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BACKGROUND Traumatic brain injury (TBI) is a disruption in normal brain function caused by an impact of external forces on the head. TBI affects millions of individuals per year, many potentially experiencing chronic symptoms and long-term disability, creating a public health crisis and an economic burden on society. The public discourse around sport-related TBIs has increased in recent decades; however, recognition of a possible TBI remains a challenge. The fencing response is an immediate posturing of the limbs, which can occur in individuals who sustain a TBI and can be used as an overt indicator of TBI. Typically, an individual demonstrating the fencing response exhibits extension in 1 arm and flexion in the contralateral arm immediately upon impact to the head; variations of forearm posturing among each limb have been observed. The tonic posturing is retained for several seconds, sufficient for observation and recognition of a TBI. Since the publication of the original peer-reviewed article on the fencing response, there have been efforts to raise awareness of the fencing response as a visible sign of TBI through publicly available web-based platforms, such as Twitter and Wikipedia. OBJECTIVE We aimed to quantify trends that demonstrate levels of public discussion and awareness of the fencing response over time using data from Twitter and Wikipedia. METHODS Raw Twitter data from January 1, 2010, to December 31, 2019, were accessed using the RStudio package academictwitteR and queried for the text “fencing response.” Data for page views of the Fencing Response Wikipedia article from January 1, 2010, to December 31, 2019, were accessed using the RStudio packages wikipediatrend and pageviews. Data were clustered by weekday, month, half-year (to represent the American football season vs off-season), and year to identify trends over time. Seasonal regression analysis was used to analyze the relationship between the number of fencing response tweets and page views and month of the year. RESULTS Twitter mentions of the fencing response and Wikipedia page views increased overall from 2010 to 2019, with hundreds of tweets and hundreds of thousands of Wikipedia page views per year. Twitter mentions peaked during the American football season, especially on and following game days. Wikipedia page views did not demonstrate a clear weekday or seasonal pattern, but instead had multiple peaks across various months and years, with January having more page views than May. CONCLUSIONS Here, we demonstrated increased awareness of the fencing response over time using public data from Twitter and Wikipedia. Effective scientific communication through free public platforms can help spread awareness of clinical indicators of TBI, such as the fencing response. Greater awareness of the fencing response as a “red-flag” sign of TBI among coaches, athletic trainers, and sports organizations can help with medical care and return-to-play decisions. CLINICALTRIAL

Implementing tighter intensity modulated radiation therapy (IMRT) quality assurance (QA) tolerances initially resulted in high numbers of marginal or failing QA results and motivated a number of improvements to our calculational processes. This work details those improvements and their effect on results. One hundred eighty IMRT plans analyzed previously were collected and new gamma criteria were applied and compared to the original results. The results were used to obtain an estimate for the number of plans that would require additional dose volume histogram (DVH)‐based analysis and therefore predicted workload increase. For 2 months and 133 plans, the established criteria were continued while the new criteria were applied and tracked in parallel. Because the number of marginal or failing plans far exceeded the predicted levels, a number of calculational elements were investigated: IMRT modeling parameters, calculation grid size, and couch top modeling. After improvements to these elements, the new criteria were clinically implemented and the frequency of passing, questionable, and failing plans measured for the subsequent 15 months and 674 plans. The retrospective analysis of selected IMRT QA results demonstrated that 75% of plans should pass, while 19% of IMRT QA plans would need DVH‐based analysis and an additional 6% would fail. However, after applying the tighter criteria for 2 months, the distribution of plans was significantly different from prediction with questionable or failing plans reaching 47%. After investigating and improving several elements of the IMRT calculation processes, the frequency of questionable plans was reduced to 11% and that of failing plans to less than 1%. Tighter IMRT QA tolerances revealed the need to improve several elements of our plan calculations. As a consequence, the accuracy of our plans have improved, and the frequency of finding marginal or failing IMRT QA results, remains within our practical ability to respond.

Purpose The purpose of this study was to determine clinically relevant pass/question/fail criteria for gamma analysis of intensity-modulated radiation therapy quality assurance (IMRT QA) plans, identify which plans should be further analyzed with dose-volume histogram (DVH) metrics, and create a workflow for performing that DVH-based analysis. Methods A total of 11 plans, 5 prostate and 6 head/neck, were selected to represent known good plans based on their high-passing rate using conventional IMRT QA criteria. These were modified by moving the programmed MLC positions to underdose the target or overdose important structures by varying amounts. Commercially available hardware/software was used to measure and analyze all plans (76 total) using 4%/3 mm, 3%/3 mm, 3%/2 mm, and 2%/2 mm gamma criteria. Two receiver operator characteristic (ROC) curves per criterion were created to assess effective passing rates. One ROC curve was to find a higher threshold that determined a clear pass and the second to find a lower threshold to determine a clear failure. Plans between these two thresholds need DVH-based analysis to assess the clinical consequence of the dose difference. The modified plans were analyzed in the planning system and reconstructed in commercially available DVH-based analysis software to access the accuracy and usefulness of the software. Results Analysis of the ROC curves showed optimal pass and fail thresholds for plan error detection per criterion to achieve clinically relevant sensitivity and specificity. Based on measurement uncertainty and pass/fail ranges, 3%/2 mm gamma criteria with a pass threshold of 95% and a fail threshold of 90% were most optimal. DVH analysis showed good agreement with all reconstructed plans except where the changes to the MLC patterns caused the periphery of the target to be underdosed. For questionable plans, comparing the organ-specific DVHs to the physician-provided planning constraints proved to be an efficient and effective workflow since plans for which the target dose was slightly high or where organs at risk were underdosed could be released for the treatment without consulting the physician for a clinical decision. Conclusion This work indicates the potential for appreciable improvement in error detection for IMRT QA. Using effective pass/fail thresholds to determine plans that need DVH-based analysis minimizes the need for excessive, time-consuming, analysis, and making use of the dosimetric constraints of the plan minimizes the burden on physicians. Overall, DVH-based analysis is a powerful tool that can provide substantial insight over the traditional approach that does not provide structure-specific data.

This work is to show which is more relevant to cause local failures (LFs) due to patient setup uncertainty between the planning target volume (PTV) underdosage and the potential target underdosage subject to patient setup uncertainties in head and neck (H&N) cancer treated with volumetric‐modulated arc therapy (VMAT). Thirteen LFs in 10 H&N patients treated by VMAT were analyzed. Measures have been taken to minimize the chances of insufficient target delineation for these patients and the patients were clinically determined to have LF based on the PET/CT scan results by an experienced radiologist and then reviewed by a second experienced radiation oncologist. Two methods were used to identify the possible locations of LF due to underdosage: (a) examining the standard VMAT plan, in which the underdosed volume in the nominal dose distribution (UVN) was generated by subtracting the volumes receiving the prescription doses from PTVs, and (b) plan robustness analysis, in which in addition to the nominal dose distribution, six perturbed dose distributions were created by translating the CT iso‐center in three cardinal directions by the PTV margin. The coldest dose distribution was represented by the minimum of the seven doses in each voxel. The underdosed volume in the coldest dose distribution (UVC) was generated by subtracting the volumes receiving the prescription doses in the coldest dose distribution from the volumes receiving the prescription doses in the nominal dose distribution. UVN and UVC were subsequently examined for spatial association with the locations of LF. The association was tested using the binominal distribution and the Fisher's exact test of independence. We found that of 13 LFs, 11 were associated with UVCs (P = 0.011), while three were associated with UVNs (P = 0.99). We concluded that the possible target underdosage due to patient setup uncertainties appeared to be a more relevant factor associated with LF in VMAT for H&N cancer than the compromised PTV coverage at least for the patients included in this study.

Purpose This report describes the long-term outcomes of a prospective trial of intensity modulated radiation therapy (IMRT), integrating a 111 In capromab pendetide (ProstaScint) scan-directed simultaneous integrated boost (SIB) for localized prostate cancer. Methods and Materials Seventy-one patients with T1N0M0 to T4N0M0 prostate cancer were enrolled, and their ProstaScint and pelvic computed tomography scans were coregistered for treatment planning. The entire prostate received 75.6 Gy in 42 fractions with IMRT, whereas regions of increased uptake on ProstaScint scans received 82 Gy as an SIB. Patients with intermediate- and high-risk disease also received 6 months and 12 months of adjuvant hormonal therapy, respectively. Results The study enrolled 31 low-, 30 intermediate-, and 10 high-risk patients. The median follow-up was 120 months (range, 24-150 months). The 10-year biochemical control rates were 85% for the entire cohort and 84%, 84%, and 90% for patients with low-, intermediate-, and high-risk disease, respectively. The 10-year survival rate of the entire cohort was 69%. Pretreatment prostate-specific antigen level >10 ng/mL and boost volume of >10% of the prostate volume were significantly associated with poorer biochemical control and survival. The outcomes were compared with those of a cohort of 302 patients treated similarly but without the SIB and followed up for a median of 91 months (range, 6-138 months). The 5- and 10-year biochemical control rates were 86% and 61%, respectively, in patients without the SIB compared with 94% and 85%, respectively, in patients in this trial who received the SIB ( P =.02). The cohort that received an SIB did not have increased toxicity. Conclusions The described IMRT strategy, integrating multiple imaging modalities to administer 75.6 Gy to the entire prostate with a boost dose of 82 Gy, was feasible. The addition of the SIB was associated with greater biochemical control but not toxicity. Modern imaging technology can be used to locally intensify the dose to tumors and spare normal tissues, producing very favorable long-term biochemical disease control.

Purpose To present the most updated American College of Radiology (ACR) Appropriateness Criteria formed by an expert panel on the appropriate delivery of external beam radiation to manage stage T1 and T2 prostate cancer (in the definitive setting and post-prostatectomy) and to provide clinical variants with expert recommendations based on accompanying Appropriateness Criteria for target volumes and treatment planning. Methods and materials The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a panel of multidisciplinary experts. The guideline development and revision process includes an extensive analysis of current medical literature from peer-reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In instances in which evidence is lacking or equivocal, expert opinion may supplement available evidence to recommend imaging or treatment. Results The panel summarizes the most recent and relevant literature on the topic, including organ motion and localization methods, image guidance, and delivery techniques (eg, 3-dimensional conformal intensity modulation). The panel presents 7 clinical variants, including (1) a standard case and cases with (2) a distended rectum, (3) a large-volume prostate, (4) bilateral hip implants, (5) inflammatory bowel disease, (6) prior prostatectomy, and (7) a pannus extending into the radiation field. Each case outlines the appropriate techniques for simulation, treatment planning, image guidance, dose, and fractionation. Numerical rating and commentary is given for each treatment approach in each variant. Conclusions External beam radiation is a key component of the curative management of T1 and T2 prostate cancer. By combining the most recent medical literature, these Appropriateness Criteria can aid clinicians in determining the appropriate treatment delivery and personalized approaches for individual patients.

Purpose Incident learning is a critical tool to improve patient safety. The Patient Safety and Quality Improvement Act of 2005 established essential legal protections to allow for the collection and analysis of medical incidents nationwide. Methods and materials Working with a federally listed patient safety organization (PSO), the American Society for Radiation Oncology and the American Association of Physicists in Medicine established RO-ILS: Radiation Oncology Incident Learning System (RO-ILS). This paper provides an overview of the RO-ILS background, development, structure, and workflow, as well as examples of preliminary data and lessons learned. RO-ILS is actively collecting, analyzing, and reporting patient safety events. Results As of February 24, 2015, 46 institutions have signed contracts with Clarity PSO, with 33 contracts pending. Of these, 27 sites have entered 739 patient safety events into local database space, with 358 events (48%) pushed to the national database. Conclusions To establish an optimal safety culture, radiation oncology departments should establish formal systems for incident learning that include participation in a nationwide incident learning program such as RO-ILS.

Purpose The Radiation Oncology Incident Learning System (RO-ILS) receives event reports from facilities across the country. This effort extracted common error pathways seen in the data. These pathways, expressed as fault trees, demonstrate the need for, and opportunities for, preventing these errors and/or limiting their propagation to treatment. Methods and materials As of the third quarter of 2016, 2344 event reports had been submitted to RO-ILS and reviewed. A total of 396 of the reports judged highest priority were rereviewed and assigned up to 3 keywords to classify events. Based on patterns among the keyword assignments, the data were further aggregated into pathways leading to 3 general error types: "problematic plan approved for treatment," "wrong shift instructions given to therapists," and "wrong shift performed at treatment." Fault trees were created showing how different errors at different stages in the treatment process combine to flow into these general error types. Results A total of 173 of the 396 (44%) events were characterized as belonging to 1 of these 3 general error types. Ninety-nine events were defined as "problematic plan approved for treatment," 40 as "wrong shift instructions given to therapists," and 34 as "wrong shift performed at treatment." Seventy-six of these events (44%) resulted in incorrectly delivered treatment. Event discovery was by therapists (n = 76), physicists (n = 45), physicians (n = 23), dosimetrists (n = 15), or not identified (n = 9); 5 events were found as a result of the patient questioning the staff. For the event type "problematic plan approved for treatment," 64 of the 99 (65%) events were attributable to physician error: incorrect target or dosing pattern prescribed. Conclusions Data extracted from RO-ILS event reports demonstrate common error pathways in radiation oncology that propagate all the way to treatment. Additional study and coordination of efforts is needed to develop and share best practices to address the sources of these errors and curtail their propagation.

Single‐isocenter, multitarget cranial stereotactic radiosurgery (SRS) is more efficient than using an isocenter for each target, but spatial positioning uncertainties can be magnified at locations away from the isocenter. This study reports on the spatial accuracy of two frameless, linac‐based SRS systems for multitarget, single‐isocenter SRS as a function of distance from the isocenter. One system uses the ExacTrac platform for image guidance and the other localizes with cone beam computed tomography (CBCT). For each platform, a phantom with 12 target BBs distributed up to 13.8 cm from the isocenter was aligned starting from five different initial offsets and then imaged with the treatment beam at seven different gantry and couch angles. The distribution of the resulting positioning errors demonstrated the value of adding a 1‐mm PTV margin for targets up to about 7–8 cm from the isocenter. For distances 10 cm or more, the CBCT‐based alignment remained within 1.1 mm while the ExacTrac‐based alignment differed by up to 2.2 mm.

Background To compare plan robustness of volumetric modulated arc therapy (VMAT) with intensity modulated radiation therapy (IMRT) and to compare the effectiveness of 3 plan robustness quantification methods. Methods and materials The VMAT and IMRT plans were created for 9 head and neck cancer patients. For each plan, 6 new perturbed dose distributions were computed using ±3 mm setup deviations along each of the 3 orientations. Worst-case analysis (WCA), dose-volume histogram (DVH) band (DVHB), and root-mean-square dose-volume histogram (RVH) were used to quantify plan robustness. In WCA, a shaded area in the DVH plot bounded by the DVHs from the lowest and highest dose per voxel was displayed. In DVHB, we displayed the envelope of all DVHs in band graphs of all the 7 dose distributions. The RVH represents the relative volume on the vertical axis and the root-mean-square-dose on the horizontal axis. The width from the first 2 methods at different target DVH indices (such as D 95% and D 5% ) and the area under the RVH curve for the target were used to indicate plan robustness. Results were compared using Wilcoxon signed-rank test. Results The DVHB showed that the width at D 95% of IMRT was larger than that of VMAT (unit Gy) (1.59 vs 1.18) and the width at D 5% of IMRT was comparable to that of VMAT (0.59 vs 0.54). The WCA showed similar results between IMRT and VMAT plans (D 95% : 3.28 vs 3.00; D 5% : 1.68 vs 1.95). The RVH showed the area under the RVH curve of IMRT was comparable to that of VMAT (1.13 vs 1.15). No statistical significance was found in plan robustness between IMRT and VMAT. Conclusions The VMAT is comparable to IMRT in terms of plan robustness. For the 3 quantification methods, WCA and DVHB are DVH parameter–dependent, whereas RVH captures the overall effect of uncertainties.

Purpose: Incident learning systems encompass volumes, varieties, values, and velocities of underlying data elements consistent with the V’s of big data. Veracity, the 5th V however exists only if there is high inter-rater reliability (IRR) within the data elements. The purpose of this work was to assess IRR in the nationally deployed RO-ILS: Radiation Oncology-Incident Learning System (R) sponsored by the American Society for Radiation Oncology (ASTRO) and the American Association of Physicists in Medicine (AAPM). Methods: Ten incident reports covering a wide range of scenarios were created in standardized narrative and video formats and disseminated to 67 volunteers of multiple disciplines from 26 institutions along with two published narratives from the International Commission of Radiological Protection to assess IRR on a nationally representative level. The volunteers were instructed to independently enter the associated data elements in a test version of RO-ILS over a 3-week period. All responses were aggregated into a spreadsheet to assess IRR using free-marginal kappa metrics. Results: 48 volunteers from 21 institutions completed all reports in the study period. The average kappa score for all raters across all critical data elements was 0.659 [range 0.326–1.000]. Statistically significant differences (p

Purpose: IMRT QA using planar dose measurements is still a widely used method for checking the accuracy of treatment plans. A pass/fail judgment is made using gamma analysis based on a single endpoint. Using more stringent criteria is a way to increase the sensitivity to planning and delivery errors. Before such implementation, it is necessary to understand how the sensitivity to different gamma criteria settings affects gamma passing rates (GPR). Methods: 752 IMRT QA measurements were re-analyzed with varying distance to agreement (DTA) and dose difference (DD) percentages using a Matlab program. Other quantifying information such as the mean dose difference in the treatment target (defined as points that are greater than 80% of maximal dose) were stored in a relational database for retrospective analysis. Results: The average and standard deviation of GPR (%) fell from 99.84 ± (0.43) to 89.61 ± (6.08) when restricting DD from 5 − 1% respectively, as compared to a drop from 99.15 ± (1.19) to 95.00 ± (4.43), when restricting the DTA from 5 − 1 mm respectively. The mean dose difference (%) in the treatment target between measured and calculated dose was −1.96 ± (0. 83), −0.09 ± (0.98), and 1.44 ± (0. 86) for each of our institution’s three matched linear accelerators (LINAC 1, 2, and 3 respectively). For plans that are approximately 2.7 sigma below the mean GPR, an average of 78.4% of those plans were measured on LINAC 1 or 3, while only 48% of the total plans were run on those machines. Conclusion: The data demonstrates that when restricting gamma criterion, such as the DD, the greatest indicator of reduced GPR in our institution is which matched LINAC the plan was measured on. While small, these differences manifest themselves to levels comparable to other treatment related differences and possibly confound the gamma analysis.

Purpose: Several promising IMRT QA tools have been developed in recent years to combat problems found in the lack of sensitivity in planar dose measurements analyzed using consensus gamma analysis criteria. The increased complexity and added information with such devices adds not only increased time, but new challenges in determining endpoints for pass/fail criteria. Using a large cohort of previously measured planar IMRT QA data, it may be possible to correlate potentially problematic plans with calculated plan metrics that can be done a priori, such that these tools can be used only in clinically relevant situations. Methods: 90 previously measured, clinically delivered VMAT plans were exported in DICOM RT format. Using a Matlab program, plan metrics were computed based on a previously developed set of equations (Du et al. 2014). These metrics included MU-weighted beam irregularity, which quantifies an MLC shape’s deviation from that of a circle. Machine delivery parameters such as MU delivered per degree and leaf movement in millimeters per MU were also calculated. Based on a previous analysis of 394 IMRT QA measurements, a “failing” plan was defined as one with less than 85% gamma pass rate when computed at 1% dose difference and 2 mm distance to agreement; 16 of the 90 plans were identified as failing Results: A ROC curve was generated with an AUC of 0.9409. All 16 outliers were detected with a specificity of 85.1% when using a threshold value based on a linear combination of the MU-weighted plan irregularity and the leaf speed in mm/degree (average MU delivered per degree multiplied by average leaf movement in mm per MU). Conclusion: This data supports that potentially problematic VMAT plans can possibly be predicted before measurement by assessing the MU-weighted irregularity of the MLC shapes combined with the averaged leaf speed of the MLC.

It has long been standard practice in radiation oncology to report internally when a patient’s treatment has not gone as planned and to report events to regulatory agencies when legally required. Most potential errors are caught early and never affect the patient. Quality assurance steps routinely prevent errors from reaching the patient, and these “near misses” are much more frequent than treatment errors. A growing number of radiation oncology facilities have implemented incident learning systems to report and analyze both errors and near misses. Using the term “incident learning” instead of “event reporting” emphasizes the need to use these experiences to change the practice and make future errors less likely and promote an educational, non-punitive environment. There are challenges in making such a system practical and effective. Speakers from institutions of different sizes and practice environments will share their experiences on how to make such a system work and what benefits their clinics have accrued. Questions that will be addressed include: • How to create a system that is easy for front line staff to access • How to motivate staff to report • How to promote the system as positive and educational and not punitive or demeaning • How to organize the team for reviewing and responding to reports • How to prioritize which reports to discuss in depth • How not to dismiss the rest • How to identify underlying causes • How to design corrective actions and implement change • How to develop useful statistics and analysis tools • How to coordinate a departmental system with a larger risk management system • How to do this without a dedicated quality manager Some speakers’ experience is with in-house systems and some will share experience with the AAPM/ASTRO national Radiation Oncology Incident Learning System (RO-ILS). Reports intended to be of value nationally need to be comprehensible to outsiders; examples of useful reports will be shown. There will be ample time set aside for audience members to contribute to the discussion. Learning Objectives: 1. Learn how to promote the use of an incident learning system in a clinic. 2. Learn how to convert “event reporting” into “incident learning”. 3. See examples of practice changes that have come out of learning systems. 4. Learn how the RO-ILS system can be used as a primary internal learning system. 5. Learn how to create succinct, meaningful reports useful to outside readers. Gary Ezzell chairs the AAPM committee overseeing RO-ILS and has received an honorarium from ASTRO for working on the committee reviewing RO-ILS reports. Derek Brown is a director of TreatSafely.org . Brett Miller has previously received travel expenses and an honorarium from Varian. Phillip Beron has nothing to report.

Have a question about RO-ILS: Radiation Oncology Incident Learning System™, or interested in learning more? Sponsored by the American Society for Radiation Oncology (ASTRO) and AAPM, RO-ILS is the only medical specialty society-sponsored incident learning system for radiation oncology. It facilitates safer and higher quality care in radiation oncology by providing a mechanism for shared learning in a secure and non-punitive environment. Please join our RO-ILS experts for a question and answer session on Wednesday, July 15th at 8:30 in the Partners in Solutions Room in Exhibit Hall C. Our experts include: Gary Ezzell, PhD, Mayo Clinic Arizona, [Brett Miller, Phillip Beron, and Derek Brown], Emily Sanscrainte from Clarity PSO and Cindy Tomlinson, MPP, ASTRO.

Purpose: Lack of plan robustness may contribute to local failure in volumetric-modulated arc therapy (VMAT) to treat head and neck (H&N) cancer. Thus we compared plan robustness of VMAT with intensity-modulated radiation therapy (IMRT). Methods: VMAT and IMRT plans were created for 9 HN (2) dose-volume histograms (DVHs) band (DVHB); and (3) root-mean-square-dose deviation (RMSD) volume histogram (DDVH). DDVH represents the relative volume (y) on the vertical axis and the RMSD (x) on the horizontal axis. Similar to DVH, this means that y% of the volume of the indicated structure has the RMSD at least x Gy[RBE].The width from the first two methods at different target DVH indices (such as D95 and D5) and the area under the DDVH curves (AUC) for the target were used to indicate plan robustness. In these robustness quantification tools, the smaller the value, the more robust the plan is. Plan robustness evaluation metrics were compared using Wilcoxon test. Results: DVHB showed the width at D95 from IMRT to be larger than from VMAT (unit Gy) [1.59 vs 1.18 (p=0.49)], while the width at D5 from IMRT was found to be slightly larger than from VMAT [0.59 vs 0.54 (p=0.84)]. WCA showed similar results [D95: 3.28 vs 3.00 (p=0.56); D5: 1.68 vs 1.95 (p=0.23)]. DDVH showed the AUC from IMRT to be slightly smaller than from VMAT [1.13 vs 1.15 (p=0.43)]. Conclusion: VMAT plan robustness is comparable to IMRT plan robustness. The plan robustness conclusions from WCA and DVHB are DVH parameter dependent. On the other hand DDVH captures the overall effect of uncertainties on the dose to a volume of interest. NIH/NCI K25CA168984; Eagles Cancer Research Career Development; The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research Mayo ASU Seed Grant; The Kemper Marley Foundation