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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.

PurposeThe 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 materialsAn 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.ResultsA 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).ConclusionsTo 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.

Background Clinical medical physics duties include routine tasks, special procedures, and development projects. It can be challenging to distribute the effort equitably across all team members, especially in large clinics or systems where physicists cover multiple sites. The purpose of this work is to study an equitable workload distribution system in radiotherapy physics that addresses the complex and dynamic nature of effort assignment. Methods We formed a working group that defined all relevant clinical tasks and estimated the total time spent per task. Estimates used data from the oncology information system, a survey of physicists, and group consensus. We introduced a quantitative workload unit, “equivalent workday” (eWD), as a common unit for effort. The sum of all eWD values adjusted for each physicist's clinical full‐time equivalent yields a “normalized total effort” (nTE) metric for each physicist, that is, the fraction of the total effort assigned to that physicist. We implemented this system in clinical operation. During a trial period of 9 months, we made adjustments to include tasks previously unaccounted for and refined the system. The workload distribution of eight physicists over 12 months was compared before and after implementation of the nTE system. Results Prior to implementation, differences in workload of up to 50% existed between individual physicists (nTE range of 10.0%–15.0%). During the trial period, additional categories were added to account for leave and clinical projects that had previously been assigned informally. In the 1‐year period after implementation, the individual workload differences were within 5% (nTE range of 12.3%–12.8%). Conclusion We developed a system to equitably distribute workload and demonstrated improvements in the equity of workload. A quantitative approach to workload distribution improves both transparency and accountability. While the system was motivated by the complexities within an academic medical center, it may be generally applicable for other clinics.

The terms "safety and quality" (SAQ) have become inextricably linked, highly used terms that together encompass a wide range of parameters within medical departments. Safety has always been a priority in radiation oncology; quality assurance has been foundational to our practice. Despite this increased focus and attention on SAQ, the "what" of SAQ remains ill-defined, largely because of the vast number of indicators that fall under this umbrella. Similarly, the "how" of developing and maintaining the highest standards of SAQ is not formulaic and varies based on the unique setting of individual practices. There are several excellent resources available to inform SAQ in radiation oncology, including the American Society for Radiation Oncology's "Safety Is No Accident," which provides an overview of safety and quality standards and resources. This review is intended as a brief summary of key considerations, with the goal of providing a practical framework and context for improving or developing a SAQ program in radiation oncology practices. We believe that the following 10 key elements, drawn from numerous reports that have appeared over the last decade examining this topic, should be considered when conceptualizing a practice-based approach to SAQ: establishing a strong safety culture; establishing a structured program for safety and quality; establishing up-to-date, relevant, and accessible policies and procedures; a system for peer review; systems to assess and reduce risk; an educational program focused on safety and quality; development and review of meaningful quality metrics; utilization of a physics quality control system; well-defined models for staffing, training, and professional development; and finally, validation from external bodies via accreditations and audits. These 10 items are addressed herein.

Purpose Peer review during physician chart rounds is a major quality assurance and patient safety step in radiation oncology. However, the effectiveness of chart rounds in detecting problematic treatment plans is unknown. We performed a prospective blinded study of error detection at chart rounds to clarify the effectiveness of this quality assurance step. Methods and Materials Radiation Oncology Incident Learning System publications were queried for problematic plans approved for treatment that would be detectable at chart rounds. A resident physician, physicist, and dosimetrist collaboratively generated 20 treatment plans with simulated errors identical in nature to those reported to the Radiation Oncology Incident Learning System. These were inserted randomly into weekly chart rounds over 9 weeks, with a median of 2 problematic plans presented per chart rounds (range, 1-4). Data were collected on detection, attendance, length, and number of cases presented at chart rounds. Data were analyzed using descriptive statistics and univariable logistic regression with odds ratios. Results The median length of chart rounds over the study period was 60 minutes (range, 42-79); median number of cases presented per chart rounds was 45 (range, 38-50). The overall detection rate was 55% (11 of 20). Detection rates were higher for cases presented earlier in chart rounds: 75% versus 25% of problematic plans were detected within 30 minutes of start of chart rounds versus after 30 minutes (odds ratio, 0.11; 95% confidence interval, 0.01–0.88; P = .037). Detection rates showed a trend toward increase during the study period but this was not significant: 33% in weeks 1 to 5 and 73% during weeks 6 to 9 (5.3; 95% confidence interval, 0.78–36; P = .08). Conclusions The detection of clinically significant problematic plans during chart rounds could be significantly improved. Problematic plans are more frequently detected earlier in chart rounds and inserting such plans into chart rounds may enhance detection; however, larger studies are needed to confirm these findings. A multi-institutional study is planned.

