Preclinical Ultra-High Dose Rate (FLASH) Proton Radiation Therapy System for Small Animal Studies.

Purpose: Animal studies with ultrahigh dose-rate radiation therapy (FLASH, >40 Gy/s) preferentially spare normal tissues without sacrificing antitumor efficacy compared with conventional dose-rate radiation therapy (CONV). At the University of Washington, we developed a cyclotron-generated preclinical scattered proton beam with FLASH dose rates. We present the technical details of our FLASH radiation system and preliminary biologic results from whole pelvis radiation. Methods and Materials: A Scanditronix MC50 compact cyclotron beamline has been modified to produce a 48.7 MeV proton beam at dose rates between 0.1 and 150 Gy/s. The system produces a 6 cm diameter scattered proton beam (flat to ± 3%) at the target location. Female C57BL/6 mice 5 to 6 weeks old were used for all experiments. To study normal tissue effects in the distal colon, mice were irradiated using the entrance region of the proton beam to the whole pelvis, 18.5 Gy at different dose rates: control, CONV (0.6-1 Gy/s) and FLASH (50-80 Gy/s). Survival was monitored daily and EdU (5-ethynyl-2´-deoxyuridine) staining was performed at 24- and 96-hours postradiation. Cleaved caspase-3 staining was performed 24-hours postradiation. To study tumor control, allograft B16F10 tumors were implanted in the right flank and received 18 Gy CONV or FLASH proton radiation. Tumor growth and survival were monitored. Results: After 18.5 Gy whole pelvis radiation, survival was 100% in the control group, 0% in the CONV group, and 44% in the FLASH group (P < .01). EdU staining showed cell proliferation was significantly higher in the FLASH versus CONV group at both 24-hours and 96-hours postradiation in the distal colon, although both radiation groups showed decreased proliferation compared with controls (P < .05). Lower cleaved caspase-3 staining was seen in the FLASH versus conventional group postradiation (P < .05). Comparable flank tumor control was observed in the CONV and FLASH groups. Conclusions: We present our preclinical FLASH proton radiation system and biologic results showing improved survival after whole pelvis radiation, with equivalent tumor control.

Radiation Therapy Physics Quality Assurance and Management Practices in Low- and Middle-Income Countries: An Initial Pilot Survey in Six Countries and Validation Through a Site Visit.

Purpose: Our purpose was to assess physics quality assurance (QA) practices in less resourced radiation therapy (RT) centers to improve quality of care. Methods and Materials: A preliminary study was conducted in 2020 of 13 select RT centers in 6 countries, and in 2021, our team conducted onsite visits to all the RT centers in Ghana, one of the countries from the initial survey. The RT centers included 1 private and 2 public institutions (denoted as Public-1 and Public-2). Follow-up surveys were sent to 17 medical physicists from the site visit. Questions centered on the topics of equipment, institutional practice, physics quality assurance, management, and safety practices. Qualitative and descriptive methods were used for data analysis. Questions regarding operational challenges (machine downtime, patient-related issues, power outages, and staffing) were asked on a 5-point Likert scale. Results: The preliminary survey from 2020 had a 92% response rate. One key result showed that for RT centers in lower gross national income per capita countries there was a direct correlation between QA needs and the gross national income per capita of the country. The needs identified included film/array detectors, independent dose calculation software, calibration of ion chambers, diodes, thermoluminiscence diodes (TLDs), phantoms for verification, Treatment Planning System (TPS) test phantoms, imaging test phantoms and film dosimeters, education, and training. For the post survey after the site visit in 2021, we received a 100% response rate. The private and the Public-1 institutions each have computed tomography simulators located in their RT center. The average daily patient external beam workload for each clinic on a linear accelerator was: private = 25, Public-1 = 55, Public-2 = 40. The Co-60 workload was: Public-1 = 45, Public-2 = 25 (there was no Co-60 at the private hospital). Public-1 and -2 lacked the equipment necessary to conform to best practices in Task Group reports (TG) 142 and 198. Public-2 reported significant operational challenges. Notably, Public-1 and -2 have peer review chart rounds, which are attended by clinical oncologists, medical physicists, physicians, and physics trainees. All 17 physicists who responded to the post site visit survey indicated they had a system of documenting, tracking, and trending patient-related safety incidents, but only 1 physicist reported using International Atomic Energy Agency Safety in Radiation Oncology. Conclusions: The preliminary study showed a direct correlation between QA needs and the development index of a country, and the follow-up survey examines operational and physics QA practices in the RT clinics in Ghana, one of the initial countries surveyed. This will form the basis of a planned continent-wide survey in Africa intended to spotlight QA practices in low- and middle-income countries, the challenges faced, and lessons learned to help understand the gaps and needs to support local physics QA and management programs. Audits during the site visit show education and training remain the most important needs in operating successful QA programs.

