UNASSIGNED: Magnetic Resonance Imaging (MRI) guided stereotactic body radiotherapy (SBRT) of liver metastases is an upcoming high-precision non-invasive treatment. Interobserver variation (IOV) in tumor delineation, however, remains a relevant uncertainty for planning target volume (PTV) margins. The aims of this study were to quantify IOV in MRI-based delineation of the gross tumor volume (GTV) of liver metastases and to detect patient-specific factors influencing IOV.
UNASSIGNED: A total of 22 patients with liver metastases from three primary tumor origins were selected (colorectal(8), breast(6), lung(8)). Delineation guidelines and planning MRI-scans were provided to eight radiation oncologists who delineated all GTVs. All delineations were centrally peer reviewed to identify outliers not meeting the guidelines. Analyses were performed both in- and excluding outliers. IOV was quantified as the standard deviation (SD) of the perpendicular distance of each observer\'s delineation towards the median delineation. The correlation of IOV with shape regularity, tumor origin and volume was determined.
UNASSIGNED: Including all delineations, average IOV was 1.6 mm (range 0.6-3.3 mm). From 160 delineations, in total fourteen single delineations were marked as outliers after peer review. After excluding outliers, the average IOV was 1.3 mm (range 0.6-2.3 mm). There was no significant correlation between IOV and tumor origin or volume. However, there was a significant correlation between IOV and regularity (Spearman\'s ρs = -0.66; p = 0.002).
UNASSIGNED: MRI-based IOV in tumor delineation of liver metastases was 1.3-1.6 mm, from which PTV margins for IOV can be calculated. Tumor regularity and IOV were significantly correlated, potentially allowing for patient-specific margin calculation.

BACKGROUND: Peer review (PR) of palliative-intent radiation plans is an important but understudied component of quality assurance. This retrospective review aims to improve our understanding of palliative PR by examining the characteristics of reviewed plans and peer feedback along with the associated time burden of two different types of PR processes.
METHODS: This single-institution, quality assurance project assessed palliative PR between 2018 and 2020. Initially, the PR involved a multi-disciplinary team PR. Subsequently, it transitioned to independent PR by a single physician. Characteristics of reviewed plans and feedback on PR were captured and abstracted. Time requirements of PR were based on self-reported estimates and attendance records.
RESULTS: A total of 1942 cases were reviewed, representing 85.7% (1942/2266) of all palliative-intent plans between 2018 and 2020. A total of 41.1% (n=799) were simple (2D/3D) radiation plans while 56.0% (n=1087) were complex (volumetric modulated arc therapy (VMAT) or tomotherapy) plans. Approximately one-third (30.4%, n=590) of all plans were stereotactic treatments. The rate of any peer feedback was 2.3% (n=45), while the rate of a specific recommended or implemented change was 1.2% (n=24) and 0.9% (n=18), respectively. PR before the start of treatment was associated with more frequent recommended (p=0.005) and implemented changes (p=0.008). Most other factors, including plan complexity and use of stereotactic radiation, were not predictive in this analysis. Comparing the independent versus team PR approach, there was no significant difference in recommended or implemented changes. The mean±standard deviation (SD) staff time required per plan reviewed was 36±6 and 37±6 minutes, including 21±6 and 10±6 minutes of physician time, for team and independent PR, respectively.
CONCLUSIONS: This work highlights the high frequency of complex and stereotactic radiation in the palliative setting, along with the importance of timely PR and the potential benefit of reviewing even simple, 2D/3D radiation plans. Additionally, from a process perspective, our work showed that independent PR may require less dedicated physician time.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples\' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

BACKGROUND: Quality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities.
OBJECTIVE: A present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry.
UNASSIGNED: Community engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a \"living guidance\" document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field.

OBJECTIVE: MatriXX ionization chamber array has been widely used for the composite dose verification of IMRT/VMAT plans. However, in addition to its dose response dependence on gantry angle, there seems to be an offset between the beam axis and measured dose profile by MatriXX for oblique beam incidence at various gantry angles, leading to unnecessary quality assurance (QA) fails. In this study, we investigated the offset at various setup conditions and how to eliminate or decrease it to improve the accuracy of MatriXX for IMRT/VMAT plan verification with original gantry angles.
METHODS: We measured profiles for a narrow beam with MatriXX located at various depths in increments of 0.5 mm from the top to bottom of the sensitive volume of the array detectors and gantry angles from 0° to 360°. The optimal depth for QA measurement was determined at the depth where the measured profile had minimum offset.
RESULTS: The measured beam profile offset varies with incident gantry angle, increasing from vertical direction to lateral direction, and could be over 3 cm at vendor-recommended depth for near lateral direction beams. The offset also varies with depth, and the minimum offset (almost 0 for most oblique beams) was found to be at a depth of ∼2.5 mm below the vendor suggested depth, which was chosen as the optimal depth for all QA measurements. Using the optimal depth we determined, QA results (3%/2 mm Gamma analysis) were largely improved with an average of 99.4% gamma passing rate (no fails for 95% criteria) for 10 IMRT and VMAT plans with original gantry angles compared to 94.1% using the vendor recommended depth.
CONCLUSIONS: The improved accuracy and passing rate for QA measurement performed at the optimal depth with original gantry angles would lead to reduction in unnecessary repeated QA or plan changes due to QA system errors.