Purpose Treatment planning system (TPS) dose calculation is sensitive to multileaf collimator (MLC) modeling, especially when treating with intensity‐modulated radiation therapy (IMRT) or VMAT. This study investigates the dosimetric impact of the MLC leaf‐tip model in a commercial TPS (RayStation v.6.1). The detectability of modeling errors was assessed through both measurements with an anthropomorphic head‐and‐neck phantom and patient‐specific IMRT QA using a 3D diode array. Methods and Materials An Agility MLC (Elekta Inc.) was commissioned in RayStation. Nine IMRT and VMAT plans were optimized to treat the head‐and‐neck phantom from the Imaging and Radiation Oncology Core Houston branch (IROC‐H). Dose distributions for each plan were re‐calculated on 27 beam models, varying leaf‐tip width (2.0, 4.5, and 6.5 mm) and leaf‐tip offset (−2.0 to +2.0 mm) values. Doses were compared to phantom TLD measurements. Patient‐specific IMRT QA was performed, and receiver‐operating characteristic (ROC) analysis was performed to determine the detectability of modeling errors. Results Dose calculations were very sensitive to leaf‐tip offset values. Offsets of ±1.0 mm resulted in dose differences up to 10% and 15% in the PTV and spinal cord TLDs respectively. Offsets of ±2.0 mm caused dose deviations up to 50% in the spinal cord TLD. Patient‐specific IMRT QA could not reliably detect these deviations, with an ROC area under the curve (AUC) value of 0.537 for a ±1.0 mm change in leaf‐tip offset, corresponding to >7% dose deviation. Leaf‐tip width had a modest dosimetric impact with

Purpose Previous studies suggest that within radiation oncology, medical physicists (MP) experience high workloads. Little is known about how MPs use social support (SS) in times of stress. Methods In collaboration with the Workgroup on Prevention of Medical Error, the American Association of Physicists in Medicine administered this Human Investigation Committee (HIC) approved email survey to 8566 members. Respondents were considered likely to seek SS if they answered (probably/definitely would) and unlikely to seek support if they answered (probably/definitely would not). Logistic regression was applied to determine associations between demographic factors and willingness to seek support as well as perception of barriers. Results One thousand two hundred and ninety‐seven members (15.1%) accessed and gave consent for the survey. One thousand and one (11.7%) respondents answered all relevant questions. Respondents were predominantly male (69.1%), MP in radiation oncology (81.8%), private practice (51.6%), with practice duration> 10 yr (60.2%). MPs were likely to seek SS for personal physical illness (78.63%), involvement in a medical error (73.94%) or adverse patient outcome (75.17%). MPs sought SS in the setting of personal fatigue (33.2%) or burnout (44.3%). Barriers to seeking SS were lack of time (80.3%), and uncertainty about whom to access (70.7%). MPs responded that they would be most likely to seek support from an equally experienced medical physicist colleague (81.0%). Most MPs (67.0%) identified as having experienced stressors, with serious family illness (35.2%), or burnout (32.8%) being most common. Factors associated with MPs unwillingness to seek SS for medical error included> 20 yr in practice (vs still in training — OR 0.30, P = 0.015), and male gender (OR 0.60, P = 0.003). Male gender was associated with the lowest willingness to seek support (OR 2.10, P = 0.0001), but also with fewer perceived barriers (OR 1.60, P = 0.0075). Conclusion Willingness to seek SS is demonstrated, and MPs want colleagues to provide support. Given these results, peer support could be considered among MPs.

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.

We have designed and modeled a novel optical system composed of a Laue lens coupled to an x-ray tube that produces a focused beam in an energy range near 100 keV (λ = 12.4 picometer). One application of this system is radiation therapy where it could enable treatment units that are considerably simpler and lower in cost than present technologies relying on linear accelerators. The Laue lens is made of Silicon Laue components which exploit the silicon pore optics technology. The lens concentrates photons to a small region thus allowing high dose rates at the focal area with very much lower dose rates at the skin and superficial regions. Monte Carlo simulations with Geant4 indicate a dose deposition rate of 0.2 Gy min−1 in a cylindrical volume of 0.7 mm diameter and 10 mm length, and a dose ratio of 72 at the surface (skin) compared to the focus placed 10 cm within a water phantom. Work is ongoing to newer generation crystal technologies to increase dose rate.

Emerging data indicates SGRT could improve safety and quality by preventing errors in its capacity as an independent system in the treatment room. The aim of this work is to investigate the utility of SGRT in the context of safety and quality. Three incident learning systems (ILS) were reviewed to categorize and quantify errors that could have been prevented with SGRT: SAFRON (International Atomic Energy Agency), UW-ILS (University of Washington) and AvIC (Skane University Hospital). A total of 849/9737 events occurred during the pre-treatment review/verification and treatment stages. Of these, 179 (21%) events were predicted to have been preventable with SGRT. The most common preventable events were wrong isocentre (43%) and incorrect accessories (34%), which appeared at comparable rates among SAFRON and UW-ILS. The proportion of events due to wrong accessories was much smaller in the AvIC ILS, which may be attributable to the mandatory use of SGRT in Sweden. Several case scenarios are presented to demonstrate that SGRT operates as a valuable complement to other quality-improvement tools routinely used in radiotherapy. Cases are noted in which SGRT itself caused incidents. These were mostly related to workflow issues and were of low severity. Severity data indicated that events with the potential to be mitigated by SGRT were of higher severity for all categories except wrong accessories. Improved vendor integration of SGRT systems within the overall workflow could further enhance its clinical utility. SGRT is a valuable tool with the potential to increase patient safety and treatment quality in radiotherapy.

Purpose/objective(s) Despite the prevalence and importance of peer review, there remains a paucity of evidence on different formats of peer review. The purpose of this study was to understand current perceptions of disease-site specific chart rounds at a single institution considering transitioning from practice-site specific chart rounds to disease-site specific chart rounds. Materials/methods An electronic survey was distributed to faculty (24 attendings and 18 physicists), dosimetrists, nurses, therapists, and trainees at an academic institution that has weekly departmental chart rounds. The survey consisted of 13 questions on demographics, perceptions of current chart rounds, and perceptions of disease-site specific chart rounds. ANOVA and Chi-square testing were used to analyze the data. Criteria for statistical significance was P Results A total of 35 (55%) responses were received, 18 of 24 (75%) attendings responded. Of all respondents, 51% were attendings, 23% were physicists, 14% were residents, 9% were dosimetrists, and 3% was a therapist. Attending responses were distributed across all disease sites which included CNS, Head and Neck, Thoracic, Breast, GI, GU, GYN, Pediatric, Palliative, Skin, Sarcoma, and Hematologic (P = 0.218). Most respondents (19; 54%) favored transitioning to disease-specific chart rounds (26% strongly agreed and 29% agreed, whereas 11% disagreed and 9% strongly disagreed). Most respondents (23; 66%) believed disease-specific chart rounds would improve patient safety (26% strongly agreed and 40% agreed, while 11% disagreed and 3% strongly disagreed). Most respondents (27; 77%) believed disease-site specific chart rounds would improve the quality of patient plans (29% strongly agreed and 49% agreed, whereas 11% disagreed and 3% strongly disagreed). Most respondents (23; 66%) believed disease-site specific chart rounds would improve education for trainees (29% strongly agreed and 37% agreed, while 11% disagreed and none strongly disagreed). Most attendings favored transitioning to disease-specific chart rounds (50% agreed and 17% disagreed). All physicists favored transitioning to disease-specific chart rounds. Respondents who favored transitioning to disease-specific chart rounds were significantly more likely to believe that disease-specific chart rounds would improve patient safety (P Conclusion Most respondents favored transitioning to disease-specific chart rounds. Furthermore, most respondents believed disease-specific chart rounds would improve patient safety, the quality of patient plans, and trainee education. Further research will be conducted and presented measuring the impact after implementation.

Highlights • COVID-19 impact on safety in radiation oncology. • Failure modes and effects analysis applied to pandemic response. • Examination of how infection control measures impact radiotherapy workflow. • Multi-institution study of COVID-19-related practices in radiation oncology departments., Purpose The COVID-19 pandemic has presented challenges to delivering safe and timely care for cancer patients. The oncology community has undertaken substantial workflow adaptations to reduce transmission risk for patients and providers. While various control measures have been proposed and implemented, little is known about their impact on safety of the radiation oncology workflow and potential for transmission. The objective of this study was to assess potential safety impacts of control measures employed during the COVID-19 pandemic. Methods A multi-institutional study was undertaken to assess the risks of pandemic-associated workflow adaptations using failure mode and effects analysis (FMEA). Failure modes were identified and scored using FMEA formalism. FMEA scores were used to identify highest-risk aspects of the radiation therapy process. The impact of control measures on overall risk was quantified. Agreement among institutions was evaluated. Results Thirty three failure modes and 22 control measures were identified. Control measures resulted in risk score reductions for 22 of the failure modes, with the largest reductions from screening of patients and staff, requiring use of masks, and regular cleaning of patient areas. The median risk score for all failure modes was reduced from 280 to 168. There was high institutional agreement for 90.3% of failure modes but only 47% of control measures. Conclusions COVID-related risks are similar across oncology practices in this study. While control measures can reduce risk, their use varied. The effectiveness of control measures on risk may guide selection of the highest-impact workflow adaptions to ensure safe care in oncology.

