The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.

Molecular imaging is a key tool in the diagnosis and treatment of prostate cancer (PCa). Magnetic Resonance (MR) plays a major role in this respect with nuclear medicine imaging, particularly, Prostate-Specific Membrane Antigen-based, (PSMA-based) positron emission tomography with computed tomography (PET/CT) also playing a major role of rapidly increasing importance. Another key technology finding growing application across medicine and specifically in molecular imaging is the use of machine learning (ML) and artificial intelligence (AI). Several authoritative reviews are available of the role of MR-based molecular imaging with a sparsity of reviews of the role of PET/CT. This review will focus on the use of AI for molecular imaging for PCa. It will aim to achieve two goals: firstly, to give the reader an introduction to the AI technologies available, and secondly, to provide an overview of AI applied to PET/CT in PCa. The clinical applications include diagnosis, staging, target volume definition for treatment planning, outcome prediction and outcome monitoring. ML and AL techniques discussed include radiomics, convolutional neural networks (CNN), generative adversarial networks (GAN) and training methods: supervised, unsupervised and semi-supervised learning.

Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

A likelihood-based particle imaging filter using prior information.

BACKGROUND: Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium. PURPOSE: This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. METHODS: The Prior Filter uses Fermi-Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. RESULTS: The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method. CONCLUSIONS: The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.

Technical note: suppression of artifacts arising from simultaneous cone-beam imaging and RF transponder tracking in prostate radiotherapy.

PURPOSE: Artifacts in treatment-room cone-beam reconstructions have been observed at the authors' center when cone-beam acquisition is simultaneous with radio frequency (RF) transponder tracking using the Calypso 4D system (Calypso Medical, Seattle, WA). These artifacts manifest as CT-number modulations and increased CT-noise. The authors present a method for the suppression of the artifacts. METHODS: The authors propose a three-stage postprocessing technique that can be applied to image volumes previously reconstructed by a cone-beam system. The stages are (1) segmentation of voxels into air, soft-tissue, and bone; (2) application of a 2D spatial-filter in the axial plane to the soft-tissue voxels; and (3) normalization to remove streaking along the axial-direction. The algorithm was tested on patient data acquired with Synergy XVI cone-beam CT systems (Elekta, Crawley, United Kingdom). RESULTS: The computational demands of the suggested correction are small, taking less than 15 s per cone-beam reconstruction on a desktop PC. For a moderate loss of spatial-resolution, the artifacts are strongly suppressed and low-contrast visibility is improved. CONCLUSIONS: The correction technique proposed is fast and effective in removing the artifacts caused by simultaneous cone-beam imaging and RF-transponder tracking.

Early dietitian referral in lung cancer: use of machine learning.

OBJECTIVES: The Dietetic Assessment and Intervention in Lung Cancer (DAIL) study was an observational cohort study. It triaged the need for dietetic input in patients with lung cancer, using questionnaires with 137 responses. This substudy tested if machine learning could predict need to see a dietitian (NTSD) using 5 or 10 measures. METHODS: 76 cases from DAIL were included (Royal Surrey NHS Foundation Trust; RSH: 56, Frimley Park Hospital; FPH 20). Univariate analysis was used to find the strongest correlates with NTSD and 'critical need to see a dietitian' CNTSD. Those with a Spearman correlation above ±0.4 were selected to train a support vector machine (SVM) to predict NTSD and CNTSD. The 10 and 5 best correlates were evaluated. RESULTS: 18 and 13 measures had a correlation above ±0.4 for NTSD and CNTSD, respectively, producing SVMs with 3% and 7% misclassification error. 10 measures yielded errors of 7% (NTSD) and 9% (CNTSD). 5 measures yielded between 7% and 11% errors. SVM trained on the RSH data and tested on the FPH data resulted in errors of 20%. CONCLUSIONS: Machine learning can predict NTSD producing misclassification errors <10%. With further work, this methodology allows integrated early referral to a dietitian independently of a healthcare professional.

Imaging of moving fiducial markers during radiotherapy using a fast, efficient active pixel sensor based EPID.

PURPOSE: The purpose of this work was to investigate the use of an experimental complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) for tracking of moving fiducial markers during radiotherapy. METHODS: The APS has an active area of 5.4 × 5.4 cm and maximum full frame read-out rate of 20 frame s(-1), with the option to read out a region-of-interest (ROI) at an increased rate. It was coupled to a 4 mm thick ZnWO4 scintillator which provided a quantum efficiency (QE) of 8% for a 6 MV x-ray treatment beam. The APS was compared with a standard iViewGT flat panel amorphous Silicon (a-Si) electronic portal imaging device (EPID), with a QE of 0.34% and a frame-rate of 2.5 frame s(-1). To investigate the ability of the two systems to image markers, four gold cylinders of length 8 mm and diameter 0.8, 1.2, 1.6, and 2 mm were placed on a motion-platform. Images of the stationary markers were acquired using the APS at a frame-rate of 20 frame s(-1), and a dose-rate of 143 MU min(-1) to avoid saturation. EPID images were acquired at the maximum frame-rate of 2.5 frame s(-1), and a reduced dose-rate of 19 MU min(-1) to provide a similar dose per frame to the APS. Signal-to-noise ratio (SNR) of the background signal and contrast-to-noise ratio (CNR) of the marker signal relative to the background were evaluated for both imagers at doses of 0.125 to 2 MU. RESULTS: Image quality and marker visibility was found to be greater in the APS with SNR ∼5 times greater than in the EPID and CNR up to an order of magnitude greater for all four markers. To investigate the ability to image and track moving markers the motion-platform was moved to simulate a breathing cycle with period 6 s, amplitude 20 mm and maximum speed 13.2 mm s(-1). At the minimum integration time of 50 ms a tracking algorithm applied to the APS data found all four markers with a success rate of ≥92% and positional error ≤90 µm. At an integration time of 400 ms the smallest marker became difficult to detect when moving. The detection of moving markers using the a-Si EPID was difficult even at the maximum dose-rate of 592 MU min(-1) due to the lower QE and longer integration time of 400 ms. CONCLUSIONS: This work demonstrates that a fast read-out, high QE APS may be useful in the tracking of moving fiducial markers during radiotherapy. Further study is required to investigate the tracking of markers moving in 3D in a treatment beam attenuated by moving patient anatomy. This will require a larger sensor with ROI read-out to maintain speed and a manageable data-rate.

Statistical limitations in ion imaging.

In this study, we investigated the capacity of various ion beams available for radiotherapy to produce high quality relative stopping power map acquired from energy-loss measurements. The image quality metrics chosen to compare the different ions were signal-to-noise ratio (SNR) as a function of dose and spatial resolution. Geant4 Monte Carlo simulations were performed for: hydrogen, helium, lithium, boron and carbon ion beams crossing a 20 cm diameter water phantom to determine SNR and spatial resolution. It has been found that protons possess a significantly larger SNR when compared with other ions at a fixed range (up to 36% higher than helium) due to the proton nuclear stability and low dose per primary. However, it also yields the lowest spatial resolution against all other ions, with a resolution lowered by a factor 4 compared to that of carbon imaging, for a beam with the same initial range. When comparing for a fixed spatial resolution of 10 lp cm-1, carbon ions produce the highest image quality metrics with proton ions producing the lowest. In conclusion, it has been found that no ion can maximize all image quality metrics simultaneously and that a choice must be made between spatial resolution, SNR, and dose.

Statistical limitations in proton imaging.

Proton imaging is a promising technology for proton radiotherapy as it can be used for: (1) direct sampling of the tissue stopping power, (2) input information for multi-modality RSP reconstruction, (3) gold-standard calibration against concurrent techniques, (4) tracking motion and (5) pre-treatment positioning. However, no end-to-end characterization of the image quality (signal-to-noise ratio and spatial resolution, blurring uncertainty) against the dose has been done. This work aims to establish a model relating these characteristics and to describe their relationship with proton energy and object size. The imaging noise originates from two processes: the Coulomb scattering with the nucleus, producing a path deviation, and the energy loss straggling with electrons. The noise is found to increases with thickness crossed and, independently, decreases with decreasing energy. The scattering noise is dominant around high-gradient edge whereas the straggling noise is maximal in homogeneous regions. Image quality metrics are found to behave oppositely against energy: lower energy minimizes both the noise and the spatial resolution, with the optimal energy choice depending on the application and location in the imaged object. In conclusion, the model presented will help define an optimal usage of proton imaging to reach the promised application of this technology and establish a fair comparison with other imaging techniques.

Obtaining breathing patterns from any sequential thoracic x-ray image set.

A technique is presented to allow a breathing pattern to be obtained from any multi-slice CT, cone-beam or other series of sequential chest x-ray image sets. The technique requires no extra signals to be recorded and does not need specific external or internal oscillating structures to be visible in the field of view. The breathing pattern is instead acquired from analysing the variation in pixel values between projection images. For cone-beam image sets, slowly varying changes, due to an angular attenuation dependence, must be corrected before the breathing trace analysis can begin. All the results of the new technique were checked visually and were in good agreement. If the studied image set could be analysed using the existing 'Amsterdam shroud' technique, then the results it provided were also used for comparison. In cases that allowed comparison by both techniques, the results were in agreement. The new technique was also shown to provide a usable signal when applied to cardiac motion.

Recent developments in non-coplanar radiotherapy.

This paper gives an overview of recent developments in non-coplanar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Modern linear accelerators are capable of automating motion around multiple axes, allowing efficient delivery of highly non-coplanar radiotherapy techniques. Novel techniques developed for C-arm and non-standard linac geometries, methods of optimization, and clinical applications are reviewed. The additional degrees of freedom are shown to increase the therapeutic ratio, either through dose escalation to the target or dose reduction to functionally important organs at risk, by multiple research groups. Although significant work is still needed to translate these new non-coplanar radiotherapy techniques into the clinic, clinical implementation should be prioritized. Recent developments in non-coplanar radiotherapy demonstrate that it continues to have a place in modern cancer treatment.

Dosimetric accuracy of dynamic couch rotation during volumetric modulated arc therapy (DCR-VMAT) for primary brain tumours.

Radiotherapy treatment plans using dynamic couch rotation during volumetric modulated arc therapy (DCR-VMAT) reduce the dose to organs at risk (OARs) compared to coplanar VMAT, while maintaining the dose to the planning target volume (PTV). This paper seeks to validate this finding with measurements. DCR-VMAT treatment plans were produced for five patients with primary brain tumours and delivered using a commercial linear accelerator (linac). Dosimetric accuracy was assessed using point dose and radiochromic film measurements. Linac-recorded mechanical errors were assessed by extracting deviations from log files for multi-leaf collimator (MLC), couch, and gantry positions every 20 ms. Dose distributions, reconstructed from every fifth log file sample, were calculated and used to determine deviations from the treatment plans. Median (range) treatment delivery times were 125 s (123-133 s) for DCR-VMAT, compared to 78 s (64-130 s) for coplanar VMAT. Absolute point doses were 0.8% (0.6%-1.7%) higher than prediction. For coronal and sagittal films, respectively, 99.2% (96.7%-100%) and 98.1% (92.9%-99.0%) of pixels above a 20% low dose threshold reported gamma <1 for 3% and 3 mm criteria. Log file analysis showed similar gantry rotation root-mean-square error (RMSE) for VMAT and DCR-VMAT. Couch rotation RMSE for DCR-VMAT was 0.091° (0.086-0.102°). For delivered dose reconstructions, 100% of pixels above a 5% low dose threshold reported gamma <1 for 2% and 2 mm criteria in all cases. DCR-VMAT, for the primary brain tumour cases studied, can be delivered accurately using a commercial linac.

Defining the margins in the radical radiotherapy of non-small cell lung cancer (NSCLC) with active breathing control (ABC) and the effect on physical lung parameters.

BACKGROUND: The effectiveness of ABC has been traditionally measured as the reduction in internal margin (IM) within the planning target volume (PTV). Not to overestimate the benefit of ABC, the effect of patient movement during treatment also needs to be taken into account. We determined the IM and set-up error with ABC and the effect on physical lung parameters compared to standard margins used with free breathing. We also assessed interfraction oesophageal movement to determine a planning organ at risk volume (PRV). MATERIALS AND METHODS: Two sequential studies were performed using ABC in NSCLC patients suitable for radical radiotherapy (RT). Twelve out of 14 patients in Study 1 had tumours visible fluoroscopically and had intrafraction tumour movement assessed with and without ABC. Sixteen patients were recruited to Study 2 and had interfraction tumour movement measured using ABC in a moderate deep inspiration breath-hold, of these 7 patients also had interfraction oesophageal movement recorded. Interfraction movement was assessed by CT scan prior to and in the middle and final week of RT. Displacement of the tumour centre of mass and oesophageal borders relative to the first scan provided a measure of movement. Set-up error was measured in 9 patients treated with an in-house lung board adapted for the ABC device. Combining movement and set-up errors determined PTV and PRV margins with ABC. The effect of ABC on mean lung dose (MLD), lung V20 and V13 was calculated. RESULTS: ABC in a moderate deep inspiration breath-hold was tolerated in 25 out of 30 patients (83%) in Study 1 and 2. The random contribution of periodic tumour motion was reduced by 90% in the y direction with ABC compared to free-breathing. The magnitude of motion reduction was less in the x and z direction. Combining the systematic and random set-up error in quadrature with the systematic and random intrafraction and interfraction tumour variations with ABC results in a PTV margin of 8.3mm in the x direction, 12.0mm in the y direction and 9.8mm in the z direction. There was a relative mean reduction in MLD, lung V20 and V13 of 25%, 21% and 18% with the ABC PTV compared to a free-breathing PTV. Oesophageal movement combined with set-up error resulted in an isotropic PRV of 4.7 mm. CONCLUSIONS: The reduction in PTV size with ABC resulted in an 18-25% relative reduction in physical lung parameters. PTV margin reduction has the potential to spare normal lung and allow dose-escalation if coupled with image-guided RT. The oesophageal PRV needs to be considered when irradiating central disease and is of increasing importance with altered RT fractionation and concomitant chemoradiation schedules. Further reductions in PTV and PRV may be possible if patient set-up error was minimised, confirming that attention to patient immobilisation is as important as attempts to control tumour motion.

Anatomical imaging for radiotherapy.

The goal of radiation therapy is to achieve maximal therapeutic benefit expressed in terms of a high probability of local control of disease with minimal side effects. Physically this often equates to the delivery of a high dose of radiation to the tumour or target region whilst maintaining an acceptably low dose to other tissues, particularly those adjacent to the target. Techniques such as intensity modulated radiotherapy (IMRT), stereotactic radiosurgery and computer planned brachytherapy provide the means to calculate the radiation dose delivery to achieve the desired dose distribution. Imaging is an essential tool in all state of the art planning and delivery techniques: (i) to enable planning of the desired treatment, (ii) to verify the treatment is delivered as planned and (iii) to follow-up treatment outcome to monitor that the treatment has had the desired effect. Clinical imaging techniques can be loosely classified into anatomic methods which measure the basic physical characteristics of tissue such as their density and biological imaging techniques which measure functional characteristics such as metabolism. In this review we consider anatomical imaging techniques. Biological imaging is considered in another article. Anatomical imaging is generally used for goals (i) and (ii) above. Computed tomography (CT) has been the mainstay of anatomical treatment planning for many years, enabling some delineation of soft tissue as well as radiation attenuation estimation for dose prediction. Magnetic resonance imaging is fast becoming widespread alongside CT, enabling superior soft-tissue visualization. Traditionally scanning for treatment planning has relied on the use of a single snapshot scan. Recent years have seen the development of techniques such as 4D CT and adaptive radiotherapy (ART). In 4D CT raw data are encoded with phase information and reconstructed to yield a set of scans detailing motion through the breathing, or cardiac, cycle. In ART a set of scans is taken on different days. Both allow planning to account for variability intrinsic to the patient. Treatment verification has been carried out using a variety of technologies including: MV portal imaging, kV portal/fluoroscopy, MVCT, conebeam kVCT, ultrasound and optical surface imaging. The various methods have their pros and cons. The four x-ray methods involve an extra radiation dose to normal tissue. The portal methods may not generally be used to visualize soft tissue, consequently they are often used in conjunction with implanted fiducial markers. The two CT-based methods allow measurement of inter-fraction variation only. Ultrasound allows soft-tissue measurement with zero dose but requires skilled interpretation, and there is evidence of systematic differences between ultrasound and other data sources, perhaps due to the effects of the probe pressure. Optical imaging also involves zero dose but requires good correlation between the target and the external measurement and thus is often used in conjunction with an x-ray method. The use of anatomical imaging in radiotherapy allows treatment uncertainties to be determined. These include errors between the mean position at treatment and that at planning (the systematic error) and the day-to-day variation in treatment set-up (the random error). Positional variations may also be categorized in terms of inter- and intra-fraction errors. Various empirical treatment margin formulae and intervention approaches exist to determine the optimum strategies for treatment in the presence of these known errors. Other methods exist to try to minimize error margins drastically including the currently available breath-hold techniques and the tracking methods which are largely in development. This paper will review anatomical imaging techniques in radiotherapy and how they are used to boost the therapeutic benefit of the treatment.

PRaVDA: The first solid-state system for proton computed tomography.

PURPOSE: Proton CT is widely recognised as a beneficial alternative to conventional X-ray CT for treatment planning in proton beam radiotherapy. A novel proton CT imaging system, based entirely on solid-state detector technology, is presented. Compared to conventional scintillator-based calorimeters, positional sensitive detectors allow for multiple protons to be tracked per read out cycle, leading to a potential reduction in proton CT scan time. Design and characterisation of its components are discussed. An early proton CT image obtained with a fully solid-state imaging system is shown and accuracy (as defined in Section IV) in Relative Stopping Power to water (RSP) quantified. METHOD: A solid-state imaging system for proton CT, based on silicon strip detectors, has been developed by the PRaVDA collaboration. The system comprises a tracking system that infers individual proton trajectories through an imaging phantom, and a Range Telescope (RT) which records the corresponding residual energy (range) for each proton. A back-projection-then-filtering algorithm is used for CT reconstruction of an experimentally acquired proton CT scan. RESULTS: An initial experimental result for proton CT imaging with a fully solid-state system is shown for an imaging phantom, namely a 75 mm diameter PMMA sphere containing tissue substitute inserts, imaged with a passively-scattered 125 MeV beam. Accuracy in RSP is measured to be ⩽1.6% for all the inserts shown. CONCLUSIONS: A fully solid-state imaging system for proton CT has been shown capable of imaging a phantom with protons and successfully improving RSP accuracy. These promising results, together with system the capability to cope with high proton fluences (2×108 protons/s), suggests that this research platform could improve current standards in treatment planning for proton beam radiotherapy.

Calculation of x-ray spectra emerging from an x-ray tube. Part I. electron penetration characteristics in x-ray targets.

The penetration characteristics of electron beams into x-ray targets are investigated for incident electron kinetic energies in the range 50-150 keV. The frequency densities of electrons penetrating to a depth x in a target, with a fraction of initial kinetic energy, u, are calculated using Monte Carlo methods for beam energies of 50, 80, 100, 120 and 150 keV in a tungsten target. The frequency densities for 100 keV electrons in Al, Mo and Re targets are also calculated. A mixture of simple modeling with equations and interpolation from data is used to generalize the calculations in tungsten. Where possible, parameters derived from the Monte Carlo data are compared to experimental measurements. Previous electron transport approximations in the semiempirical models of other authors are discussed and related to this work. In particular, the crudity of the use of the Thomson-Whiddington law to describe electron penetration and energy loss is highlighted. The results presented here may be used towards calculating the target self-attenuation correction for bremsstrahlung photons emitted within a tungsten target.

Monte Carlo and Lambertian light guide models of the light output from scintillation crystals at megavoltage energies.

A new model of the light output from single-crystal scintillators in megavoltage energy x-ray beams has been developed, based on the concept of a Lambertian light guide model (LLG). This was evaluated in comparison with a Monte Carlo (MC) model of optical photon transport, previously developed and reported in the literature, which was used as a gold standard. The LLG model was developed to enable optimization of scintillator detector design. In both models the dose deposition and light propagation were decoupled, the scintillators were cuboids, split into a series of cells as a function of depth, with Lambertian side and entrance faces, and a specular exit face. The signal in a sensor placed 1 and 1000 mm beyond the exit face was calculated. Cesium iodide (CSI) crystals of 1.5 and 3 mm square cross section and 1, 5, and 10 mm depth were modeled. Both models were also used to determine detector signal and optical gain factor as a function of CsI scintillator thickness, from 2 to 10 mm. Results showed a variation in light output with position of dose deposition of a factor of up to approximately 5, for long, thin scintillators (such as 10 X 1.5 x 1.5 mm3). For short, fat scintillators (such as 1 X 3 X 3 mm3) the light output was more uniform with depth. MC and LLG generally agreed to within 5%. Results for a sensor distance of 1 mm showed an increase in light output the closer the light originates to the exit face, while a distance of 1000 mm showed a decrease in light output the closer the light originates to the exit face. For a sensor distance of 1 mm, the ratio of signal for a 10 mm scintillator to that for a 2 mm scintillator was 1.98, whereas for the 1000 mm distance the ratio was 3.00. The ratio of quantum efficiency (QE) between 10 and 2 mm thicknesses was 4.62. We conclude that these models may be used for detector optimization, with the light guide model suitable for parametric study.

Effects of averaging over motion and the resulting systematic errors in radiation therapy.

The potential for systematic errors in radiotherapy of a breathing patient is considered using the statistical model of Bortfeld et al (2002 Phys. Med. Biol. 47 2203-20). It is shown that although averaging over 30 fractions does result in a narrow Gaussian distribution of errors, as predicted by the central limit theorem, the fact that one or a few samples of the breathing patient's motion distribution are used for treatment planning (in contrast to the many treatment fractions that are likely to be delivered) may result in a much larger error with a systematic component. The error distribution may be particularly large if a scan at breath-hold is used for planning.

