increasingly complex planning. Treatment complexity and local service factors need to be taken into account when translating fraction burden into linear accelerator demand or throughput.

Variations in Demand across England for the Magnetic Resonance-Linac Technology, Simulated Utilising Local-level Demographic and Cancer Data in the Malthus Project

AIMS

Cancer incidence varies across England, which affects the local-level demand for treatments. The magnetic resonance-linac (MR-linac) is a new radiotherapy technology that combines imaging and treatment. Here we model the demand and demand variations for the MR-linac across England.

MATERIALS AND METHODS

Initial clinical indications were provided by the MR-linac consortium and introduced into the Malthus radiotherapy clinical decision trees. The Malthus model contains Clinical Commissioning Group (CCG) population, cancer incidence and stage presentation data (for lung and prostate) and simulated the demand for the MR-linac for all CCGs and Radiotherapy Operational Delivery Networks (RODN) across England.

RESULTS

Based on the initial target clinical indications, the MR-linac could service 16% of England's fraction burden. The simulated fractions/million population demand/annum varies between 3000 and 10 600 fractions/million at the CCG level. Focussing only on the cancer population, the simulated fractions/1000 cancer cases demand/annum ranges from 1028 to 1195 fractions/1000 cases. If a national average for fractions/million demand was then used, at the RODN level, the variation from actual annual demand ranges from an overestimation of 8400 fractions to an underestimation of 5800 fractions. When using the national average fractions/1000 cases, the RODN demand varies from an overestimation of 3200 fractions to an underestimation of 3000 fractions.

CONCLUSIONS

Planning cancer services is complex due to regional variations in cancer burden. The variations in simulated demand of the MR-linac highlight the requirement to use local-level data when planning to introduce a new technology.

Evaluating very high energy electron RBE from nanodosimetric pBR322 plasmid DNA damage

This paper presents the first plasmid DNA irradiations carried out with Very High Energy Electrons (VHEE) over 100-200 MeV at the CLEAR user facility at CERN to determine the Relative Biological Effectiveness (RBE) of VHEE. DNA damage yields were measured in dry and aqueous environments to determine that ~ 99% of total DNA breaks were caused by indirect effects, consistent with other published measurements for protons and photons. Double-Strand Break (DSB) yield was used as the biological endpoint for RBE calculation, with values found to be consistent with established radiotherapy modalities. Similarities in physical damage between VHEE and conventional modalities gives confidence that biological effects of VHEE will also be similar-key for clinical implementation. Damage yields were used as a baseline for track structure simulations of VHEE plasmid irradiation using GEANT4-DNA. Current models for DSB yield have shown reasonable agreement with experimental values. The growing interest in FLASH radiotherapy motivated a study into DSB yield variation with dose rate following VHEE irradiation. No significant variations were observed between conventional and FLASH dose rate irradiations, indicating that no FLASH effect is seen under these conditions.

In Silico Models of DNA Damage and Repair in Proton Treatment Planning: A Proof of Concept

There is strong in vitro cell survival evidence that the relative biological effectiveness (RBE) of protons is variable, with dependence on factors such as linear energy transfer (LET) and dose. This is coupled with the growing in vivo evidence, from post-treatment image change analysis, of a variable RBE. Despite this, a constant RBE of 1.1 is still applied as a standard in proton therapy. However, there is a building clinical interest in incorporating a variable RBE. Recently, correlations summarising Monte Carlo-based mechanistic models of DNA damage and repair with absorbed dose and LET have been published as the Manchester mechanistic (MM) model. These correlations offer an alternative path to variable RBE compared to the more standard phenomenological models. In this proof of concept work, these correlations have been extended to acquire RBE-weighted dose distributions and calculated, along with other RBE models, on a treatment plan. The phenomenological and mechanistic models for RBE have been shown to produce comparable results with some differences in magnitude and relative distribution. The mechanistic model found a large RBE for misrepair, which phenomenological models are unable to do. The potential of the MM model to predict multiple endpoints presents a clear advantage over phenomenological models.

Clinically relevant nanodosimetric simulation of DNA damage complexity from photons and protons

Relative Biological Effectiveness (RBE), the ratio of doses between radiation modalities to produce the same biological endpoint, is a controversial and important topic in proton therapy. A number of phenomenological models incorporate variable RBE as a function of Linear Energy Transfer (LET), though a lack of mechanistic description limits their applicability. In this work we take a different approach, using a track structure model employing fundamental physics and chemistry to make predictions of proton and photon induced DNA damage, the first step in the mechanism of radiation-induced cell death. We apply this model to a proton therapy clinical case showing, for the first time, predictions of DNA damage on a patient treatment plan. Our model predictions are for an idealised cell and are applied to an ependymoma case, at this stage without any cell specific parameters. By comparing to similar predictions for photons, we present a voxel-wise RBE of DNA damage complexity. This RBE of damage complexity shows similar trends to the expected RBE for cell kill, implying that damage complexity is an important factor in DNA repair and therefore biological effect.

Relative Biological Effectiveness (RBE) is a controversial and important topic in proton therapy. This work uses Monte Carlo simulations of DNA damage for protons and photons to probe this phenomenon, providing a plausible mechanistic understanding.

A New Standard DNA Damage (SDD) Data Format

Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called "indirect" damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.

Mathematical Modelling for Patient Selection in Proton Therapy

Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.

Nanodosimetric Simulation of Direct Ion-Induced DNA Damage Using Different Chromatin Geometry Models

Monte Carlo based simulation has proven useful in investigating the effect of proton-induced DNA damage and the processes through which this damage occurs. Clustering of ionizations within a small volume can be related to DNA damage through the principles of nanodosimetry. For simulation, it is standard to construct a small volume of water and determine spatial clusters. More recently, realistic DNA geometries have been used, tracking energy depositions within DNA backbone volumes. Traditionally a chromatin fiber is built within the simulation and identically replicated throughout a cell nucleus, representing the cell in interphase. However, the in vivo geometry of the chromatin fiber is still unknown within the literature, with many proposed models. In this work, the Geant4-DNA toolkit was used to build three chromatin models: the solenoid, zig-zag and cross-linked geometries. All fibers were built to the same chromatin density of 4.2 nucleosomes/11 nm. The fibers were then irradiated with protons (LET 5-80 keV/μm) or alpha particles (LET 63-226 keV/μm). Nanodosimetric parameters were scored for each fiber after each LET and used as a comparator among the models. Statistically significant differences were observed in the double-strand break backbone size distributions among the models, although nonsignificant differences were noted among the nanodosimetric parameters. From the data presented in this article, we conclude that selection of the solenoid, zig-zag or cross-linked chromatin model does not significantly affect the calculated nanodosimetric parameters. This allows for a simulation-based cell model to make use of any of these chromatin models for the scoring of direct ion-induced DNA damage.

Automatic cell detection in bright-field microscopy for microbeam irradiation studies

Automatic cell detection in bright-field illumination microscopy is challenging due to cells' inherent optical properties. Applications including individual cell microbeam irradiation demand minimisation of additional cell stressing factors, so contrast-enhancing fluorescence microscopy should be avoided. Additionally, the use of optically non-homogeneous substrates amplifies the problem. This research focuses on the design of a method for automatic cell detection on polypropylene substrate, suitable for microbeam irradiation. In order to fulfil the relative requirements, the Harris corner detector was employed to detect apparent cellular features. These features-corners were clustered based on a dual-clustering technique according to the density of their distribution across the image. Weighted centroids were extracted from the clusters of corners and constituted the targets for irradiation. The proposed method identified more than 88% of the 1,738 V79 Chinese hamster cells examined. Moreover, a processing time of 2.6 s per image fulfilled the requirements for a near real-time cell detection-irradiation system.

Investigation of gold nanoparticle radiosensitization mechanisms using a free radical scavenger and protons of different energies

Gold nanoparticles (GNPs) have been shown to sensitize cancer cells to x-ray radiation, particularly at kV energies where photoelectric interactions dominate and the high atomic number of gold makes a large difference to x-ray absorption. Protons have a high cross-section for gold at a large range of relevant clinical energies, and so potentially could be used with GNPs for increased therapeutic effect.Here, we investigate the contribution of secondary electron emission to cancer cell radiosensitization and investigate how this parameter is affected by proton energy and a free radical scavenger. We simulate the emission from a realistic cell phantom containing GNPs after traversal by protons and x-rays with different energies. We find that with a range of proton energies (1-250 MeV) there is a small increase in secondaries compared to a much larger increase with x-rays. Secondary electrons are known to produce toxic free radicals. Using a cancer cell line in vitro we find that a free radical scavenger has no protective effect on cells containing GNPs irradiated with 3 MeV protons, while it does protect against cells irradiated with x-rays. We conclude that GNP generated free radicals are a major cause of radiosensitization and that there is likely to be much less dose enhancement effect with clinical proton beams compared to x-rays.

Influence of the nucleus area distribution on the survival fraction after charged particles broad beam irradiation

It is well known that broad beam irradiation with heavy ions leads to variation in the number of hit(s) received by each cell as the distribution of particles follows the Poisson statistics. Although the nucleus area will determine the number of hit(s) received for a given dose, variation amongst its irradiated cell population is generally not considered. In this work, we investigate the effect of the nucleus area's distribution on the survival fraction. More specifically, this work aims to explain the deviation, or tail, which might be observed in the survival fraction at high irradiation doses. For this purpose, the nucleus area distribution was added to the beam Poisson statistics and the Linear-Quadratic model in order to fit the experimental data. As shown in this study, nucleus size variation, and the associated Poisson statistics, can lead to an upward survival trend after broad beam irradiation. The influence of the distribution parameters (mean area and standard deviation) was studied using a normal distribution, along with the Linear-Quadratic model parameters (α and β). Finally, the model proposed here was successfully tested to the survival fraction of LN18 cells irradiated with a 85 keV µm(- 1) carbon ion broad beam for which the distribution in the area of the nucleus had been determined.

"Broadbeam" irradiation of mammalian cells using a vertical microbeam facility

A "broadbeam" facility is demonstrated for the vertical microbeam at Surrey's Ion Beam Centre, validating the new technique used by Barazzuol et al. (Radiat Res 177:651-662, 2012). Here, droplets with a diameter of about 4 mm of 15,000 mammalian cells in suspension were pipetted onto defined locations on a 42-mm-diameter cell dish with each droplet individually irradiated in "broadbeam" mode with 2 MeV protons and 4 MeV alpha particles and assayed for clonogenicity. This method enables multiple experimental data points to be rapidly collected from the same cell dish. Initially, the Surrey vertical beamline was designed for the targeted irradiation of single cells with single counted ions. Here, the benefits of both targeted single-cell and broadbeam irradiations being available at the same facility are discussed: in particular, high-throughput cell irradiation experiments can be conducted on the same system as time-intensive focused-beam experiments with the added benefits of fluorescent microscopy, cell recognition and time-lapse capabilities. The limitations of the system based on a 2 MV tandem accelerator are also discussed, including the uncertainties associated with particle Poisson counting statistics, spread of linear energy transfer in the nucleus and a timed dose delivery. These uncertainties are calculated with Monte Carlo methods. An analysis of how this uncertainty affects relative biological effect measurements is made and discussed.

Cellular automaton model of cell response to targeted radiation

It has been shown that the response of cells to low doses of radiation is not linear and cannot be accurately extrapolated from the high dose response. To investigate possible mechanisms involved in the behaviour of cells under very low doses of radiation, a cellular automaton (CA) model was created. The diffusion and consumption of glucose in the culture dish were computed in parallel to the growth of cells. A new model for calculating survival probability was introduced; the communication between targeted and non-targeted cells was also included. Early results on the response of non-confluent cells to targeted irradiation showed the capability of the model to take account for the non-linear response in the low-dose domain.