Purpose Medical errors in radiation oncology sometimes involve tasks reliant on practitioners’ grasp of numeracy. Numeracy has been shown to be suboptimal across various health care professionals. Herein, we assess health numeracy among American Society of Radiologic Technologists (ASRT) members. Methods and materials The Numeracy Understanding for Medicine instrument (NUMi), an instrument to measure numeracy in the general population, was adapted to oncology for this study and distributed to ASRT members (n = 14,228) in 2017. Per NUMi scoring, health numeracy scores were categorized as low (0-7), low average (8-12), high average (13-17), or high (18-20). The impact of cGy versus Gy on numeracy performance was investigated. Spearman’s rho and a Wilcox-Mann-Whitney test were used for comparisons between the different groups. Results A total of 662 eligible participants completed the instrument and identified as radiation oncology professionals. In the cGy and Gy NUMi scores, approximately 2% of respondents scored low-average, approximately 40% scored high-average, and approximately 58% scored high, with a median score of 18.0. Although the optimum NUMi score for ASRT members is unknown, one might expect our cohort to have numeracy skills at least as high as college freshmen. Roughly one-sixth of our study group scored at or below the average score of college freshmen (NUMi = 15). In the subset analysis of NUMi questions pertaining to radiation dose unit (cGy vs Gy), respondents performed better with cGy (mean score: 2.94; range, 2-3) versus Gy (mean: 2.91; range, 0-3; P = .011). Conclusions In this study of limited sample size, overall numeracy is quite good compared with the general population. However, the range of scores is wide, and some respondents have lower scores that may be concerning, suggesting that numeracy may be an issue that requires improvement for a subset of the studied cohort. Performance was superior with the unit cGy; thus, the adoption of cGy as the standard unit is reasonable.

Purpose To characterize the participation of radiation oncology (RO) in reporting quality metrics through the Centers for Medicare and Medicaid Services’ (CMS) Hospital Compare database and to describe the association of hospital characteristics with RO-specific quality metrics. Methods and Materials Data from the CMS Hospital Compare, International Atomic Energy Agency’s Directory of Radiotherapy Centre, 2010 US Census, and CMS Inpatient Prospective Payment System were linked to create an integrated data set of geographic information, facility characteristics, and quality measures, focusing on the use of external beam radiation therapy (EBRT) for bony metastases. Results Of 4829 hospitals in the Hospital Compare database, 2030 had access to radiation therapy. Among these, 814 (40%) reported on the rate of guideline-concordant EBRT for bony metastases, a RO-specific quality measure. A total of 33,614 eligible cases of bony metastases treated with EBRT were sampled. Participation in quality reporting varied significantly by geography, population type, teaching status, hospital ownership, hospital type, and hospital size. The median rate of guideline-concordant palliative EBRT utilization was 89%. Nine percent of 814 centers had a compliance rate of less than 50%. On multivariable analysis, increasing number of cases sampled (odds ratio 0.93, P = .028), increasing hospital star-rating, and above-average patient experience rating (odds ratio 0.58, P = .024) remained significantly associated with decreased odds of falling into the lowest quartile of guideline-concordant EBRT utilization. Conclusions RO participation in a large, national quality improvement effort is nascent and reveals potential quality gaps between hospitals offering palliative EBRT for bone metastases. More robust RO-specific quality measures are needed.

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.

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

The biological effectiveness of proton beams varies with depth, spot size and lateral distance from the beam central axis. The aim of this work is to incorporate proton relative biological effectiveness (RBE) and equivalent uniform dose (EUD) considerations into comparisons of broad beam and highly modulated proton minibeams. A Monte Carlo model of a small animal proton beamline is presented. Dose and variable RBE is calculated on a per-voxel basis for a range of energies (30-109 MeV). For an open beam, the RBE values at the beam entrance ranged from 1.02-1.04, at the Bragg peak (BP) from 1.3 to 1.6, and at the distal end of the BP from 1.4 to 2.0. For a 50 MeV proton beam, a minibeam collimator designed to produce uniform dose at the depth of the BP peak, had minimal impact on the open beam RBE values at depth. RBE changes were observed near the surface when the collimator was placed flush with the irradiated object, due to a higher neutron contribution derived from proton interactions with the collimator. For proton minibeams, the relative mean RBE weighted entrance dose (RWD) was ~25% lower than the physical mean dose. A strong dependency of the EUD with fraction size was observed. For 20 Gy fractions, the EUD varied widely depending on the radiosensitivity of the cells. For radiosensitive cells, the difference was up to ~50% in mean dose and ~40% in mean RWD and the EUD trended towards the valley dose rather than the mean dose. For comparative studies of uniform dose with spatially fractionated proton minibeams, EUD derived from a per-voxel RWD distribution is recommended for biological assessments of reproductive cell survival and related endpoints.

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 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.

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.

Background While the review of radiotherapy treatment plans and charts by a medical physicist is a key component of safe, high-quality care, very few specific recommendations currently exist for this task. Aims The goal of TG-275 is to provide practical, evidence-based recommendations on physics plan and chart review for radiation therapy. While this report is aimed mainly at medical physicists, others may benefit including dosimetrists, radiation therapists, physicians and other professionals interested in quality management. Methods The scope of the report includes photon/electron external beam radiotherapy (EBRT), proton radiotherapy, as well as high-dose rate (HDR) brachytherapy for gynecological applications (currently the highest volume brachytherapy service in most practices). The following review time points are considered: initial review prior to treatment, weekly review, and end-of-treatment review. The Task Group takes a risk-informed approach to developing recommendations. A failure mode and effects analysis was performed to determine the highest-risk aspects of each process. In the case of photon/electron EBRT, a survey of all American Association of Physicists in Medicine (AAPM) members was also conducted to determine current practices. A draft of this report was provided to the full AAPM membership for comment through a 3-week open-comment period, and the report was revised in response to these comments. Results The highest-risk failure modes included 112 failure modes in photon/electron EBRT initial review, 55 in weekly and end-of-treatment review, 24 for initial review specific to proton therapy, and 48 in HDR brachytherapy. A 103-question survey on current practices was released to all AAPM members who self-reported as working in the radiation oncology field. The response rate was 33%. The survey data and risk data were used to inform recommendations. Discussion Tables of recommended checks are presented and recommendations for best practice are discussed. Suggestions to software vendors are also provided. Conclusions TG-275 provides specific recommendations for physics plan and chart review which should enhance the safety and quality of care for patients receiving radiation treatments.

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.

Purpose Tools for assessing the severity and risk of near-miss events in radiation oncology are few and needed. Recent work has described guidelines for the use of a 5-tier near-miss risk index (NMRI) for the classification of near-miss events. The purpose of this study was to assess the reliability of the NMRI among users in a radiation oncology department. Methods and materials Reliability of the NMRI was assessed using an online survey distributed to members of a radiation oncology department. The survey contained 70 events extracted from the department's incident learning system (ILS). Survey participants rated each event using the NMRI guidelines, reported their attendance to weekly ILS meetings (used as a surrogate for familiarity with the ILS), and indicated their familiarity with the radiation oncology workflow. Interrater reliability was determined using Krippendorff's alpha. Use of the NMRI to rate actual events during 5 weekly ILS meetings was also assessed and interrater reliability determined. Results Twenty-eight survey respondents represented a wide variety of care providers. Krippendorff's alpha was calculated for the whole respondent cohort to be 0.376, indicating fair agreement among raters. Respondents who had the most participation at ILS meetings (n = 4) had moderate agreement with an alpha of 0.501. Interestingly, there were significant differences in reliability and median NMRI scores between professions. NMRI use during weekly NMRI meetings (80 events rated), participants showed moderate reliability (alpha = 0.607). Conclusions Using the NMRI guidelines, raters from a wide variety of professions were able to assess the severity of near-miss incidents with fair agreement. Those experienced with the ILS showed better agreement, and higher agreement was seen during multidisciplinary ILS meetings. These data support the use the indices such as the NMRI for near-miss risk assessment in patient safety and prioritization of process improvements in radiation oncology.