Omarigliptin for the treatment of type 2 diabetes mellitus.

INTRODUCTION: The estimated global prevalence of diabetes mellitus for adults aged 20-70 in 2015 was 415 million with approximately 90% of diagnosed cases being Type 2 diabetes mellitus (T2DM). Improvements in lifestyle and effective therapies are key to management but due to the progressive nature of T2DM, pharmacotherapy is typically required. Whilst the initial therapy will usually be with metformin, thereafter treatment should be individualised, with consideration of several different second line options. These include the dipeptidyl peptidase-4 (DPP-4) inhibitors, of which omarigliptin is the second once weekly version. AREAS COVERED: The paper summarises key pharmoacodynamic and pharmacokinetic features and reviews the efficacy and safety trial data of omarigliptin, a once-weekly DPP-4 inhibitor. EXPERT OPINION: Omarigliptin results in a significant improvement in glycaemia with an effective once weekly pharmacokinetic profile and low risk of drug-drug interactions. It has equivalent efficacy to existing once daily DPP-4 inhibitors and shares a similar side effect profile. It is weight neutral with a significantly lower risk of hypoglycaemia compared with sulphonylureas. Adherence to prescribed medication is poor in patients with T2DM. Once weekly omarigliptin is a welcomed addition to the therapeutic armoury but whether it will improve compliance remains to be seen.

Mean heart dose variation over a course of breath-holding breast cancer radiotherapy.

OBJECTIVE: The purpose of the work was to estimate the dose received by the heart throughout a course of breath-holding breast radiotherapy. METHODS: 113 cone-beam CT (CBCT) scans were acquired for 20 patients treated within the HeartSpare 1A study, in which both an active breathing control (ABC) device and a voluntary breath-hold (VBH) method were used. Predicted mean heart doses were obtained from treatment plans. CBCT scans were imported into a treatment planning system, heart outlines defined, images registered to the CT planning scan and mean heart dose recorded. Two observers outlined two cases three times each to assess interobserver and intraobserver variation. RESULTS: There were no statistically significant differences between ABC and VBH heart dose data from CT planning scans, or in the CBCT-based estimates of heart dose, and no effect from the order of the breath-hold method. Variation in mean heart dose per fraction over the three imaged fractions was <6 cGy without setup correction, decreasing to 3.3 cGy with setup correction. If scaled to 15 fractions, all differences between predicted and estimated mean heart doses were <0.5 Gy and in 80% of cases, they were <0.25 Gy. CONCLUSION: Variation in mean heart dose was at an acceptable level over the duration of breath-holding radiotherapy and was well predicted by the planning system. Advances in knowledge: Mean heart dose was not adversely affected by fraction-to-fraction variations throughout a course of heart-sparing radiotherapy using two well-established breath-holding methods.

Non-coplanar trajectories to improve organ at risk sparing in volumetric modulated arc therapy for primary brain tumors.

BACKGROUND AND PURPOSE: To evaluate non-coplanar volumetric modulated arc radiotherapy (VMAT) trajectories for organ at risk (OAR) sparing in primary brain tumor radiotherapy. MATERIALS AND METHODS: Fifteen patients were planned using coplanar VMAT and compared against non-coplanar VMAT plans for three trajectory optimization techniques. A geometric heuristic technique (GH) combined beam scoring and Dijkstra's algorithm to minimize the importance-weighted sum of OAR volumes irradiated. Fluence optimization was used to perform a local search around coplanar and GH trajectories, producing fluence-based local search (FBLS) and FBLS+GH trajectories respectively. RESULTS: GH, FBLS, and FBLS+GH trajectories reduced doses to the contralateral globe, optic nerve, hippocampus, temporal lobe, and cochlea. However, FBLS increased dose to the ipsilateral lens, optic nerve and globe. Compared to GH, FBLS+GH increased dose to the ipsilateral temporal lobe and hippocampus, contralateral optics, and the brainstem and body. GH and FBLS+GH trajectories reduced bilateral hippocampi normal tissue complication probability (p=0.028 and p=0.043, respectively). All techniques reduced PTV conformity; GH and FBLS+GH trajectories reduced homogeneity but less so for FBLS+GH. CONCLUSIONS: The geometric heuristic technique best spared OARs and reduced normal tissue complication probability, however incorporating fluence information into non-coplanar trajectory optimization maintained PTV homogeneity.