The advent of Graphics Processing Units (GPU) has prompted the development of Monte Carlo (MC) algorithms that can significantly reduce the simulation time with respect to standard MC algorithms based on Central Processing Unit (CPU) hardware. The possibility to evaluate a complete treatment plan within minutes, instead of hours, paves the way for many clinical applications where the time-factor is important. FRED (Fast paRticle thErapy Dose evaluator) is a software that exploits the GPU power to recalculate and optimise ion beam treatment plans. The main goal when developing the FRED physics model was to balance accuracy, calculation time and GPU execution guidelines. Nowadays, FRED is already used as a quality assurance tool in Maastricht and Krakow proton clinical centers and as a research tool in several clinical and research centers across Europe. Lately the core software has been updated including a model of carbon ions interactions with matter. The implementation is phenomenological and based on carbon fragmentation data currently available. The model has been tested against the MC FLUKA software, commonly used in particle therapy, and a good agreement was found. In this paper, the new FRED data-driven model for carbon ion fragmentation will be presented together with the validation tests against the FLUKA MC software. The results will be discussed in the context of FRED clinical applications to 12C ions treatment planning.

We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist\'s report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice. Here we consider early indications of the potential of artificial intelligence as the ultimate quality assurance for radiologists.

10B-neutron capture was observed optically using a boron-added liquid scintillator. Trimethyl borate was dissolved in a commercially available liquid scintillator at natural boron concentrations of approximately 1 wt% and 0.25 wt%. The boron-added liquid scintillator was placed in a phantom quartz bottle and irradiated by thermal neutrons (~ 105 n/[cm2 s]) for 150, 300, and 600 s. The luminescence of the liquid scintillator was clearly observed using a cooled charge-coupled device (CCD) camera during irradiation. The luminance value recorded by the CCD camera was proportional to the duration of irradiation by thermal neutrons. The luminescence distribution showed reasonable agreement with that of energy deposition by Li and alpha particles from 10B-neutron capture reactions calculated via Monte Carlo simulations. When trimethyl borate was not dissolved in the liquid scintillator (0 wt% natural boron), no visible luminescence was observed even after 600 s of irradiation. These findings demonstrate that the observed luminance originates from the Li and alpha particles generated by 10B-neutron capture reactions. Consequently, the luminescence distribution is directly related to the boron dose of the liquid scintillator. To the best of our knowledge, direct experimental optical observations of boron dose distribution have not yet been reported. This novel technique will be useful for quality assurance in boron neutron capture therapy (BNCT) because instantaneous neutron irradiation may be sufficient for the observing the intense neutron beam used in clinical BNCT (~ 109 n/[cm2 s]), and quick evaluation of the boron dose distribution is expected to be feasible.

Magnetic resonance-guided focused ultrasound (MRgFUS) is a completely non-invasive technology that has been approved by FDA to treat several diseases. This report, prepared by the American Association of Physicist in Medicine (AAPM) Task Group 241, provides background on MRgFUS technology with a focus on clinical body MRgFUS systems. The report addresses the issues of interest to the medical physics community, specific to the body MRgFUS system configuration, and provides recommendations on how to successfully implement and maintain a clinical MRgFUS program. The following sections describe the key features of typical MRgFUS systems and clinical workflow and provide key points and best practices for the medical physicist. Commonly used terms, metrics and physics are defined and sources of uncertainty that affect MRgFUS procedures are described. Finally, safety and quality assurance procedures are explained, the recommended role of the medical physicist in MRgFUS procedures is described, and regulatory requirements for planning clinical trials are detailed. Although this report is limited in scope to clinical body MRgFUS systems that are approved or currently undergoing clinical trials in the United States, much of the material presented is also applicable to systems designed for other applications.

BACKGROUND: Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any single accelerator are unique and generally unknown, an appropriate model of an electron beam must be assumed before MC simulations can be run. The purpose of the present study is to develop a flexible framework with suitable regression models for estimating parameters of the model of primary electron beam in simulators of medical linear accelerators using real reference dose profiles measured in a water phantom.
METHODS: All simulations were run using PRIMO MC simulator. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The second model, based on deep learning, consists of a set of encoders processing measured dose profiles, followed by a sequence of fully connected layers acting together, which solve the regression problem of estimating values of the electron beam parameters directly from the measured dose profiles. Results of the regression are then used to reconstruct the dose profiles based on the PCA model. Agreement between the measured and reconstructed profiles can be further improved by an optimization procedure resulting in the final estimates of the parameters of the model of the primary electron beam. These final estimates are then used to determine dose profiles in MC simulations.
RESULTS: Analysed were a set of actually measured (real) dose profiles of 6 MV beams from a real Varian 2300 C/D accelerator, a set of simulated training profiles, and a separate set of simulated testing profiles, both generated for a range of parameters of the primary electron beam of the Varian 2300 C/D PRIMO simulator. Application of the two-stage procedure based on regression followed by reconstruction-based minimization of the difference between measured (real) and reconstructed profiles resulted in achieving consistent estimates of electron beam parameters and in a very good agreement between the measured and simulated photon beam profiles.
CONCLUSIONS: The proposed framework is a readily applicable and customizable tool which may be applied in tuning virtual primary electron beams of Monte Carlo simulators of linear accelerators. The codes, training and test data, together with readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam .