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To fully automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques.We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4-field-box (4-field-box), 3D conformal radiotherapy (3D-CRT), and volumetric modulated arc therapy (VMAT). These auto-planning algorithms were combined with a previously developed auto-contouring system. To improve the quality of the 4-field-box and 3D-CRT plans, we used an in-house, field-in-field (FIF) automation program. Thirty-five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5-point Likert scale.Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4-field-box, 3D-CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference.Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource-constrained radiotherapy departments in low- and middle-income countries.

This study aimed to highlight the value and key findings of on-site proton audits.The authors performed 38 on-site measurement-based peer reviews of proton centers participating in National Cancer Institute-funded clinical trials. The reviews covered beam calibration, lateral and depth measurements, mechanical checks, treatment planning and clinical practice, and quality assurance (QA) practices. Program deficiencies were noted, and recommendations were made about ways institutions could improve their practices.Institutions received an average of 3 (range, 1-8) recommendations for practice improvements. The number of deficiencies did not decrease over time, highlighting the continued need for this type of peer review. The most common deficiencies were for Task Group-recommended QA compliance (97% of centers), computed tomography number (CTN) to relative linear stopping power conversion (59%), and QA procedures (53%). In addition, 32% of institutions assessed failed at least 1 lateral beam profile measurement (90% of pixels passing 3% [global]/3 mm; 10% threshold), despite passing internal QA measurements. These failures occurred for several different plan configurations (large, small, shallow, and deep targets) and at different depths in the beam path (proximal to target, central, and distal). CTN to relative linear stopping power conversion curves showed deviations at low, mid, and high CTNs and highlighted areas of inconsistency between proton centers, with many centers falling outside of 2 sigma of the mean curve of their peers. All deficiencies from the peer review were discussed with the institutions, and many implemented dosimetric treatment planning and practice changes to improve the accuracy of their system and consistency with other institutions.This peer review program has been integral in confirming and promoting consistency and best practice across proton centers for clinical trials, minimizing deviations for outcomes data.

Purpose Quality assurance (QA) practices improve the quality level of oncology trials by ensuring that the protocol is followed and the results are valid and reproducible. This study investigated the utilization of QA among randomized controlled trials that involve radiotherapy (RT). Methods and Materials We searched ClinicalTrials.gov in February 2020 for all phase III oncology randomized clinical trials (RCTs). These trials were screened for RT-specific RCTs that had published primary trial results. Information regarding QA in each trial was collected from the study publications and trial protocol if available. Two individuals independently performed trial screening and data collection. Pearson’s Chi-square tests analyses were used to assess factors that were associated with QA inclusion in RT trials. Results Forty-two RCTs with RT as the primary intervention or as a mandatory component of the protocol were analyzed; the earliest was started in 1994 and one trial was still active though not recruiting. Twenty-nine (69%) trials mandated RT quality assurance (RTQA) practices as part of the trial protocol, with 19 (45%) trials requiring institutional credentialing. Twenty-one (50%) trials published protocol deviation outcomes. Clinical trials involving advanced radiation techniques (IMRT, VMAT, SRS, SBRT) did not include more RTQA than trials without these advanced techniques (73% vs. 65%, p=0.55). Trials that reported protocol deviation outcomes were associated with mandating RTQA in their protocols as compared to trials that did not report these outcomes (100% vs. 38%, p Conclusions There is a lack of RTQA utilization and transparency in RT clinical trials. It is imperative for RT trials to include increased QA for safe, consistent, and high-quality RT planning and delivery.

BACKGROUND AND PURPOSE We previously evaluated late cardiac disease in long-term survivors in the Childhood Cancer Survivor Study (CCSS) based on heart radiation therapy (RT) doses estimated from an age-scaled phantom with a simple atlas-based heart model (HAtlas). We enhanced our phantom with a high-resolution CT-based anatomically realistic and validated age-scalable cardiac model (HHybrid). We aimed to evaluate how this update would impact our prior estimates of RT-related late cardiac disease risk in the CCSS cohort. METHODS We evaluated 24,214 survivors from the CCSS diagnosed from 1970 to 1999. RT fields were reconstructed on an age-scaled phantom with HHybrid and mean heart dose (Dm), percent volume receiving ≥ 20 Gy (V20) and ≥ 5 Gy with V20 = 0 ( [Formula: see text] ) were calculated. We reevaluated cumulative incidences and adjusted relative rates of grade 3-5 Common Terminology Criteria for Adverse Events outcomes for any cardiac disease, coronary artery disease (CAD), and heart failure (HF) in association with Dm, V20, and [Formula: see text] (as categorical variables). Dose-response relationships were evaluated using piecewise-exponential models, adjusting for attained age, sex, cancer diagnosis age, race/ethnicity, time-dependent smoking history, diagnosis year, and chemotherapy exposure and doses. For relative rates, Dm was also considered as a continuous variable. RESULTS Consistent with previous findings with HAtlas, reevaluation using HHybrid dosimetry found that, Dm ≥ 10 Gy, V20 ≥ 0.1%, and [Formula: see text] ≥ 50% were all associated with increased cumulative incidences and relative rates for any cardiac disease, CAD, and HF. While updated risk estimates were consistent with previous estimates overall without statistically significant changes, there were some important and significant (P

The field of human space travel is in the midst of a dramatic revolution. Upcoming missions are looking to push the boundaries of space travel, with plans to travel for longer distances and durations than ever before. Both the National Aeronautics and Space Administration (NASA) and several commercial space companies (e.g., Blue Origin, SpaceX, Virgin Galactic) have already started the process of preparing for long-distance, long-duration space exploration and currently plan to explore inner solar planets (e.g., Mars) by the 2030s. With the emergence of space tourism, space travel has materialized as a potential new, exciting frontier of business, hospitality, medicine, and technology in the coming years. However, current evidence regarding human health in space is very limited, particularly pertaining to short-term and long-term space travel. This review synthesizes developments across the continuum of space health including prior studies and unpublished data from NASA related to each individual organ system, and medical screening prior to space travel. We categorized the extraterrestrial environment into exogenous (e.g., space radiation and microgravity) and endogenous processes (e.g., alteration of humans’ natural circadian rhythm and mental health due to confinement, isolation, immobilization, and lack of social interaction) and their various effects on human health. The aim of this review is to explore the potential health challenges associated with space travel and how they may be overcome in order to enable new paradigms for space health, as well as the use of emerging Artificial Intelligence based (AI) technology to propel future space health research.

Interplanetary space radiation presents significant hazards to astronaut health and survival because it can cause both acute and chronic health effects including carcinogenesis. Mitigation of space radiation risks remains critical for enabling human missions beyond Earth orbit. Currently available options for space radiation shielding, such as water shielding, may not be sufficient at reducing space radiation dose and cancer risk to within current regulatory limits. Active magnetic shielding using superconductors has consistently been identified for further study due to its high potential benefit. In response, we have developed Monte Carlo models to determine the effectiveness of several of the latest magnetic shielding concepts, along with current passive shielding techniques, in terms of risk of exposure induced death. This was done for various Mars flyby mission profiles according to current space agency risk tolerance limits. Our study found that few shielding options were able to meet current risk tolerance limits without relying on astronauts of advanced age (>60 years). However, all of the magnetic shielding configurations provided substantial benefit in reducing space radiation cancer risk given high magnetic field strength (7 Tesla). With this information, space agencies can move towards engineering assessments of magnetic shielding technology and begin to advance the concept into a solution enabling interplanetary travel.

Purpose To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. Methods An auto‐contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web‐based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN‐based auto‐contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen‐dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician‐drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals. Results The average DSC, mean surface distance, and Hausdorff distance of our CNN‐based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review. Conclusions Our CNN‐based auto‐contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability.

Background and purpose The Global Quality Assurance of Radiation Therapy Clinical Trials Harmonization Group (GHG) is a collaborative group of Radiation Therapy Quality Assurance (RTQA) Groups harmonizing and improving RTQA for multi-institutional clinical trials. The objective of the GHG OAR Working Group was to unify OAR contouring guidance across RTQA groups by compiling a single reference list of OARs in line with AAPM TG 263 and ASTRO, together with peer-reviewed, anatomically defined contouring guidance for integration into clinical trial protocols independent of the radiation therapy delivery technique. Materials and methods The GHG OAR Working Group comprised of 22 multi-professional members from 6 international RTQA Groups and affiliated organizations conducted the work in 3 stages: (1) Clinical trial documentation review and identification of structures of interest (2) Review of existing contouring guidance and survey of proposed OAR contouring guidance (3) Review of survey feedback with recommendations for contouring guidance with standardized OAR nomenclature. Results 157 clinical trials were examined; 222 OAR structures were identified. Duplicates, non-anatomical, non-specific, structures with more specific alternative nomenclature, and structures identified by one RTQA group were excluded leaving 58 structures of interest. 6 OAR descriptions were accepted with no amendments, 41 required minor amendments, 6 major amendments, 20 developed as a result of feedback, and 5 structures excluded in response to feedback. The final GHG consensus guidance includes 73 OARs with peer-reviewed descriptions (Appendix A). Conclusion We provide OAR descriptions with standardized nomenclature for use in clinical trials. A more uniform dataset supports the delivery of clinically relevant and valid conclusions from clinical trials.

Purpose Treatment planning system (TPS) dose calculations have previously been shown to be sensitive to modeling errors, especially when treating with complex strategies like intensity‐modulated radiation therapy (IMRT). This work investigates the dosimetric impact of several dosimetric and nondosimetric beam modeling parameters, based on their distribution in the radiotherapy community, in two commercial TPSs in order to understand the realistic potential for dose deviations and their clinical effects. Methods and materials Beam models representing standard 120‐leaf Varian Clinac‐type machines were developed in Eclipse 13.5 (AAA algorithm) and RayStation 9A (v8.99, collapsed‐cone algorithm) based upon median values of dosimetric measurements from Imaging and Radiation Oncology Core (IROC) Houston site visit data and community beam modeling parameter survey data in order to represent a baseline linear accelerator. Five clinically acceptable treatment plans (three IMRT, two VMAT) were developed for the IROC head and neck phantom. Dose distributions for each plan were recalculated after individually modifying parameters of interest (e.g., MLC transmission, percent depth doses [PDDs], and output factors) according to the 2.5th to 97.5th percentiles of community survey and machine performance data to encompass the realistic extent of variance in the radiotherapy community. The resultant dose distributions were evaluated by examining relative changes in average dose for thermoluminescent dosimeter (TLD) locations across the two target volumes and organ at risk (OAR). Interplay was also examined for parameters generating changes in target dose greater than 1%. Results For Eclipse, dose calculations were sensitive to changes in the dosimetric leaf gap (DLG), which resulted in differences from −5% to +3% to the targets relative to the baseline beam model. Modifying the MLC transmission factor introduced differences up to ± 1%. For RayStation, parameters determining MLC behaviors likewise contributed substantially; the MLC offset introduced changes in dose from −4% to +7%, and the MLC transmission caused changes of −4% to +2%. Among the dosimetric qualities examined, changes in PDD implementation resulted in the most substantial changes, but these were only up to ±1%. Other dosimetric factors had

The purposes of this study were to develop and integrate a colorectal model that incorporates anatomical variations of pediatric patients into the age-scalable MD Anderson Late Effects (MDA-LE) computational phantom, and validate the model for pediatric radiation therapy (RT) dose reconstructions.Colorectal contours were manually derived from whole-body non-contrast computed tomography (CT) scans of 114 pediatric patients (age range: 2.1-21.6 years, 74 males, 40 females). One contour was used for an anatomical template, 103 for training and 10 for testing. Training contours were used to create a colorectal principal component analysis (PCA)-based statistical shape model (SSM) to extract the population's dominant deformations. The SSM was integrated into the MDA-LE phantom. Geometric accuracy was assessed between patient-specific and SSM contours using several overlap metrics. Two alternative colorectal shapes were generated using the first 17 dominant modes of the PCA-based SSM. Dosimetric accuracy was assessed by comparing colorectal doses from test patients' CT-based RT plans (ground truth) with reconstructed doses for the mean and two alternative models in age-matched MDA-LE phantoms.When using all 103 PCA modes, the mean (min-max) Dice similarity coefficient, distance-to-agreement and Hausdorff distance between the patient-specific and reconstructed contours for the test patients were 0.89 (0.85-0.91), 2.1 mm (1.7-3.0), and 8.6 mm (5.7-14.3), respectively. The average percent difference between reconstructed and ground truth mean and maximum colorectal doses for the mean (alternative 1, 2) model were 6.3% (8.1%, 6.1%) and 4.4% (4.3%, 4.7%), respectively.We developed, validated and integrated a colorectal PCA-based SSM into the MDA-LE phantom and demonstrated its dosimetric performance for accurate pediatric RT dose reconstruction.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guidelines and technical standards by those entities not providing these services is not authorized. The following terms are used in the AAPM practice guidelines: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. While must is the term to be used in the guidelines, if an entity that adopts the guideline has shall as the preferred term, the AAPM considers that must and shall have the same meaning. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances.

To determine the most accurate similarity metric when using an independent system to verify automatically generated contours.A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para-aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans. Eleven similarity metrics (volumetric Dice similarity coefficient (DSC), Hausdorff distance, 95% Hausdorff distance, mean surface distance, and surface DSC with tolerances from 1 to 10 mm) were calculated between the reference and the verification autocontours, and between the manually generated and the verification autocontours. A support vector machine (SVM) was used to determine the threshold that separates clinically acceptable and unacceptable contours for each structure. The 11 metrics were investigated individually and in certain combinations. Linear, radial basis function, sigmoid, and polynomial kernels were tested using the combinations of metrics as inputs for the SVM.The highest contouring error detection accuracies were 0.91 for the UteroCervix, 0.90 for the CTVn, 0.89 for the PAN, 0.92 for the bladder, 0.95 for the rectum, and 0.97 for the kidneys and were achieved using surface DSCs with a thickness of 1, 2, or 3 mm. The linear kernel was the most accurate and consistent when a combination of metrics was used as an input for the SVM. However, the best model accuracy from the combinations of metrics was not better than the best model accuracy from a surface DSC as an input.We distinguished clinically acceptable contours from clinically unacceptable contours with an accuracy higher than 0.9 for the targets and critical structures in patients with cervical cancer; the most accurate similarity metric was surface DSC with a thickness of 1, 2, or 3 mm.

To provide percentage depth dose (PDD) data along the central axis for dosimetry calculations in small-animal radiation biology experiments performed in cabinet irradiators. The PDDs are provided as a function of source-to-surface distance (SSD), field size, and animal size.The X-ray tube designs for four biological cabinet irradiators, the RS2000, RT250, MultiRad350, and XRAD320, were simulated using the BEAMnrc Monte Carlo code to generate 160, 200, 250, and 320 kVp photon beams, respectively. The 320 kVp beam was simulated with two filtrations: a soft F1 aluminium filter and a hard F2 thoraeus filter made of aluminium, tin, and copper. Beams were collimated into circular fields with diameters of 0.5-10 cm at SSDs of 10-60 cm. Monte Carlo dose calculations in 1-5-cm diameter homogeneous (soft tissue) small-animal phantoms as well as in heterogeneous phantoms with 3-mm diameter cylindrical lung and bone inserts (rib and cortical bone) were performed using DOSXYZnrc. The calculated depth doses in three test-cases were estimated by applying SSD, field size, and animal size correction factors to a reference case (40-cm SSD, 1-cm field, and 5-cm animal size), and these results were compared with the specifically simulated (i.e., expected) doses to assess the accuracy of this method. Dosimetry for two test-case scenarios of 160 and 250 kVp beams (representative of end-user beam qualities) was also performed, whereby the simulated PDDs at two different depths were compared with the results based on the interpolation from reference data.The depth doses for three test-cases calculated at 200, 320 kVp F1, and 320 kVp F2 with half value layers (HVLs) ranging from ∼0.6 to 3.6 mm Cu, agreed well with the expected doses, yielding dose differences of 1.2%, 0.1%, and 1.0%, respectively. The two end-user test-cases for 160 and 250 kVp beams with respective HVLs of ∼0.8 and 1.8 mm Cu yielded dose differences of 1.4% and 3.2% between the simulated and the interpolated PDDs. The dose increase at the bone-tissue proximal interface ranged from 1.2 to 2.5 times the dose in soft tissue for rib and 1.3 to 3.7 times for cortical bone. The dose drop-off at 1-cm depth beyond the bone ranged from 1.3% to 6.0% for rib and 3.2% to 11.7% for cortical bone. No drastic dose perturbations occurred in the presence of lung, with lung-tissue interface dose of 99% of soft tissue dose and 3% dose increase at 1-cm depth beyond lung.The developed dose estimation method can be used to translate the measured dose at a point to dose at any depth in small-animal phantoms, making it feasible for preclinical calculation of dose distributions in animals irradiated with cabinet-style irradiators. The dosimetric impact of bone must be accurately quantified as dramatic dose perturbations at and beyond the bone interfaces can occur due to the relative importance of the photoelectric effect at kilovoltage energies. These results will help improve dosimetric accuracy in preclinical experiments.

Independent verification of the dose per monitor unit (MU) to deliver the prescribed dose to a patient has been a mainstay of radiation oncology quality assurance. We discuss the role of secondary dose/MU calculation programs as part of a comprehensive Quality Assurance (QA) program. This report provides guidelines on calculation-based dose/MU verification for intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) provided by various modalities. We provide a review of various algorithms for "independent/second check" of monitor unit calculations for IMRT/VMAT. The report makes recommendations on the clinical implementation of secondary dose/MU calculation programs; on commissioning and acceptance of various commercially available secondary dose/MU calculation programs; on benchmark QA and periodic quality assurance; and on clinically reasonable action levels for agreement of secondary dose/MU calculation programs.

BACKGROUND & PURPOSE To evaluate treatment planning system (TPS) beam modeling parameters as contributing factors to IMRT audit performance. MATERIALS & METHODS We retrospectively analyzed IROC Houston phantom audit performance and concurrent beam modeling survey responses from 337 irradiations performed between August 2017 and November 2019. Irradiation results were grouped based on the reporting of typical or atypical beam modeling parameter survey responses ( 90th percentile values), and compared for passing versus failing (>7% error) or "poor" (>5% error) irradiation status. Additionally, we assessed the impact on the planned dose distribution from variations in modeling parameter value. Finally, we estimated the overall impact of beam modeling parameter variance on dose calculations, based on reported community variations. RESULTS Use of atypical modeling parameters were more frequently seen with failing phantom audit results (p = 0.01). Most pronounced was for Eclipse AAA users, where phantom irradiations with atypical values of dosimetric leaf gap (DLG) showed a greater incidence of both poor-performing (p = 0.048) and failing phantom audits (p = 0.014); and in general, DLG value was correlated with dose calculation accuracy (r = 0.397, p

Microdosimetry is an important tool for assessing energy deposition distributions from ionizing radiation at cellular and cellular nucleus scales. It has served as an input parameter for multiple common mathematical models, including evaluation of relative biological effectiveness (RBE) of carbon ion therapy. The most common detector used for microdosimetry is the tissue-equivalent proportional counter (TEPC). Although it is widely applied, TEPC has various inherent uncertainties. Therefore, this work quantified the magnitude of TEPC measurement uncertainties and their impact on RBE estimates for therapeutic carbon beams. Microdosimetric spectra and frequency-, dose-, and saturation-corrected dose-mean lineal energy (****) were calculated using the Monte Carlo toolkit Geant4 for five monoenergetic and three spread-out Bragg peak carbon beams in water at every millimeter along the central beam axis. We simulated the following influences on these spectra from eight sources of uncertainty: wall effects, pulse pile-up, electronics, gas pressure, W-value, gain instability, low energy cut-off, and counting statistics. Statistic uncertainty was quantified as the standard deviation of perturbed values for each source. Bias was quantified as the difference between default lineal energy values and the mean of perturbed values for each systematic source. Uncertainties were propagated to RBE using the modified microdosimetric kinetic model (MKM). Variance introduced by statistic sources iny¯Fandy¯Daveraged 3.8% and 3.4%, respectively, and 1.5% iny*across beam depths and energies. Bias averaged 6.2% and 7.3% iny¯Fandy¯D,and 4.8% iny*.These uncertainties corresponded to 1.2 ± 0.9% on average in RBEMKM. The largest contributors to variance and bias were pulse pile-up and wall effects. This study established an error budget for microdosimetric carbon measurements by quantifying uncertainty inherent to TEPC measurements. It is necessary to understand how robust the measurement of RBE model input parameters are against this uncertainty in order to verify clinical model implementation.

The Children's Oncology Group (COG) has a strong quality assurance (QA) program managed by the Imaging and Radiation Oncology Core (IROC). This program consists of credentialing centers and providing real-time management of each case for protocol compliant target definition and radiation delivery. In the International Society of Pediatric Oncology (SIOP), the lack of an available, reliable online data platform has been a challenge and the European Society for Paediatric Oncology (SIOPE) quality and excellence in radiotherapy and imaging for children and adolescents with cancer across Europe in clinical trials (QUARTET) program currently provides QA review for prospective clinical trials. The COG and SIOP are fully committed to a QA program that ensures uniform execution of protocol treatments and provides validity of the clinical data used for analysis.

Background and Purpose Radiation therapy is a risk factor for late cardiac disease in childhood cancer survivors. Several pediatric cohort studies have established whole heart dose and dose–volume response models. Emerging data suggest that dose to cardiac substructures may be more predictive than whole heart metrics. In order to develop substructure dose-response models, the heart model previously used for pediatric cohort dosimetry needed enhancement and substructure delineation. Methods To enhance our heart model, we combined the age-scalable capability of our computational phantom with the anatomically-delineated (with substructures) heart models from an international humanoid phantom series. We examined cardiac volume similarity/overlap between registered age-scaled phantoms (1, 5, 10, and 15 years) with the enhanced heart model and the reference phantoms of the same age; dice similarity coefficient (DSC) and overlap coefficient (OC) were calculated for each matched pair. To assess the accuracy of our enhanced heart model, we compared doses from computed tomography-based planning (ground truth) with reconstructed heart doses. We also compared doses calculated with the prior and enhanced heart models for a cohort of nearly 5000 childhood cancer survivors. Results We developed a realistic cardiac model with 14-substructures, scalable across a broad age range (1–15 years); average DSC and OC were 0.84 ± 0.05 and 0.90 ± 0.05, respectively. The average percent difference between reconstructed and ground truth mean heart doses was 4.2%. In the cohort dosimetry analysis, dose and dose-volume metrics were approximately 10% lower on average when the enhanced heart model was used for dose reconstructions. Conclusion We successfully developed and validated an anatomically realistic age-scalable cardiac model that can be used to establish substructure dose-response models for late cardiac disease in childhood cancer survivor cohorts.

Purpose To compare the dosimetric impact of all major commercial vendors' metal artifact reduction (MAR) algorithms to one another, as well as to a novel in-house technique (AMPP) using an anthropomorphic head phantom. Materials and methods The phantom was an Alderson phantom, modified to allow for artifact-filled and baseline (no artifacts) computed tomography (CT) scans using teeth capsules made with metal amalgams or bone-equivalent materials. It also included a cylindrical insert that was accessible from the bottom of the neck and designed to introduce soft tissue features into the phantom that were used in the analysis. The phantom was scanned with the metal teeth in place using each respective vendor's MAR algorithm: OMAR (Philips), iMAR (Siemens), SEMAR (Canon), and SmartMAR (GE); the AMPP algorithm was designed in-house. Uncorrected and baseline (bone-equivalent teeth) image sets were also acquired using a Siemens scanner. Proton spot scanning treatment plans were designed on the baseline image set for five targets in the phantom. Once optimized, the proton beams were copied onto the different artifact-corrected image sets, with no reoptimization of the beams' parameters, to evaluate dose distribution differences in the different MAR-corrected and -uncorrected image sets. Dose distribution differences were evaluated by comparing dose-volume histogram (DVH) metrics, including planning target volume D95 and clinical target volume D99 coverages, V100, D0.03cc, and heterogeneity indexes, along with a qualitative and water equivalent thickness (WET) analysis. Results Uncorrected CT metal artifacts and commercial MAR algorithms negatively impacted the proton dose distributions of all five target shapes and locations in an inconsistent manner, sometimes overdosing by as much as 11.1% (D0.03) or underdosing by as much as 11.7% (V100) the planning target volumes. The AMPP-corrected images, however, provided dose distributions that consistently agreed with the baseline dose distribution. The dosimetry results also suggest that the commercial MAR algorithms' performances varied more with target location and less with target shape. Once relocated further from the metal, the target showed dose distributions that agreed more with the baseline for all commercial solutions, improving the overdosing by as much as 6%, implying inadequate HU correction from commercial MAR algorithms. In comparison to the baseline, HU profile shapes were considerably altered by commercial algorithms and reference values showed differences that represent stopping power percentage differences of 2.7-10%. The AMPP algorithm plans showed the smallest WET differences with the baseline (0.06 cm on average), while the commercial image sets created differences that ranged from 0.11 to 0.54 cm. Conclusions Computed tomography metal artifacts negatively impacted proton dose distributions on all five targets analyzed. The commercial MAR solutions performed inconsistently throughout all targets compared to the metal-free baseline. A lack of CTV coverage and an increased number of hotspots were observed throughout all commercial solutions. Dose distribution errors were related to the proximity to the artifacts, demonstrating the inability of commercial techniques to adequately correct severe artifacts. In contrast, AMPP consistently showed dose distributions that best matched the baseline, likely because it makes use of accurate HU information, as opposed to interpolated data like commercial algorithms.

Imaging is essential in successfully executing radiation therapy (RT) in oncology clinical trials. As technically sophisticated diagnostic imaging and RT were incorporated into trials, quality assurance in the National Clinical Trials Network groups entered a new era promoting image acquisition and review. Most trials involving RT require pre- and post-therapy imaging for target validation and outcome assessment. The increasing real-time (before and during therapy) imaging and RT object reviews are to ensure compliance with trial objectives. Objects easily transmit digitally for review from anywhere in the world. Physician interpretation of imaging and image application to RT treatment plans is essential for optimal trial execution. Imaging and RT data sets are used to credential RT sites to confirm investigator and institutional ability to meet trial target volume delineation and delivery requirements. Real-time imaging and RT object reviews can be performed multiple times during a trial to assess response to therapy and application of RT objects. This process has matured into an effective data management mechanism. When necessary, site and study investigators review objects together through web media technologies to ensure the patient is enrolled on the appropriate trial and the intended RT is planned and executed in a trial-compliant manner. Real-time imaging review makes sure: (1) the patient is entered and eligible for the trial, (2) the patient meets trial-specific adaptive therapy requirements, if applicable, and (3) the intended RT is according to trial guidelines. This review ensures the study population is uniform and the results are believable and can be applied to clinical practice.

Purpose: To design and evaluate an anthropomorphic spine phantom for use in credentialing proton therapy facilities for clinical trial participation by the Imaging and Radiation Oncology Core Houston QA Center. Materials and Methods: A phantom was designed to perform an end-to-end audit of the proton spine treatment process, including simulation, dose calculation, and proton treatment delivery. Because plastics that simulate bone in proton beams are unknown, 11 potential materials were tested to identify suitable phantom materials. Once built, preliminary testing using passive scattering and spot scanning treatment plans (including a field junction) were created in-house and delivered 3 times to test reproducibility. The following measured attributes were compared with the calculated values: absolute dose agreement using thermoluminescent dosimeters, planar gamma agreement, distal range, junction match, and right and left profile alignment using radiochromic film. Finally, credentialing results from 10 institutions were also assessed. Results: A suitable bone substitute was identified (Techtron HPV Bearing Grade), which had a measured relative stopping power that agreed within 1.1% of its value calculated by Eclipse. In-house passive scatter testing of the phantom demonstrated that the phantom was suitable for assessing craniospinal irradiation dose delivery. However, the in-house scanning beam results were more mixed, highlighting challenges in treatment delivery. Seven of ten institutions passed the proposed criteria for this phantom, a pass rate consistent with other Imaging and Radiation Oncology phantoms. Conclusions: An anthropomorphic proton spine phantom was developed to evaluate proton therapy delivery. This phantom provides a realistic challenge for centers wishing to participate in proton clinical trials and highlights the need for caution in applying advanced treatments.

Purpose. Radiation epidemiology studies of childhood cancer survivors treated in the pre-computed tomography (CT) era reconstruct the patients’ treatment fields on computational phantoms. For such studies, the phantoms are commonly scaled to age at the time of radiotherapy treatment because age is the generally available anthropometric parameter. Several reference size phantoms are used in such studies, but reference size phantoms are only available at discrete ages (e.g.: newborn, 1, 5, 10, 15, and Adult). When such phantoms are used for RT dose reconstructions, the nearest discrete-aged phantom is selected to represent a survivor of a specific age. In this work, we (1) conducted a feasibility study to scale reference size phantoms at discrete ages to various other ages, and (2) evaluated the dosimetric impact of using exact age-scaled phantoms as opposed to nearest age-matched phantoms at discrete ages. Methods. We have adopted the University of Florida/National Cancer Institute (UF/NCI) computational phantom library for our studies. For the feasibility study, eight male and female reference size UF/NCI phantoms (5, 10, 15, and 35 years) were downscaled to fourteen different ages which included next nearest available lower discrete ages (1, 5, 10 and 15 years) and the median ages at the time of RT for Wilms’ tumor (3.9 years), craniospinal (8.0 years), and all survivors (9.1 years old) in the Childhood Cancer Survivor Study (CCSS) expansion cohort treated with RT. The downscaling was performed using our in-house age scaling functions (ASFs). To geometrically validate the scaling, Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Euclidean distance (ED) were calculated between the scaled and ground-truth discrete-aged phantom (unscaled UF/NCI) for whole-body, brain, heart, liver, pancreas, and kidneys. Additionally, heights of the scaled phantoms were compared with ground-truth phantoms’ height, and the Centers for Disease Control and Prevention (CDC) reported 50th percentile height. Scaled organ masses were compared with ground-truth organ masses. For the dosimetric assessment, one reference size phantom and seventeen body-size dependent 5-year-old phantoms (9 male and 8 female) of varying body mass indices (BMI) were downscaled to 3.9-year-old dimensions for two different radiation dose studies. For the first study, we simulated a 6 MV photon right-sided flank field RT plan on a reference size 5-year-old and 3.9-year-old (both of healthy BMI), keeping the field size the same in both cases. Percent of volume receiving dose ≥15 Gy (V15) and the mean dose were calculated for the pancreas, liver, and stomach. For the second study, the same treatment plan, but with patient anatomy-dependent field sizes, was simulated on seventeen body-size dependent 5- and 3.9-year-old phantoms with varying BMIs. V15, mean dose, and minimum dose received by 1% of the volume (D1), and by 95% of the volume (D95) were calculated for pancreas, liver, stomach, left kidney (contralateral), right kidney, right and left colons, gallbladder, thoracic vertebrae, and lumbar vertebrae. A non-parametric Wilcoxon rank-sum test was performed to determine if the dose to organs of exact age-scaled and nearest age-matched phantoms were significantly different (p < 0.05). Results. In the feasibility study, the best DSCs were obtained for the brain (median: 0.86) and whole-body (median: 0.91) while kidneys (median: 0.58) and pancreas (median: 0.32) showed poorer agreement. In the case of MDA and ED, whole-body, brain, and kidneys showed tighter distribution and lower median values as compared to other organs. For height comparison, the overall agreement was within 2.8% (3.9 cm) and 3.0% (3.2 cm) of ground-truth UF/NCI and CDC reported 50th percentile heights, respectively. For mass comparison, the maximum percent and absolute differences between the scaled and ground-truth organ masses were within 31.3% (29.8 g) and 211.8 g (16.4%), respectively (across all ages). In the first dosimetric study, absolute difference up to 6% and 1.3 Gy was found for V15 and mean dose, respectively. In the second dosimetric study, V15 and mean dose were significantly different (p 1 and D95 were not significantly different for most organs (p > 0.05). Conclusion. We have successfully evaluated our ASFs by scaling UF/NCI computational phantoms from one age to another age, which demonstrates the feasibility of scaling any CT-based anatomy. We have found that dose to organs of exact age-scaled and nearest aged-matched phantoms are significantly different (p < 0.05) which indicates that using the exact age-scaled phantoms for retrospective dosimetric studies is a better approach.

Background and purpose: An audit methodology for verifying the implementation of output factors (OFs) of small fields in treatment planning systems (TPSs) used in radiotherapy was developed and tested through a multinational research group and performed on a national level in five different countries. Materials and methods: Centres participating in this study were asked to provide OFs calculated by their TPSs for 10 × 10 cm2, 6 × 6 cm2, 4 × 4 cm2, 3 × 3 cm2 and 2 × 2 cm2 field sizes using an SSD of 100 cm. The ratio of these calculated OFs to reference OFs was analysed. The action limit was ±3% for the 2 × 2 cm2 field and ±2% for all other fields. Results: OFs for more than 200 different beams were collected in total. On average, the OFs for small fields calculated by TPSs were generally larger than measured reference data. These deviations increased with decreasing field size. On a national level, 30% and 31% of the calculated OFs of the 2 × 2 cm2 field exceeded the action limit of 3% for nominal beam energies of 6 MV and for nominal beam energies higher than 6 MV, respectively. Conclusion: Modern TPS beam models generally overestimate the OFs for small fields. The verification of calculated small field OFs is a vital step and should be included when commissioning a TPS. The methodology outlined in this study can be used to identify potential discrepancies in clinical beam models. Keywords: Dosimetry audit, Small field output factors, Treatment planning system

Quality assurance (QA) for clinical trials is important. Lack of compliance can affect trial outcome. Clinical trial QA groups have different methods of dose distribution verification and analysis, all with the ultimate aim of ensuring trial compliance. The aim of this study was to gain a better understanding of different processes to inform future dosimetry audit reciprocity.Six clinical trial QA groups participated. Intensity modulated treatment plans were generated for three different cases. A range of 17 virtual 'measurements' were generated by introducing a variety of simulated perturbations (such as MLC position deviations, dose differences, gantry rotation errors, Gaussian noise) to three different treatment plan cases. Participants were blinded to the 'measured' data details. Each group analysed the datasets using their own gamma index (γ) technique and using standardised parameters for passing criteria, lower dose threshold, γ normalisation and global γ.For the same virtual 'measured' datasets, different results were observed using local techniques. For the standardised γ, differences in the percentage of points passing with γ 1 were also found, however these differences were less pronounced than for each clinical trial QA group's analysis. These variations may be due to different software implementations of γ.This virtual dosimetry audit has been an informative step in understanding differences in the verification of measured dose distributions between different clinical trial QA groups. This work lays the foundations for audit reciprocity between groups, particularly with more clinical trials being open to international recruitment.

Purpose The Imaging and Radiation Oncology Core Houston (IROC-H) (formerly the Radiological Physics Center) has reported varying levels of agreement in their anthropomorphic phantom audits. There is reason to believe one source of error in this observed disagreement is the accuracy of the dose calculation algorithms and heterogeneity corrections used. To audit this component of the radiotherapy treatment process an independent dose calculation tool is needed. Methods Monte Carlo multiple source models for Elekta 6MV and 10MV therapeutic x-ray beams were commissioned based on measurement of central axis depth dose data for a 10 x 10 cm2 field size and dose profiles for a 40 x 40 cm2 field size. The models were validated against open field measurements consisting of depth dose data and dose profiles for field sizes ranging from 3 x 3 cm2 to 30 x 30 cm2. The models were then benchmarked against measurements in IROC-H's anthropomorphic head and neck and lung phantoms. Results Validation results showed 97.9% and 96.8% of depth dose data passed a ±2% Van Dyk criterion for 6MV and 10MV models, respectively. Dose profile comparisons showed an average agreement using a ±2%/2mm criterion of 98.0% and 99.0% for 6MV and 10MV models, respectively. Phantom plan comparisons were evaluated using ±3%/2mm gamma criterion, and averaged passing rates between Monte Carlo and measurements were 87.4% and 89.9% for 6MV and 10MV models, respectively. Conclusions Accurate multiple source models for Elekta 6MV and 10MV x-ray beams have been developed for inclusion in an independent dose calculation tool for use in clinical trial audits. This article is protected by copyright. All rights reserved.

Purpose The Imaging and Radiation Oncology Core – Houston (IROC-H) Quality Assurance Center (formerly the Radiological Physics Center) has reported varying levels of compliance from their anthropomorphic phantom auditing program. IROC-H studies have suggested that one source of disagreement between institution submitted calculated doses and measurement is the accuracy of the institution's treatment planning system dose calculations and heterogeneity corrections used. In order to audit this step of the radiation therapy treatment process, an independent dose calculation tool is needed. Methods Monte Carlo multiple source models for Varian Flattening Filter Free (FFF) 6MV and FFF 10MV therapeutic x-ray beams were commissioned based on central axis depth dose data from a 10 x 10 cm2 field size and dose profiles for a 40 x 40 cm2 field size. The models were validated against open field measurements in a water tank for field sizes ranging from 3 x 3 cm2 to 40 x 40 cm2. The models were then benchmarked against IROC-H's anthropomorphic head and neck phantom and lung phantom measurements. Results Validation results, assessed with a ±2%/2mm gamma criterion, showed average agreement of 99.9% and 99.0% for central axis depth dose data for FFF 6MV and FFF 10MV models, respectively. Dose profile agreement using the same evaluation technique averaged 97.8% and 97.9% for the respective models. Phantom benchmarking comparisons were evaluated with a ±3%/2mm gamma criterion, and agreement averaged 90.1% and 90.8% for the respective models. Conclusions Multiple source models for Varian FFF 6MV and FFF 10MV beams have been developed, validated, and benchmarked for inclusion in an independent dose calculation quality assurance tool for use in clinical trial audits. This article is protected by copyright. All rights reserved.

Background and purpose Artefacts caused by dental amalgam implants present a common challenge in computed tomography (CT) and therefore treatment planning dose calculations. The goal was to perform a quantitative image quality analysis of our Artifact Management for Proton Planning (AMPP) algorithm which used gantry tilts for managing metal artefacts on Head and Neck (HN) CT scans and major vendors’ commercial approaches. Materials and methods Metal artefact reduction (MAR) algorithms were evaluated using an anthropomorphic phantom with a removable jaw for the acquisition of images with and without (baseline) metal artifacts. AMPP made use of two angled CT scans to generate one artifact-reduced image set. The MAR algorithms from four vendors were applied to the images with artefacts and the analysis was performed with respective baselines. Planar HU difference maps and volumetric HU differences were analyzed. Results AMPP algorithm outperformed all vendors’ commercial approaches in the elimination of artefacts in the oropharyngeal region, showing the lowest percent of pixels outside +− 20 HU criteria, 4%; whereas those in the MAR-corrected images ranged from 26% to 67%. In the region of interest within the affected slices, the commercial MAR algorithms showed inconsistent performance, whereas the AMPP algorithm performed consistently well throughout the phantom’s posterior region. Conclusions A novel MAR algorithm was evaluated and compared to four commercial algorithms using an anthropomorphic phantom. Unanimously, the analysis showed the AMPP algorithm outperformed vendors’ commercial approaches, showing the potential to be broadly implemented, improve visualizations in patient anatomy and provide accurate HU information.

Linac calibration is done in water, but patients are comprised primarily of soft tissue. Conceptually, and specified in NRG/RTOG trials, dose should be reported as dose-to-muscle to describe the dose to the patient. Historically, the dose-to-water of the linac calibration was often converted to dose-to-muscle for patient calculations through manual application of a 0.99 dose-to-water to dose-to-muscle correction factor, applied during the linac clinical reference calibration. However, many current treatment planning system (TPS) dose calculation algorithms approximately provide dose-to-muscle (tissue), making application of a manual scaling unnecessary. There is little guidance on when application of a scaling factor is appropriate, resulting in highly inconsistent application of this scaling by the community. In this report we provide guidance on the steps necessary to go from the linac absorbed dose-to-water calibration to dose-to-muscle in patient, for various commercial TPS algorithms. If the TPS does not account for the difference between dose-to-water and dose-to-muscle, then TPS reference dose scaling is warranted. We have tabulated the major vendors' TPS in terms of whether they approximate dose-to-muscle or calculate dose-to-water and recommend the correction factor required to report dose-to-muscle directly from the TPS algorithm. Physicists should use this report to determine the applicable correction required for specifying the reference dose in their TPS to achieve this goal and should remain attentive to possible changes to their dose calculation algorithm in the future.

PURPOSE: Between July 2013 and August 2019, 22% of the Imaging and Radiation Oncology Core (IROC) spine, and 15% of the moving lung phantom irradiations have failed to meet established acceptability criteria. The spine phantom simulates a highly modulated stereotactic body radiation therapy (SBRT) case, whereas the lung phantom represents a low-to-none modulation moving target case. In this study, we assessed the contribution of dose calculation errors to these phantom results and evaluated their effects on failure rates. METHODS: We evaluated dose calculation errors by comparing the calculation accuracy of various institutions’ treatment planning systems (TPSs) versus IROC-Houston’s previously established independent dose recalculation system (DRS). Each calculation was compared with the measured dose actually delivered to the phantom; cases in which the recalculation was more accurate were interpreted as a deficiency in the institution’s TPS. A total of 258 phantom irradiation plans (172 lung and 86 spine) were recomputed. RESULTS: Overall, the DRS performed better than the TPSs in 47% of the spine phantom cases. However, the DRS was more accurate in 93% of failing spine phantom cases (with an average improvement of 2.35%), indicating a deficiency in the institution’s treatment planning system. Deficiencies in dose calculation accounted for 60% of the overall discrepancy between measured and planned doses among spine phantoms. In contrast, lung phantom DRS calculations were more accurate in only 35% and 42% of all and failing lung phantom cases respectively, indicating that dose calculation errors were not substantially present. These errors accounted for only 30% of the overall discrepancy between measured and planned doses. CONCLUSIONS: Dose calculation errors are common and substantial in IROC spine phantom irradiations, highlighting a major failure mode in this phantom and in clinical treatment management of these cases. In contrast, dose calculation accuracy had only a minimal contribution to failing lung phantom results, indicating that other failure modes drive problems with this phantom and similar clinical treatments.

Thermoluminescent dosimeters (TLD) and optically stimulated luminescent dosimeters (OSLD) are practical, accurate, and precise tools for point dosimetry in medical physics applications. The charges of Task Group 191 were to detail the methodologies for practical and optimal luminescence dosimetry in a clinical setting. This includes: (a) to review the variety of TLD/OSLD materials available, including features and limitations of each; (b) to outline the optimal steps to achieve accurate and precise dosimetry with luminescent detectors and to evaluate the uncertainty induced when less rigorous procedures are used; (c) to develop consensus guidelines on the optimal use of luminescent dosimeters for clinical practice; and (d) to develop guidelines for special medically relevant uses of TLDs/OSLDs such as mixed photon/neutron field dosimetry, particle beam dosimetry, and skin dosimetry. While this report provides general guidelines for TLD and OSLD processes, the report provides specific details for TLD-100 and nanoDotTM dosimeters because of their prevalence in clinical practice.

Purpose The purpose of the study was to establish a quantitative method for implant quality evaluation in permanent seed implant brachytherapy for credentialing. Delivery-based credentialing will promote consistency in brachytherapy seed delivery and improve patient outcomes. Methods A workflow for delivery-based credentialing was outlined and applied to permanent breast seed implant brachytherapy. Delivery simulations were performed on implantable anthropomorphic breast phantoms. Two institutions experienced in permanent seed implant brachytherapy demonstrated the peer credentialing process. Each delivery was evaluated for seed placement accuracy as the measure of implant quality, both for implant accuracy and across five simulations to assess implant variation. Initial credentialing criteria are set based on two factors; the mean seed placement accuracy (implant accuracy) and the mean standard deviation (seed variation) with the threshold for each set with the addition of two standard deviations. Results Across two institutions, seed placement accuracy (±standard deviation) was calculated for all five delivery simulations to yield 6.1 (±2.6) mm. To set credentialing criteria, the implant accuracy (6.1 mm) plus two standard deviations (2.0 mm) and the seed variation (2.6 mm) plus two standard deviations (0.8) mm yield a threshold of 8.1 ± 3.4 mm. It is expected that 95% of experienced institutions would perform the phantom simulation within this threshold. Conclusion Brachytherapy programs should validate delivery accuracy by formal credentialing, which is standard in external beam programs. This quantitative implant evaluation should be combined with current credentialing standards for permanent seed brachytherapy to form a comprehensive validation of institutional brachytherapy program quality.

Purpose Our purpose was to investigate and classify the reasons why institutions fail the Imaging and Radiation Oncology Core (IROC) stereotactic body radiation therapy (SBRT) spine and moving lung phantoms, which are used to credential institutions for clinical trial participation. Methods and Materials All IROC moving lung and SBRT spine phantom irradiation failures recorded from January 2012 to December 2018 were evaluated in this study. A failure was a case where the institution did not meet the established IROC criteria for agreement between planned and delivered dose. We analyzed the reports for all failing irradiations, including point dose disagreement, dose profiles, and gamma analyses. Classes of failure patterns were created and used to categorize each instance. Results There were 158 failing cases analyzed: 116 of 1052 total lung irradiations and 42 of 263 total spine irradiations. Seven categories were required to describe the lung phantom failures, whereas 4 were required for the spine. Types of errors present in both phantom groups included systematic dose and localization errors. Fifty percent of lung failures were due to a superior-inferior localization error, that is, error in the direction of major motion. Systematic dose errors, however, contributed to only 22% of lung failures. In contrast, the majority (60%) of spine phantom failures were due to systematic dose errors, with localization errors (in any direction) accounting for only 14% of failures. Conclusions There were 2 distinct patterns of failure between the IROC moving lung and SBRT spine phantoms. The majority of the lung phantom failures were due to localization errors, whereas the spine phantom failures were largely attributed to systematic dose errors. Both of these errors are clinically relevant and could manifest as errors in patient cases. These findings highlight the value of independent end-to-end dosimetry audits and can help guide the community in improving the quality of radiation therapy by focusing attention on where errors manifest in the community.

Purpose To create a snapshot of common practices for 3D‐CRT and intensity‐modulated radiation therapy (IMRT) QA through a large‐scale survey and compare to TG‐218 recommendations. Methods A survey of 3D‐CRT and IMRT QA was constructed at and distributed by the IROC‐Houston QA center to all institutions monitored by IROC (n = 2,861). The first part of the survey asked about methods to check dose delivery for 3D‐CRT. The bulk of the survey focused on IMRT QA, inquiring about treatment modalities, standard tools used to verify planned dose, how assessment of agreement is calculated and the comparison criteria used, and the strategies taken if QA fails. Results The most common tools for dose verification were a 2D diode array (52.8%), point(s) measurement (39.0%), EPID (27.4%), and 2D ion chamber array (23.9%). When IMRT QA failed, the highest average rank strategy utilized was to remeasure with the same setup, which had an average position ranking of 1.1 with 90.4% of facilities employing this strategy. The second highest average ranked strategy was to move to a new calculation point and remeasure (54.9%); this had an average ranking of 2.1. Conclusion The survey provided a snapshot of the current state of dose verification for IMRT radiotherapy. The results showed variability in approaches and that work is still needed to unify and tighten criteria in the medical physics community, especially in reference to TG‐218's recommendations.

PURPOSE Consistency between different international quality assurance groups is important in the progress toward similar standards and expectations in radiotherapy dosimetry around the world, and in the context of consistent clinical trial data from international trial participants. This study compares the dosimetry audit methodology and results of two international quality assurance groups performing a side-by-side comparison at the same radiotherapy department, and interrogates the ability of the audits to detect deliberately introduced errors. METHODS A comparison of the core dosimetry components of reference and non-reference audits was conducted by the Imaging and Radiation Oncology Core (IROC, Houston, USA) and the Australian Clinical Dosimetry Service (ACDS, Melbourne, Australia). A set of measurements were conducted over 2 days at an Australian radiation therapy facility in Melbourne. Each group evaluated the reference dosimetry, output factors, small field output factors, percentage depth dose (PDD), wedge, and off-axis factors according to their standard protocols. IROC additionally investigated the Electron PDD and the ACDS investigated the effect of heterogeneities. In order to evaluate and compare the performance of these audits under suboptimal conditions, artificial errors in percentage depth dose (PDD), EDW, and small field output factors were introduced into the 6 MV beam model to simulate potential commissioning/modeling errors and both audits were tested for their sensitivity in detecting these errors. RESULTS With the plans from the clinical beam model, almost all results were within tolerance and at an optimal pass level. Good consistency was found between the two audits as almost all findings were consistent between them. Only two results were different between the results of IROC and the ACDS. The measurements of reference FFF photons showed a discrepancy of 0.7% between ACDS and IROC due to the inclusion of a 0.5% nonuniformity correction by the ACDS. The second difference between IROC and the ACDS was seen with the lung phantom. The asymmetric field behind lung measured by the ACDS was slightly (0.3%) above the ACDS's pass (optimal) level of 3.3%. IROC did not detect this issue because their measurements were all assessed in a homogeneous phantom. When errors were deliberately introduced neither audit was sensitive enough to pick up a 2% change to the small field output factors. The introduced PDD change was flagged by both audits. Similarly, the introduced error of using 25° wedge instead of 30° wedge was detectible in both audits as out of tolerance. CONCLUSIONS Despite different equipment, approach, and scope of measurements in on-site audits, there were clear similarities between the results from the two groups. This finding is encouraging in the context of a global harmonized approach to radiotherapy quality assurance and dosimetry audit.

Managing radiotherapy patients with implanted cardiac devices (implantable cardiac pacemakers and implantable cardioverter-defibrillators) has been a great practical and procedural challenge in radiation oncology practice. Since the publication of the AAPM TG-34 in 1994, large bodies of literature and case reports have been published about different kinds of radiation effects on modern technology implantable cardiac devices and patient management before, during, and after radiotherapy. This task group report provides the framework that analyzes the potential failure modes of these devices and lays out the methodology for patient management in a comprehensive and concise way, in every step of the entire radiotherapy process.