A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system.

Background and purpose: A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods: A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results: For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions: A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system.

Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning.

PURPOSE: A novel compensator-based system has been proposed which delivers intensity-modulated radiation therapy (IMRT) with cobalt-60 beams. This could improve access to advanced radiotherapy in low- and middle-income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low- and middle-income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS). METHODS: The novel treatment device considered here employs a cobalt-60 source and nine compensators. Each compensator is produced by 3-D printing a thin plastic mold which is then filled on-demand within the machine with reusable 2-mm-diameter spherical tungsten balls. This system was commissioned in the Eclipse TPS and validation tests were conducted with Monte Carlo using Geant4 Application for Tomographic Emission for percentage depth dose, in-plane profiles, penumbra, and IMRT dose validation. And the American Association of Physicists in Medicine Task Group 119 benchmarking testing was performed. Additionally, compensator-based cobalt-60 IMRT plans were created for 46 head-and-neck cancer cases and compared to the linac-based volumetric modulated arc therapy (VMAT) plans used clinically, then dosimetric parameters were evaluated. Beam-on time for each field was calculated. In addition, the measurement was also performed in a limited environment and compared with the Monte Carlo simulations. RESULTS: The differences in percent depth doses and in-plane profiles between the Eclipse and Monte Carlo simulations were 0.65% ± 0.41% and 1.02% ± 0.99%, respectively, and the 80%-20% penumbra agreed within 0.46 ± 0.27 mm. For the Task Group 119 validation plans, all treatment planning goals were met and gamma passing rates were >95% (3%/3 mm criteria). In 46 clinical head-and-neck cases, the cobalt-60 compensator-based IMRT plans had planning target volume (PTV) coverages similar to linac-based VMAT plans: all dosimetric values for PTV were within 1.5%. The organs at risk dose parameters were somewhat higher in cobalt-60 compensator-based IMRT plans versus linac-based VMAT plans. The mean dose differences for the spinal cord, brain, and brainstem were 4.43 ± 1.92, 3.39 ± 4.67, and 2.40 ± 3.71 Gy, while those for the rest of the organs were <1 Gy. The average beam-on time per field was 0.42 ± 0.10 min for the 6 MV multi-leaf-collimator plans while those for the cobalt-60 compensator plans were 0.17 ± 0.01 and 0.31 ± 0.01 min at the dose rates of 350 and 175 cGy/min. There was a good agreement between in-plane profiles from measurements and Monte Carlo simulations, which differences are 1.34 ± 1.90% and 0.13 ± 2.16% for two different fields. CONCLUSIONS: A novel compensator-based IMRT system using cobalt-60 beams was commissioned and validated in a commercial TPS. Plan quality with this system was comparable to that of linac-based plans in all test cases with shorter estimated beam-on times. This system enables reliable, high-quality plans with reduced cost and complexity and may have benefits for underserved regions of the world. This system is being integrated into the RPA, a web-based platform for auto-contouring and auto-planning.

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data.

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.

Hazardous Attitudes: Physician Decision Making in Radiation Oncology.

Purpose: The Federal Aviation Administration quantifies hazardous attitudes (HAs) among pilots using a scale. HAs have been linked to aviation risk. We assessed the influence of HAs and other factors in treatment decision making in radiation oncology (RO). Methods and Materials: An anonymous survey was sent to 809 radiation oncologists in US cities housing the top 25 cancer centers. The survey included an HA scale adapted for RO and presented 9 cases assessing risk-tolerant radiation therapy prescribing habits and compliance with the American Society for Radiation Oncology's Choosing Wisely recommendations. Demographic and treatment decision data were dichotomized to identify factors associated with prescribing habits using univariable and multivariable (MVA) logistic regression analyses. Results: A total of 139 responses (17.1%) were received, and 103 were eligible for analysis. Among respondents, 40% were female, ages were evenly distributed, and 83% were in academics. Median scores for all attitudes (macho, anti-authority, worry, resignation, and impulsivity) were below the aviation thresholds for hazard and data from surgical specialties. On MVA, responders >50 years old with >5 years' experience were 4.45 times more likely to recommend risk-tolerant radiation (P = .016). Macho attitude was negatively associated with Choosing Wisely compliant treatments (odds ratio [OR], 0.12; P = .001). Physicians who reported having previously retreated the supraclavicular fossa without complication were more likely to recommend retreatment in medically unfit patients if they felt the complication was avoided owing to careful planning (OR, 5.2; P = .008). Conclusions: To our knowledge, this represents the first study analyzing physician attitudes in RO and their effect on self-reported treatment decisions. This work suggests that attitude may be among the factors that influence risk-tolerant prescribing practices and compliance with Choosing Wisely recommendations.

Affordable compensator for IMRT delivery designed for low-and-middle income countries, a Monte Carlo study.

We propose a novel cost-effective compensator that can be used to facilitate access to IMRT in low-and-middle income countries. The compensator has the advantages of simplicity, less downtime, increased reliability and less impact on treatment quality from patient motion during treatment. Moreover, the system can be used with either a cobalt-60 unit or linear accelerator. In this Monte Carlo study, the dosimetric properties of the new compensator design have been evaluated. Results were obtained for different field sizes of cobalt teletherapy machine, and the dose was scored at 0.5 cm depth in a water phantom. The effects of compensator thickness, filling material type and shape, and field size were identified. Furthermore, the percentage depth dose and beam profiles for various field sizes and at different depths were obtained. Beam profiles show no significant signature of the beads relative to a solid compensator; in addition, they exhibit a better flatness while preserving symmetry for all field sizes. A reusable bead-based compensator appears to be feasible, and provides dose distribution similar to a solid compensator with low cost and no hazards. Our results avail the ongoing efforts to expand the reach to IMRT in low- and middle-income countries.

Low-Dose Image-Guided Pediatric CNS Radiation Therapy: Final Analysis From a Prospective Low-Dose Cone-Beam CT Protocol From a Multinational Pediatrics Consortium.

BACKGROUND: Lower-dose cone-beam computed tomography protocols for image-guided radiotherapy may permit target localization while minimizing radiation exposure. We prospectively evaluated a lower-dose cone-beam protocol for central nervous system image-guided radiotherapy across a multinational pediatrics consortium. METHODS: Seven institutions prospectively employed a lower-dose cone-beam computed tomography central nervous system protocol (weighted average dose 0.7 mGy) for patients ≤21 years. Treatment table shifts between setup with surface lasers versus cone-beam computed tomography were used to approximate setup accuracy, and vector magnitudes for these shifts were calculated. Setup group mean, interpatient, interinstitution, and random error were estimated, and clinical factors were compared by mixed linear modeling. RESULTS: Among 96 patients, with 2179 pretreatment cone-beam computed tomography acquisitions, median age was 9 years (1-20). Setup parameters were 3.13, 3.02, 1.64, and 1.48 mm for vector magnitude group mean, interpatient, interinstitution, and random error, respectively. On multivariable analysis, there were no significant differences in mean vector magnitude by age, gender, performance status, target location, extent of resection, chemotherapy, or steroid or anesthesia use. Providers rated >99% of images as adequate or better for target localization. CONCLUSIONS: A lower-dose cone-beam computed tomography protocol demonstrated table shift vector magnitude that approximate clinical target volume/planning target volume expansions used in central nervous system radiotherapy. There were no significant clinical predictors of setup accuracy identified, supporting use of this lower-dose cone-beam computed tomography protocol across a diverse pediatric population with brain tumors.

A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events.

PURPOSE: Error detection in radiation oncology relies heavily on voluntary reporting, and many adverse events and near misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise-unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS). METHODS AND MATERIALS: Twenty triggers were derived from an electronic radiation oncology information system. Data from the systems over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near misses. RESULTS: Of the 3159 treatment courses analyzed, 357 had a grade 3 to 4 ILS entry; 2210 courses composed the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, 9 reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near miss with an ILS entry had a direct relationship between the triggers and the near miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near miss without an ILS entry found 2 unreported near-miss events. CONCLUSIONS: The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.

Durable Improvement in Patient Safety Culture Over 5 Years With Use of High-volume Incident Learning System.

PURPOSE: Incident learning systems (ILSs) require substantial time and effort to maintain, risking staff burnout and ILS disuse. Herein, we assess the durability of ILS-associated safety culture improvements and ILS engagement at 5 years. METHODS AND MATERIALS: A validated survey assessing safety culture was administered to all staff of an academic radiation oncology department before starting ILS and annually thereafter for 5 years. The survey consists of 70 questions assessing key cultural domains, overall patient safety grade, and barriers to incident reporting. A χ2 test was used to compare baseline scores before starting the ILS (pre-ILS) with the aggregate 5 years during which ILS was in use (with ILS). ILS engagement was measured by the self-reported number of ILS entries submitted in the previous 12 months. RESULTS: The survey response rate was ≥68% each year (range, 68%-80%). High-volume event reporting was sustained (4673 reports; average of 0.9 ILS entries per treatment course). ILS engagement increased, with 43% of respondents submitting reports during the 12 months pre-ILS compared with 64% with ILS in use (P < .001). Significant improvements (pre- vs. with-ILS) were observed in the cultural domains of patient safety perceptions (25% vs 39%; P < .03), and responsibility and self-efficacy (43% vs 60%; P < .01). The overall patient safety grade of very good or excellent significantly increased (69% vs 85%; P < .01). Significant reductions were seen in the following barriers to error reporting: embarrassment in front of colleagues, getting colleagues into trouble, and effect on department reputation. CONCLUSIONS: Comprehensive incident learning was sustained over 5 years and is associated with significant durable improvements in metrics of patient safety culture.

Characterization of a Bayesian network-based radiotherapy plan verification model.

PURPOSE: The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time-consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof-of-principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice. METHODS: We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de-identified Elekta oncology information system from the years 2010-2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29-node, 40-edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC). RESULTS: Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2-yr, 3-yr 4-yr and 5-yr windows. With a 4-yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1-yr steps. Compared to the 4-yr window moved back by 4 yrs (2010-2013 vs 2014-2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types. CONCLUSIONS: The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4-yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.

Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks.

PURPOSE: Patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy QA. In this work, we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific QA. METHODS: Planar dose maps from 186 IMRT beams from 23 IMRT plans were evaluated. Each plan was transferred to a cylindrical phantom CT geometry. Three sets of planar doses were exported from each plan corresponding to (a) the error-free case, (b) a random multileaf collimator (MLC) error case, and (c) a systematic MLC error case. Each plan was delivered to the electronic portal imaging device (EPID), and planned and measured doses were used to calculate gamma images in an EPID dosimetry software package (for a total of 558 gamma images). Two radiomic approaches were used. In the first, a convolutional neural network with triplet learning was used to extract image features from the gamma images. In the second, a handcrafted approach using texture features was used. The resulting metrics from both approaches were input into four machine learning classifiers (support vector machines, multilayer perceptrons, decision trees, and k-nearest-neighbors) in order to determine whether images contained the introduced errors. Two experiments were considered: the two-class experiment classified images as error-free or containing any MLC error, and the three-class experiment classified images as error-free, containing a random MLC error, or containing a systematic MLC error. Additionally, threshold-based passing criteria were calculated for comparison. RESULTS: In total, 303 gamma images were used for model training and 255 images were used for model testing. The highest classification accuracy was achieved with the deep learning approach, with a maximum accuracy of 77.3% in the two-class experiment and 64.3% in the three-class experiment. The performance of the handcrafted approach with texture features was lower, with a maximum accuracy of 66.3% in the two-class experiment and 53.7% in the three-class experiment. Variability between the results of the four machine learning classifiers was lower for the deep learning approach vs the texture feature approach. Both radiomic approaches were superior to threshold-based passing criteria. CONCLUSIONS: Deep learning with convolutional neural networks can be used to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific gamma images. The performance of the deep learning network was superior to a handcrafted approach with texture features, and both radiomic approaches were better than threshold-based passing criteria. The results suggest that radiomic QA is a promising direction for clinical radiotherapy.

Radiation oncology resident training in patient safety and quality improvement: a national survey of residency program directors.

BACKGROUND: Physicians and physicists are expected to contribute to patient safety and quality improvement (QI) in Radiation Oncology (RO), but prior studies suggest that training for this may be inadequate. RO and medical physics (MP) program directors (PDs) were surveyed to better understand the current patient safety/QI training in their residency programs. METHODS: PDs were surveyed via email in January 2017. Survey questions inquired about current training, curriculum elements, and barriers to development and/or improvement of safety and QI training. RESULTS: Eighty-nine RO PDs and 84 MP PDs were surveyed, and 21 RO PDs (28%) and 31 MP PDs (37%) responded. Both RO and MP PDs had favorable opinions of current safety and QI training, and used a range of resources for program development, especially safety and QI publications. Various curriculum elements were reported. Curriculum elements used by RO and MP PDs were similar, except RO were more likely than MP PDs to implement morbidity and mortality (M&M) conference (72% vs. 45%, p < 0.05). RO and MP PDs similarly cited various barriers, but RO PDs were more likely to cite lack of experience than MP PDs (40% vs. 16%, p < 0.05). PDs responded similarly independent of whether they reported using a departmental incident learning system (ILS) or not. CONCLUSIONS: PDs view patient safety/QI as an important part of resident education. Most PDs agreed that residents are adequately exposed to patient safety/QI and prepared to meet the patient safety/QI expectations of clinical practice. This conflicts with other independent studies that indicate a majority of residents feel their patient safety/QI training is inadequate and lacks formal exposure to QI tools.

A ring-based compensator IMRT system optimized for low- and middle-income countries: Design and treatment planning study.

PURPOSE: We propose a novel compensator-based IMRT system designed to provide a simple, reliable, and cost-effective adjunct technology, with the goal of expanding global access to advanced radiotherapy techniques. The system would employ easily reusable tungsten bead compensators that operate independent of a gantry (e.g., mounted in a ring around the patient). Thereby the system can be retrofitted to existing linac and cobalt teletherapy units. This study explores the quality of treatment plans from the proposed system and the dependence on associated design parameters. METHODS: We considered 60 Co-based plans as the most challenging scenario for dosimetry and benchmarked them against clinical MLC-based plans delivered on a linac. Treatment planning was performed in the Pinnacle treatment planning system with commissioning based on Monte Carlo simulations of compensated beams. 60 Co-compensator IMRT plans were generated for five patients with head-and-neck cancer and five with gynecological cancer and compared to respective IMRT plans using a 6 MV linac beam with an MLC. The dependence of dosimetric endpoints on compensator resolution, thickness, position, and number of beams was assessed. Dosimetric accuracy was validated by Monte Carlo simulations of dose distribution in a water phantom from beams with the IMRT plan compensators. RESULTS: The 60 Co-compensator plans had on average equivalent PTV coverage and somewhat inferior OAR sparing compared to the 6 MV-MLC plans, but the differences in dosimetric endpoints were clinically acceptable. Calculated treatment times for head-and-neck plans were 7.6 ± 2.0 min vs 3.9 ± 0.8 min (6 MV-MLC vs 60 Co-compensator) and for gynecological plans were 8.7 ± 3.1 min vs 4.3 ± 0.4 min. Plan quality was insensitive to most design parameters over much of the ranges studied, with no degradation found when the compensator resolution was finer than 6 mm, maximum thickness at least 2 tenth-value-layers, and more than five beams were used. Source-to-compensator distances of 53 and 63 cm resulted in very similar plan quality. Monte Carlo simulations suggest no increase in surface dose for the geometries considered here. Simulated dosimetric validation tests had median gamma pass rates of 97.6% for criteria of 3% (global)/3 mm with a 10% threshold. CONCLUSIONS: The novel ring-compensator IMRT system can produce plans of comparable quality to standard 6 MV-MLC systems. Even when 60 Co beams are used the plan quality is acceptable and treatment times are substantially reduced. 60 Co-compensator IMRT plans are adequately modeled in an existing commercial treatment planning system. These results motivate further development of this low-cost adaptable technology with translation through clinical trials and deployment to expand the reach of IMRT in low- and middle-income countries.

Incident learning in radiation oncology: A review.

Incident learning is a key component for maintaining safety and quality in healthcare. Its use is well established and supported by professional society recommendations, regulations and accreditation, and objective evidence. There is an active interest in incident learning systems (ILS) in radiation oncology, with over 40 publications since 2010. This article is intended as a comprehensive topic review of ILS in radiation oncology, including history and summary of existing literature, nomenclature and categorization schemas, operational aspects of ILS at the institutional level including event handling and root cause analysis, and national and international ILS for shared learning. Core principles of patient safety in the context of ILS are discussed, including the systems view of error, culture of safety, and contributing factors such as cognitive bias. Finally, the topics of medical error disclosure and second victim syndrome are discussed. In spite of the rapid progress and understanding of ILS, challenges remain in applying ILS to the radiation oncology context. This comprehensive review may serve as a springboard for further work.

Reirradiation for Recurrent Pediatric Central Nervous System Malignancies: A Multi-institutional Review.

PURPOSE: Reirradiation has been proposed as an effective modality for recurrent central nervous system (CNS) malignancies in adults. We evaluated the toxicity and outcomes of CNS reirradiation in pediatric patients. METHODS AND MATERIALS: The data from pediatric patients <21 years of age at the initial diagnosis who developed a recurrent CNS malignancy that received repeat radiation therapy (RT) across 5 facilities in an international pediatric research consortium were retrospectively reviewed. RESULTS: Sixty-seven pediatric patients underwent CNS reirradiation. The primary diagnoses included medulloblastoma/primitive neuroectodermal tumor (n=20; 30%), ependymoma (n=19; 28%), germ cell tumor (n=8; 12%), high-grade glioma (n=9; 13%), low-grade glioma (n=5; 7%), and other (n=6; 9%). The median age at the first course of RT was 8.5 years (range 0.5-19.5) and was 12.3 years (range 3.3-30.2) at reirradiation. The median interval between RT courses was 2.0 years (range 0.3-16.5). The median radiation dose and fractionation in equivalent 2-Gy fractions was 63.7 Gy (range 27.6-74.8) for initial RT and 53.1 Gy (range 18.6-70.1) for repeat RT. The relapse location was infield in 52 patients (78%) and surrounding the initial RT field in 15 patients (22%). Thirty-seven patients (58%) underwent gross or subtotal resection at recurrence. The techniques used for reirradiation were intensity modulated RT (n=46), 3-dimensional conformal RT (n=9), stereotactic radiosurgery (n=4; 12-13 Gy × 1 or 5 Gy × 5), protons (n=4), combined modality (n=3), 2-dimensional RT (n=1), and brachytherapy (n=1). Radiation necrosis was detected in 2 patients after the first RT course and 1 additional patient after reirradiation. Six patients (9%) developed secondary neoplasms after initial RT (1 hematologic, 5 intracranial). One patient developed a secondary neoplasm identified shortly after repeat RT. The median overall survival after completion of repeat RT was 12.8 months for the entire cohort and 20.5 and 8.4 months for patients with recurrent ependymoma and medulloblastoma after reirradiation, respectively. CONCLUSIONS: CNS reirradiation in pediatric patients could be a reasonable treatment option, with moderate survival noted after repeat RT. However, prospective data characterizing the rates of local control and toxicity are needed.

