BackgroundThe accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT.MethodsFive clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA).ResultsFor all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency.ConclusionsImprovements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

To ensure the safe delivery of proton therapy treatments it is important to evaluate the effect of potential uncertainties, such as patient mispositioning, on the intended dose distribution. However, it can be expected that the uncertainty resulting from patient positioning is reduced in a fractionated treatment due to the convergence of random variables with the delivery of repeated treatments. This is neglected by current approaches to robustness analysis resulting in an overly conservative assessment of the robustness which can lead to sub-optimal plans. Here, a fast method of accounting for this reduced uncertainty is presented. An estimated bound to the error in the dose distribution resulting from setup uncertainty over a specified number of fractions is calculated by considering the distribution of values for each voxel across 14 initial error scenarios. The bound on the error in a given voxel is estimated using a 99.9% confidence limit assuming a convergence towards a normal distribution in line with the central limit theorem, and a correction of [Formula: see text] accounting for the reduction in the standard deviation over n fractions. The proposed method was validated in 5 patients by comparison to Monte Carlo simulations of 300 treatment courses. A voxelwise and volumetric analysis of the estimated and simulated bounds to the uncertainty in the dose distribution demonstrate that the proposed technique can be used to assess proton plan robustness more accurately allowing for less conservative treatment plans.

For radiotherapy, it is crucial to guarantee that the delivered dose matches the planned dose. Therefore, patient specific quality assurance (QA) of absolute dose distributions is necessary. Here, we investigate the potential of replacing patient specific QA for pencil beam scanned proton therapy with Monte Carlo simulations. First, the set-up of the automated Monte Carlo model is presented with an emphasis on the absolute dose validation. Second, the absolute dose results obtained from the Monte Carlo simulation for a comprehensive set of patient fields are compared to patient specific QA measurements. Absolute doses measured with the Farmer chamber are shown to be 1.4% higher than the doses measured with the Semiflex chamber. For single energy layers, Monte Carlo simulated doses are 2.1%  ±  0.4% lower than the ones measured with the ionization chamber and 1.1%  ±  1.0% lower than measurements compared to patient field verification measurements. After rescaling to account for this 1.1% discrepancy, 98 fields (94.2%) agree within 2% to measurements, the maximum difference being 2.3%. In conclusion, an automated, easy-to-use Monte Carlo calculation system has been set up. This system reproduced patient specific QA results over a wide range of cases, showing that the time consuming measurements could be reduced or even replaced using Monte Carlo simulations without jeopardizing treatment quality.

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.

Purpose: GATE-RTion is a validated version of GATE for clinical use in the field of Light Ion Beam Therapy. This paper describes the GATE-RTion project and illustrates its potential through clinical applications developed in three European centers delivering scanned proton and carbon ion treatments. Methods: GATE-RTion is a collaborative framework provided by the OpenGATE collaboration. It contains a validated GATE release based on a specific Geant4 version, a set of tools to integrate GATE into a clinical environment and a network for clinical users. Results: Three applications are presented: Proton radiography applications at the Centre Antoine Lacassagne (Nice, France); Independent dose calculation for proton therapy at the Christie NHS Foundation Trust (Manchester, UK); Independent dose calculation system for protons and carbon ions at the MedAustron Ion Therapy center (Wiener Neustadt, Austria). Conclusions: GATE-RTion builds the bridge between researchers and clinical users from the OpenGATE collaboration in the field of Light Ion Beam Therapy. The applications presented in three European facilities using three completely different machines (three different vendors, cyclotron and synchrotron-based systems, protons and carbon ions) demonstrate the relevance and versatility of this project.

Objective: We describe a model for evaluating the throughput capacity of a singleaccelerator multitreatment room proton therapy centre with the aims of (1) providing quantitative estimates of the throughput and waiting times and (2) providing insight into the sensitivity of the system to various physical parameters. Methods: A Monte Carlo approach was used to compute various statistics about the modelled centre, including the throughput capacity, fraction times for different groups of patients and beam waiting times. A method of quantifying the saturation level is also demonstrated. Results: Benchmarking against the MD Anderson Cancer Center showed good agreement between the modelled (140¡4 fractions per day) and reported (133¡35 fractions per day) throughputs. A sensitivity analysis of that system studied the impact of beam switch time, the number of treatment rooms, patient set-up times and the potential benefit of having a second accelerator. Finally, scenarios relevant to a potential UK facility were studied, finding that a centre with the same four-room, single-accelerator configuration as the MD Anderson Cancer Center but handling a more complex UK-type caseload would have a throughput reduced by approximately 19%, but still be capable of treating in excess of 100 fractions per 16-h treatment day. Conclusions: The model provides a useful tool to aid in understanding the operating dynamics of a proton therapy facility, and for investigating potential scenarios for prospective centres. Advances in knowledge: The model helps to identify which technical specifications should be targeted for future improvements. NRAG also recommended the formulation of a business case for development of proton therapy facilities within the UK [3]. As part of that work a good estimate of the throughput capacity is critical, both for cost implications [5] and for the ability to meet the clinical demand, especially for prospective centres in the UK where it is likely that demand will increase substantially over the next few years [6]. One of the options for development of proton therapy facilities in the UK suggested by Jones et al [7] was the construction of 3 or 4 centres capable of treating $1000 patients per year, and ensuring that planned centres are capable of meeting this demand is key to the development of proton facilities in the UK.Throughput estimates for a proton therapy centre cannot be based directly on estimates for a similarly sized photon centre, since the operation of proton and photon facilities are fundamentally different: because of the cost and size of proton therapy accelerators a number of clinical rooms are generally run from one accelerator (Figure 1), whereas for photon therapy each treatment room typically has a dedicated linear accelerator. Sharing

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.

Contouring structures in the head and neck is time‐consuming, and automatic segmentation is an important part of an adaptive radiotherapy workflow. Geometric accuracy of automatic segmentation algorithms has been widely reported, but there is no consensus as to which metrics provide clinically meaningful results. This study investigated whether geometric accuracy (as quantified by several commonly used metrics) was associated with dosimetric differences for the parotid and larynx, comparing automatically generated contours against manually drawn ground truth contours. This enabled the suitability of different commonly used metrics to be assessed for measuring automatic segmentation accuracy of the parotid and larynx. Parotid and larynx structures for 10 head and neck patients were outlined by five clinicians to create ground truth structures. An automatic segmentation algorithm was used to create automatically generated normal structures, which were then used to create volumetric‐modulated arc therapy plans. The mean doses to the automatically generated structures were compared with those of the corresponding ground truth structures, and the relative difference in mean dose was calculated for each structure. It was found that this difference did not correlate with the geometric accuracy provided by several metrics, notably the Dice similarity coefficient, which is a commonly used measure of spatial overlap. Surface‐based metrics provided stronger correlation and are, therefore, more suitable for assessing automatic segmentation of the parotid and larynx.PACS number(s): 87.57.nm, 87.55.D, 87.55.Qr