There is a growing awareness of the gaps in the technical methods employed in radiation biology experiments. These quality gaps can have a substantial effect on the reliability and reproducibility of results as outlined in several recent meta-studies. This is especially true in the context of the newer laboratory irradiation technologies. These technologies allow for delivery of highly localized dose distributions and increased spatial accuracy but also present increased challenges of their own. In this article, we highlight some of the features of the new technologies and the experiments they support; this includes image-guided localized radiation systems, microirradiator systems using carbon nanotubes and physical radiation modifiers like gold nanoparticles. We discuss the key technical issues related to the consistency and quality of modern radiation biology experiments including dosimetry protocols that are essential to all experiments, quality assurance approaches, methods to validate physical radiation targeting including immunohistochemical assays and other biovalidation approaches. We highlight the future needs in terms of education and training and the creation of tools for cross-institutional benchmarking quality in preclinical studies. The demands for increased experimental rigor are challenging but can be met with an awareness and a systematic approach which ensures quality.

Big data research refers to the collection and analysis of large sets of data elements and interrelationships that are difficult to process with traditional methods. It can be considered a subspecialty of the medical informatics domain under data science and analytics. This approach has been used in many areas of medicine to address topics such as clinical care and quality assessment (1–3). The need for informatics research in radiation oncology emerged as an important initiative during the 2013 National Institutes of Health (NIH)–National Cancer Institute (NCI), American Society for Radiation Oncology (ASTRO), and American Association of Physicists in Medicine (AAPM) workshop on the topic “Technology for Innovation in Radiation Oncology” (4). Our existing clinical practice generates discrete, quantitative, and structured patient-specific data (eg, images, doses, and volumes) that position us well to exploit and participate in big data initiatives. The well-established electronic infrastructure within radiation oncology should facilitate the retrieval and aggregation of much of the needed data. With additional efforts to integrate structured data collection of patient outcomes and assessments into the clinical workflow, the field of radiation oncology has a tremendous opportunity to generate large, comprehensive patient-specific data sets (5). However, there are major challenges to realizing this goal. For example, existing data are presently housed across different platforms at multiple institutions and are often not stored in a standardized manner or with common terminologies to enable pooling of data. In addition, many important data elements are not routinely discretely captured in clinical practice. There are cultural, structural, and logistical challenges (eg, computer compatibility and workflow demands) that will make the dream of big data research difficult. The big data research workshop provided a forum for leaders in cancer registries, incident report quality-assurance systems, radiogenomics, ontology of oncology, and a wide range of ongoing big data and cloud computing development projects to interact with peers in radiation oncology to develop strategies to harness data for research, quality assessment, and clinical care. The workshop provided a platform to discuss items such as data capture, data infrastructure, and protection of patient confidentiality and to improve awareness of the wide-ranging opportunities in radiation oncology, as well as to enhance the potential for research and collaboration opportunities with NIH on big data initiatives. The goals of the workshop were as follows: To discuss current and future sources of big data for use in radiation oncology research, To identify ways to improve our current data collection methods by adopting new strategies used in fields outside of radiation oncology, and To consider what new knowledge and solutions big data research can provide for clinical decision support for personalized medicine.

Incident learning systems (ILSs) are a key component of improving patient safety in radiation oncology, but the practicalities of ILS implementation can present major challenges. We describe the implementation and best practices derived from 2 years of experience with institutional incident learning, with details on root cause analysis (RCA), a list of key process improvements, and operational aspects of ILS use. The structure of the ILS is consistent with recommendations from the American Association of Physicists in Medicine (AAPM). Workflow is analyzed for incident reports from initial reporting to analysis and feedback to the reporter, including staffing required. A system for incident categorization is shown, as well as sample events selected for root cause analysis, and suggestions for providing feedback to users of ILS. In the first 2 years of the ILS implementation from 2012 to 2014, 1897 near-miss incidents were reported. There is widespread participation in the ILS program across all professional groups inside the department, with at least 75 % of clinical staff having filed at least one report. Total workload for the ILS program is estimated to be approximately one full-time employee, shared by approximately eight team members. Fifteen events were selected for RCA during this period. Eighteen major process improvement projects are described, ranging from issues related to process standardization, automation, staffing, new organization structures, and equipment purchase. A unique high reporting volume institutional ILS has successfully resulted in numerous improvements in process, safety, and quality. Details for implementation and best practices for incident learning have been presented to allow adaptation in other practices.

Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically during the documentation of patient positioning and localization of the patient. Incidents were most frequently detected during treatment delivery (30%), and incidents identified at this point also had higher severity scores than other workflow areas (NMRI = 1.6). Incidents identified during on-treatment quality management were also more severe (NMRI = 1.7), and the specific process steps of reviewing portal and CBCT images tended to catch highest-severity incidents. On average, safety barriers caught 46% of all incidents, most frequently at physics chart review, therapist’s chart check, and the review of portal images; however, most of the incidents that pass through a particular safety barrier are not designed to be capable of being captured at that barrier. Conclusions: Incident learning systems can be used to assess the most common points of error origination and detection in radiation oncology. This can help tailor safety improvement efforts and target the highest impact portions of the workflow. The most severe near-miss events tend to originate during simulation, with the most severe near-miss events detected at the time of patient treatment. Safety barriers can be improved to allow earlier detection of near-miss events.

Radiation-induced lymphopenia (RIL) is associated with inferior survival in patients with glioblastoma, lung cancer, and pancreatic cancer. We asked whether stereotactic body radiation therapy (SBRT) decreases severity of RIL compared to conventional chemoradiation therapy (CRT) in locally advanced pancreatic cancer (LAPC).Serial total lymphocyte counts (TLCs) from patients enrolled in a prospective trial of SBRT for LAPC were compared to TLCs from an existing database of LAPC patients undergoing definitive CRT. SBRT patients received 33 Gy (6.6 Gy × 5 fractions). CRT patients received a median dose of 50.4 Gy (1.8 Gy × 28 fractions) with concurrent 5-fluorouracil (77%) or gemcitabine (23%) therapy. Univariate and multivariate analyses (MVA) were used to identify associations between clinical factors and post-treatment TLC and between TLC and survival.Thirty-two patients received SBRT and 101 received CRT. Median planning target volume (PTV) was smaller in SBRT (88.7 cm(3)) than in CRT (344.6 cm(3); P.001); median tumor diameter was larger for SBRT (4.6 cm) than for CRT (3.6 cm; P=.01). SBRT and CRT groups had similar median baseline TLCs. One month after starting radiation, 71.7% of CRT patients had severe lymphopenia (ie, TLC500 cells/mm(3) vs 13.8% of SBRT patients; P.001). At 2 months, 46.0% of CRT patients remained severely lymphopenic compared with 13.6% of SBRT patients (P=.007). MVA demonstrated that treatment technique and baseline TLCs were significantly associated with post-treatment TLC at 1 but not 2 months after treatment. Higher post-treatment TLC was associated with improved survival regardless of treatment technique (hazard ratio [HR] for death: 2.059; 95% confidence interval: 1.310-3.237; P=.002).SBRT is associated with significantly less severe RIL than CRT at 1 month in LAPC, suggesting that radiation technique affects RIL and supporting previous modeling studies. Given the association of severe RIL with survival in LAPC, further study of the effect of radiation technique on immune status is warranted.

PURPOSE/OBJECTIVES There is little consensus regarding the application of stereotactic radiotherapy (SRT) in pediatrics. We evaluated patterns of pediatric SRT practice through an international research consortium. MATERIALS AND METHODS Eight international institutions with pediatric expertise completed a 124-item survey evaluating patterns of SRT use for patients 21 years old and younger. Frequencies of SRT use and median margins applied with and without SRT were evaluated. RESULTS Across institutions, 75% reported utilizing SRT in pediatrics. SRT was used in 22% of brain, 18% of spine, 16% of other bone, 16% of head and neck, and

Purpose The review of a radiation therapy plan by a physicist prior to treatment is a standard tool for ensuring the quality of treatments. However, little is known about how well this task is performed in practice. The goal of this study is to present a novel method to measure the effectiveness of physics plan review by introducing simulated errors into computerized "mock" treatment charts and measuring the performance of plan review by physicists. Methods We generated six simulated treatment charts containing multiple errors. To select errors, we compiled a list based on events from a departmental incident learning system and an international incident learning system (SAFRON). Seventeen errors with the highest scores for frequency and severity were included in the simulations included six mock treatment charts. Eight physicists reviewed the simulated charts as they would a normal pretreatment plan review, with each chart being reviewed by at least six physicists. There were 113 data points for evaluation. Observer bias was minimized using a simple error vs hidden error approach, using detectability scores for stratification. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. Results Simulated errors were detected in 67% of reviews [58-75%] (95% confidence interval [CI] in brackets). Of the errors included in the simulated plans, the following error scenarios had the highest detection rates: an incorrect isocenter in DRR (93% [70-99%]), a planned dose different from the prescribed dose (92% [67-99%]) and invalid QA (85% [58-96%]). Errors with low detection rates included incorrect CT dataset (0%, [0-39%]) and incorrect isocenter localization in planning system (38% [18-64%]). Detection rates of errors from simulated charts were compared against observed detection rates of errors from a departmental incident learning system. Conclusions It has been notoriously difficult to quantify error and safety performance in oncology. This study uses a novel technique of simulated errors to quantify performance and suggests that the pretreatment physics plan review identifies some errors with high fidelity while other errors are more challenging to detect. These data will guide future work on standardization and automation. The example process studied here was physics plan review, but this approach of simulated errors may be applied in other contexts as well and may also be useful for training and education purposes.