A patient safety education program in a medical physics residency.

Education in patient safety and quality of care is a requirement for radiation oncology residency programs according to accrediting agencies. However, recent surveys indicate that most programs lack a formal program to support this learning. The aim of this report was to address this gap and share experiences with a structured educational program on quality and safety designed specifically for medical physics therapy residencies. Five key topic areas were identified, drawn from published recommendations on safety and quality. A didactic component was developed, which includes an extensive reading list supported by a series of lectures. This was coupled with practice-based learning which includes one project, for example, failure modes and effect analysis exercise, and also continued participation in the departmental incident learning system including a root-cause analysis exercise. Performance was evaluated through quizzes, presentations, and reports. Over the period of 2014-2016, five medical physics residents successfully completed the program. Evaluations indicated that the residents had a positive experience. In addition to educating physics residents this program may be adapted for medical physics graduate programs or certificate programs, radiation oncology residencies, or as a self-directed educational project for practicing physicists. Future directions might include a system that coordinates between medical training centers such as a resident exchange program.

Practice patterns of palliative radiation therapy in pediatric oncology patients in an international pediatric research consortium.

BACKGROUND/OBJECTIVES: The practice of palliative radiation therapy (RT) is based on extrapolation from adult literature. We evaluated patterns of pediatric palliative RT to describe regimens used to identify opportunity for future pediatric-specific clinical trials. DESIGN/METHODS: Six international institutions with pediatric expertise completed a 122-item survey evaluating patterns of palliative RT for patients ≤21 years old from 2010 to 2015. Two institutions use proton RT. Palliative RT was defined as treatment with the goal of symptom control or prevention of immediate life-threatening progression. RESULTS: Of 3,225 pediatric patients, 365 (11%) were treated with palliative intent to a total of 427 disease sites. Anesthesia was required in 10% of patients. Treatment was delivered to metastatic disease in 54% of patients. Histologies included neuroblastoma (30%), osteosarcoma (18%), leukemia/lymphoma (12%), rhabdomyosarcoma (12%), medulloblastoma/ependymoma (12%), Ewing sarcoma (8%), and other (8%). Indications included pain (43%), intracranial symptoms (23%), respiratory compromise (14%), cord compression (8%), and abdominal distention (6%). Sites included nonspine bone (35%), brain (16% primary tumors, 6% metastases), abdomen/pelvis (15%), spine (12%), head/neck (9%), and lung/mediastinum (5%). Re-irradiation comprised 16% of cases. Techniques employed three-dimensional conformal RT (41%), intensity-modulated RT (23%), conventional RT (26%), stereotactic body RT (6%), protons (1%), electrons (1%), and other (2%). The most common physician-reported barrier to consideration of palliative RT was the concern about treatment toxicity (83%). CONCLUSION: There is significant diversity of practice in pediatric palliative RT. Combined with ongoing research characterizing treatment response and toxicity, these data will inform the design of forthcoming clinical trials to establish effective regimens and minimize treatment toxicity for this patient population.