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.

Purpose: To share our clinical experience of an optimized and comprehensive pediatric TBI technique. Methods and Materials: Through the use of incident learning, safety-critical areas were identified in our procedure for total body irradiation (TBI) for pediatric patients under anesthesia for bone-marrow transplant. The previous procedure lacked flexibility to accommodate various requests from the anesthesia team due to the wide range of patient sizes. To address this issue and to improve the process overall, we updated our procedure for TBI simulation, dosimetry planning, patient setup during treatment, and in vivo dosimetry. A simulation form was redesigned with additional detailed instructions and documentation requirements. The dose calculation procedure was reformulated to remove dependence on setup variations. Tissue compensation determination and therefore dose uniformity were improved by introducing rigorous calculation methods. Calculations were performed on 28 previously-treated patients to compare the dose uniformity using the new versus previous methods. Results: The new procedures improve interdepartmental communication, simplify the workflow, decrease the risk of treating patients in a setup that differs from that used during the simulation, and reduce dose heterogeneity. The new compensator design significantly improved patient dose uniformity: 0.8% ± 0.4% (new method) vs. 4.2% ± 2.3% (previous method) (p Conclusion: A near-miss incident reporting system was used to improve the safety and quality of pediatric TBI procedures under anesthesia.

Purpose: To quantify the ability of electronic portal imaging device(EPID)dosimetry used during treatment (in vivo) in detecting variations that can occur in the course of patient treatment. Methods: Images of transmitted radiation from in vivoEPID measurements were converted to a 2D planar dose at isocenter and compared to the treatment planningdose using a prototype software system. Using the treatment planning system (TPS), four different types of variability were modeled: overall dose scaling, shifting the positions of the multileaf collimator(MLC) leaves, shifting of the patient position, and changes in the patient body contour. The gamma pass rate was calculated for the modified and unmodified plans and used to construct a receiver operator characteristic (ROC) curve to assess the detectability of the different parameter variations. The detectability is given by the area under the ROC curve (AUC). The TPS was also used to calculate the impact of the variations on the target dose–volume histogram. Results: Nine intensity modulation radiation therapy plans were measured for four different anatomical sites consisting of 70 separate fields. Results show that in vivoEPIDdosimetry was most sensitive to variations in the machine output, AUC = 0.70 − 0.94, changes in patient body habitus, AUC = 0.67 − 0.88, and systematic shifts in the MLC bank positions, AUC = 0.59 − 0.82. These deviations are expected to have a relatively small clinical impact [planning target volume (PTV) D99 change

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: Complex treatments in radiation therapy require robust verification in order to prevent errors that can adversely affect the patient. For this purpose, the authors estimate the effectiveness of detecting errors with a “defense in depth” system composed of electronic portal imaging device (EPID) based dosimetry and a software-based system composed of rules-based and Bayesian network verifications. Methods: The authors analyzed incidents with a high potential severity score, scored as a 3 or 4 on a 4 point scale, recorded in an in-house voluntary incident reporting system, collected from February 2012 to August 2014. The incidents were categorized into different failure modes. The detectability, defined as the number of incidents that are detectable divided total number of incidents, was calculated for each failure mode. Results: In total, 343 incidents were used in this study. Of the incidents 67% were related to photon external beam therapy (EBRT). The majority of the EBRT incidents were related to patient positioning and only a small number of these could be detected by EPID dosimetry when performed prior to treatment (6%). A large fraction could be detected by in vivo dosimetry performed during the first fraction (74%). Rules-based and Bayesian network verifications were found to be complimentary to EPID dosimetry, able to detect errors related to patient prescriptions and documentation, and errors unrelated to photon EBRT. Combining all of the verification steps together, 91% of all EBRT incidents could be detected. Conclusions: This study shows that the defense in depth system is potentially able to detect a large majority of incidents. The most effective EPID-based dosimetry verification is in vivo measurements during the first fraction and is complemented by rules-based and Bayesian network plan checking.

Purpose: Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. Methods: FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. Results: FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). Conclusions: FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high-severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.

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 8,000 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. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances.