Competency-Based Medical Education in Radiation Therapy Treatment Planning.

PURPOSE: To develop a technology-enhanced education methodology with competency-based evaluation for radiation therapy treatment planning. The education program is designed for integration in the existing framework of Commission on Accreditation of Medical Physics Education Programs (CAMPEP) accredited medical physics residency programs. METHODS AND MATERIALS: This education program pairs an accessible, multi-institutional infrastructure with established medical education evaluation tools to modernize treatment planning education. This program includes 3 evaluation components: (1) competency-based evaluation, (2) inter- and intramodality comparison, and (3) learner feedback. For this study, synchronous bilateral breast cancer was selected to demonstrate a complex treatment site and nonstandardized technique. Additionally, an online study was made available to a public cohort of worldwide participants of certified Medical Dosimetrists and Medical Physicists to benchmark performance. Before evaluation, learners were given a disease site-specific education session on potential clinical treatment strategies. During the assessment, learners generated treatment plans in their institutional planning system under the direct observation of an expert evaluator. Qualitative proficiency was evaluated for all learners on a 5-point scale of graduated task independence. Quantitative dosimetry was compared between the learner cohort and public cohort. A feedback session provided learners context of multi-institutional experience through multimodality and technique comparison. After study completion, learners were provided a survey that was used to gauge their perception of the education program. RESULTS: In the public study, 34 participants submitted treatment plans. Across 3 CAMPEP-accredited residency programs, 6 learners participated in the education and evaluation program. All learners successfully completed treatment plans that met the dosimetric constraints described in the case study. All learners favorably reviewed the study either comprehensively or in specified domains. CONCLUSIONS: The competency-based education and evaluation program developed in this work has been incorporated in CAMPEP-accredited residency programs and is adaptable to other residency programs with minimal resource commitment.

OpenKBP: The open-access knowledge-based planning grand challenge and dataset.

PURPOSE: To advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. METHODS: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three-dimensional (3D) dose distributions, and (b) dose-volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out-of-sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head-and-neck cancer with radiation therapy. The data were partitioned into training ( n = 200 ), validation ( n = 40 ), and testing ( n = 100 ) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out-of-sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models. RESULTS: The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner-up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. CONCLUSION: OpenKBP is the first competition for knowledge-based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open-source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research.

An Innovative Learning Tool for Radiation Therapy Treatment Plan Evaluation: Implementation and Evaluation.

PURPOSE: To design, develop, and evaluate an interactive simulation-based learning tool for treatment plan evaluation for radiation oncology and medical physics residents to address gaps in learning. METHODS AND MATERIALS: We first conducted a needs assessment for optimal learning tool design and case selection. Next, we generated a curated database of cases with clinically unacceptable treatment plans accessible through an in-house developed interactive web-based digital imaging and communications in medicine-radiation therapy viewer. We then developed an interactive user module that allows case selection, learner participation, and immediate feedback, including the final clinically acceptable plan. We pilot tested this case bank learning tool with current radiation oncology and medical physics residents within our institution. Afterward, residents completed an evaluation of tool design, content, and perceived impact on learning and provided suggestions for improvement. RESULTS: We generated 70 cases and learning modules for the case bank, encompassing various clinical sites, levels of difficulty, and classified errors. Residents positively endorsed the learning tool, including design, content, and perceived impact on learning. The learning tool's interactivity was perceived to provide increased educational value compared with other current learning methods. CONCLUSIONS: We created a high-fidelity simulation platform for treatment plan evaluation linked to a curated case bank. Evaluation of the pilot deployment demonstrated a benefit for resident learning and competency attainment. Future directions include external validation and expansion.

The importance of evaluating the complete automated knowledge-based planning pipeline.

We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models. The first optimization model, inverse planning (IP), estimates weights for dose-objectives from a predicted dose distribution and generates new plans using conventional inverse planning. The second optimization model, dose mimicking (DM), minimizes the sum of one-sided quadratic penalties between the predictions and the generated plans using several dose-objectives. Altogether, four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and benchmarked against the corresponding clinical plans using clinical criteria; the error of both prediction methods was also evaluated. The best performing plans were GAN-IP plans, which satisfied the same criteria as their corresponding clinical plans (78%) more often than any other KBP pipeline. However, GAN did not necessarily provide the best prediction for the second-stage optimization models. Specifically, both the RF-IP and RF-DM plans satisfied the same criteria as the clinical plans 25% and 15% more often than GAN-DM plans (the worst performing plans), respectively. GAN predictions also had a higher mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that state-of-the-art prediction methods when paired with different optimization algorithms, produce treatment plans with considerable variation in quality.

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions.

PURPOSE: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&N). METHODS: Pipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development. RESULTS: Clinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33-0.93), where a PR-AUC = 0.11 is considered random. CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.

Knowledge-based automated planning with three-dimensional generative adversarial networks.

PURPOSE: To develop a knowledge-based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three-dimensional (3D) dose. METHODS: Our knowledge-based automated planning (KBAP) pipeline consisted of a knowledge-based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence-based plans, respectively. We developed a novel generative adversarial network (GAN)-based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices. Baseline comparisons were made against two state-of-the-art deep learning-based KBP methods from the literature. We also developed an additional benchmark, a two-dimensional (2D) GAN model which predicts dose to each axial slice independently. For all models, we investigated the impact of multiplicatively scaling the predictions before optimization, such that the predicted dose distributions achieved all target clinical criteria. Each KBP model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out-of-sample previously delivered treatment plans. All KBAP plans were evaluated using clinical planning criteria and compared to their corresponding clinical plans. KBP prediction quality was assessed using dose-volume histogram (DVH) differences from the corresponding clinical plans. RESULTS: The best performing KBAP plans were generated using predictions from the 3D GAN model that were multiplicatively scaled. These plans satisfied 77% of all clinical criteria, compared to the clinical plans, which satisfied 67% of all criteria. In general, multiplicatively scaling predictions prior to optimization increased the fraction of clinical criteria satisfaction by 11% relative to the plans generated with nonscaled predictions. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% and 8% more frequently as compared to the two benchmark methods, respectively. CONCLUSIONS: We developed the first knowledge-based automated planning framework using a 3D generative adversarial network for prediction. Our results, based on 217 oropharyngeal cancer treatment plans, demonstrated superior performance in satisfying clinical criteria and generated more realistic plans as compared to the previous state-of-the-art approaches.

Knowledge-based automated planning for oropharyngeal cancer.

PURPOSE: The purpose of this study was to automatically generate radiation therapy plans for oropharynx patients by combining knowledge-based planning (KBP) predictions with an inverse optimization (IO) pipeline. METHODS: We developed two KBP approaches, the bagging query (BQ) method and the generalized principal component analysis-based (gPCA) method, to predict achievable dose-volume histograms (DVHs). These approaches generalize existing methods by predicting physically feasible organ-at-risk (OAR) and target DVHs in sites with multiple targets. Using leave-one-out cross validation, we applied both models to a large dataset of 217 oropharynx patients. The predicted DVHs were input into an IO pipeline that generated treatment plans (BQ and gPCA plans) via an intermediate step that estimated objective function weights for an inverse planning model. The KBP predictions were compared to the clinical DVHs for benchmarking. To assess the complete pipeline, we compared the BQ and gPCA plans to both the predictions and clinical plans. To isolate the effect of the KBP predictions, we put clinical DVHs through the IO pipeline to produce clinical inverse optimized (CIO) plans. This approach also allowed us to estimate the complexity of the clinical plans. The BQ and gPCA plans were benchmarked against the CIO plans using DVH differences and clinical planning criteria. Iso-complexity plans (relative to CIO) were also generated and evaluated. RESULTS: The BQ method tended to predict that less dose is delivered than what was observed in the clinical plans while the gPCA predictions were more similar to clinical DVHs. Both populations of KBP predictions were reproduced with inverse plans to within a median DVH difference of 3 Gy. Clinical planning criteria for OARs were satisfied most frequently by the BQ plans (74.4%), by 6.3% points more than the clinical plans. Meanwhile, target criteria were satisfied most frequently by the gPCA plans (90.2%), and by 21.2% points more than clinical plans. However, once the complexity of the plans was constrained to that of the CIO plans, the performance of the BQ plans degraded significantly. In contrast, the gPCA plans still satisfied more clinical criteria than both the clinical and CIO plans, with the most notable improvement being in target criteria. CONCLUSION: Our automated pipeline can successfully use DVH predictions to generate high-quality plans without human intervention. Between the two KBP methods, gPCA plans tend to achieve comparable performance as clinical plans, even when controlling for plan complexity, whereas BQ plans tended to underperform.

Inverse optimization of objective function weights for treatment planning using clinical dose-volume histograms.

We developed and evaluated a novel inverse optimization (IO) model to estimate objective function weights from clinical dose-volume histograms (DVHs). These weights were used to solve a treatment planning problem to generate 'inverse plans' that had similar DVHs to the original clinical DVHs. Our methodology was applied to 217 clinical head and neck cancer treatment plans that were previously delivered at Princess Margaret Cancer Centre in Canada. Inverse plan DVHs were compared to the clinical DVHs using objective function values, dose-volume differences, and frequency of clinical planning criteria satisfaction. Median differences between the clinical and inverse DVHs were within 1.1 Gy. For most structures, the difference in clinical planning criteria satisfaction between the clinical and inverse plans was at most 1.4%. For structures where the two plans differed by more than 1.4% in planning criteria satisfaction, the difference in average criterion violation was less than 0.5 Gy. Overall, the inverse plans were very similar to the clinical plans. Compared with a previous inverse optimization method from the literature, our new inverse plans typically satisfied the same or more clinical criteria, and had consistently lower fluence heterogeneity. Overall, this paper demonstrates that DVHs, which are essentially summary statistics, provide sufficient information to estimate objective function weights that result in high quality treatment plans. However, as with any summary statistic that compresses three-dimensional dose information, care must be taken to avoid generating plans with undesirable features such as hotspots; our computational results suggest that such undesirable spatial features were uncommon. Our IO-based approach can be integrated into the current clinical planning paradigm to better initialize the planning process and improve planning efficiency. It could also be embedded in a knowledge-based planning or adaptive radiation therapy framework to automatically generate a new plan given a predicted or updated target DVH, respectively.

Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma.

PURPOSE: Distant metastasis (DM) is the main cause of death for patients with human papillomavirus (HPV)-related oropharyngeal cancers (OPCs); yet, there are few reliable predictors of DM in this disease. The role of quantitative imaging (ie, radiomic) analysis was examined to determine whether there are primary tumor features discernible on imaging studies that are associated with a higher risk of DM developing. METHODS AND MATERIALS: Radiation therapy planning computed tomography scans were retrieved for all nonmetastatic p16-positive OPC patients treated with radiation therapy or chemoradiation therapy at a single institution between 2005 and 2010. Radiomic biomarkers were derived from each gross tumor volume. The biomarkers included 4 representative radiomic features from tumor first-order statistics, shape, texture, and wavelet groups, as well as a combined 4-feature signature. Univariable Cox proportional hazards models for DM risk were identified. The discriminative performance of prognostic univariable and multivariable models was compared using the concordance index (C-index). Subgroup analyses were performed. RESULTS: There were 300 HPV-related OPC patients who were eligible for the analysis. A total of 36 DM events occurred within a median follow-up period of 5 years. On univariable analysis, top results included the 4 representative radiomic features (C-index, 0.670-0.686; P < .001), the radiomic signature (C-index, 0.670; P < .001), tumor stage (C-index, 0.633; P < .001), tumor diameter (C-index, 0.653; P < .001), and tumor volume (C-index, 0.674; P < .001), which demonstrated moderate discrimination of DM risk. Combined clinical-radiomic models yielded significantly improved performance (C-index, 0.701-0.714; P < .05). In subgroup analyses, the radiomic biomarkers consistently stratified patients for DM risk, particularly for those cohorts with greater risks (C-index, 0.663-0.796), such as patients with stage III disease. CONCLUSIONS: Radiomic biomarkers appear to classify DM risk for patients with nonmetastatic HPV-related OPC. Radiomic biomarkers could be used either alone or with other clinical characteristics in the assignment of DM risk in future HPV-related OPC clinical trials.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

Development of a Quality and Safety Competency Curriculum for Radiation Oncology Residency: An International Delphi Study.

PURPOSE: To develop an entry-to-practice quality and safety competency profile for radiation oncology residency. METHODS AND MATERIALS: A comprehensive list of potential quality and safety competency items was generated from public and professional resources and interprofessional focus groups. Redundant or out-of-scope items were eliminated through investigator consensus. Remaining items were subjected to an international 2-round modified Delphi process involving experts in radiation oncology, radiation therapy, and medical physics. During Round 1, each item was scored independently on a 9-point Likert scale indicating appropriateness for inclusion in the competency profile. Items indistinctly ranked for inclusion or exclusion were re-evaluated through web conference discussion and reranked in Round 2. RESULTS: An initial 1211 items were compiled from 32 international sources and distilled to 105 unique potential quality and safety competency items. Fifteen of the 50 invited experts participated in round 1: 10 radiation oncologists, 4 radiation therapists, and 1 medical physicist from 13 centers in 5 countries. Round 1 rankings resulted in 80 items included, 1 item excluded, and 24 items indeterminate. Two areas emerged more prominently within the latter group: change management and human factors. Web conference with 5 participants resulted in 9 of these 24 items edited for content or clarity. In Round 2, 12 participants rescored all indeterminate items resulting in 10 items ranked for inclusion. The final 90 enabling competency items were organized into thematic groups consisting of 18 key competencies under headings adapted from Deming's System of Profound Knowledge. CONCLUSIONS: This quality and safety competency profile may inform minimum training standards for radiation oncology residency programs.

Mandibular changes on panoramic imaging after head and neck radiotherapy.

OBJECTIVES: Changes to the radiographic appearance of the jaws after head and neck radiotherapy have not been thoroughly characterized. This retrospective study examines changes to the appearance of the mandible on panoramic images following intensity-modulated radiotherapy (IMRT) and relates these changes to medical co-morbidities and radiation dose. STUDY DESIGN: The medical and dental charts, and panoramic images of 126 patients who received IMRT at the Princess Margaret Hospital between January 1, 2005, and December 31, 2008, were analyzed independently by three observers. RESULTS: Of the 126 patients, 75 (60%) had post-IMRT changes, as seen on panoramic images; most, 66 (88%), consisted of widened periodontal ligament space (WPLS). The median time to WPLS was 29 months after IMRT. Female gender and radiation dose correlated with decreased time to WPLS. CONCLUSIONS: These results indicate that WPLS is a common radiographic sequela after head and neck radiotherapy, underscoring its clinical significance as a reliable marker of irradiated bone. Furthermore, this type of WPLS needs to be differentiated from odontogenic inflammatory disease and cancer recurrence to avoid unnecessary treatment that may precipitate osteoradionecrosis.

Clinical outcomes in patients with T4 laryngeal cancer treated with primary radiotherapy versus primary laryngectomy.

BACKGROUND: The purpose of this study was to determine the clinical outcomes of T4 laryngeal cancers. METHODS: T4 laryngeal cancers treated with curative intent from January 2003 to December 2010 were analyzed. Outcomes were evaluated in both primary radiotherapy (+/- chemotherapy) (RT/CRT) and primary surgery cohorts. RESULTS: Among the 65 primary RT/CRT and 42 primary surgery patients included, median follow-up was 4.4 years. There was a trend for improved locoregional control with surgery (74% vs 88%; p = .08). In the RT/CRT group the 3-year laryngectomy-free survival was 67%. The 2-year gastrostomy dependency rate was 23% with RT/CRT versus 6% with primary surgery (p = .07). Overall survival (OS) at 3 years was significantly lower in the RT/CRT versus primary surgery group (41% vs 70%; p < .01). CONCLUSION: Laryngeal preservation is achieved in over two thirds of patients with primary RT/CRT. Patients with low volume minimal cartilage involvement T4 disease may be best suited to RT/CRT. © 2016 Wiley Periodicals, Inc. Head Neck 38: E2035-E2040, 2016.

Outcomes after reirradiation for recurrent nasopharyngeal carcinoma: North American experience.

BACKGROUND: The purpose of this article was to report outcomes of reirradiation for locoregionally recurrent nasopharyngeal carcinoma (NPC). METHODS: A retrospective review was conducted of all patients with locoregionally recurrent NPC who received reirradiation between 2001 and 2012. Overall survival (OS), local control, regional control, distant control, and Radiation Therapy Oncology Group (RTOG) grades 3 to 4 late toxicity were examined. RESULTS: A total of 42 recurrent cases treated with intensity-modulated radiotherapy (IMRT; 27 patients) or non-IMRT (stereotactic radiotherapy [RT], 12 patients; 3D conformal RT, 3 patients) were identified. Median time from initial RT to recurrence was 4.6 years. Hyperfractionation with 1.1 to 1.4 Gy/fraction twice daily to a total of 40 to 60 Gy was used in 27 IMRT and 5 non-IMRT patients. The remaining 10 patients received conventional fractionation 1.8 to 2.0 Gy/fraction to 50 to 60 Gy. Median follow-up was 3.0 years. The 3-year OS, local control, regional control, distant control, and late toxicity rates were 49%, 46%, 71%, 79%, and 37%, respectively. CONCLUSION: Reirradiation for recurrent NPC, delivered mostly with hyperfractionated IMRT, can result in durable disease control with acceptable late toxicity. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1102-E1109, 2016.

Multileaf collimator performance monitoring and improvement using semiautomated quality control testing and statistical process control.

PURPOSE: High-quality radiation therapy using highly conformal dose distributions and image-guided techniques requires optimum machine delivery performance. In this work, a monitoring system for multileaf collimator (MLC) performance, integrating semiautomated MLC quality control (QC) tests and statistical process control tools, was developed. The MLC performance monitoring system was used for almost a year on two commercially available MLC models. Control charts were used to establish MLC performance and assess test frequency required to achieve a given level of performance. MLC-related interlocks and servicing events were recorded during the monitoring period and were investigated as indicators of MLC performance variations. METHODS: The QC test developed as part of the MLC performance monitoring system uses 2D megavoltage images (acquired using an electronic portal imaging device) of 23 fields to determine the location of the leaves with respect to the radiation isocenter. The precision of the MLC performance monitoring QC test and the MLC itself was assessed by detecting the MLC leaf positions on 127 megavoltage images of a static field. After initial calibration, the MLC performance monitoring QC test was performed 3-4 times/week over a period of 10-11 months to monitor positional accuracy of individual leaves for two different MLC models. Analysis of test results was performed using individuals control charts per leaf with control limits computed based on the measurements as well as two sets of specifications of ± 0.5 and ± 1 mm. Out-of-specification and out-of-control leaves were automatically flagged by the monitoring system and reviewed monthly by physicists. MLC-related interlocks reported by the linear accelerator and servicing events were recorded to help identify potential causes of nonrandom MLC leaf positioning variations. RESULTS: The precision of the MLC performance monitoring QC test and the MLC itself was within ± 0.22 mm for most MLC leaves and the majority of the apparent leaf motion was attributed to beam spot displacements between irradiations. The MLC QC test was performed 193 and 162 times over the monitoring period for the studied units and recalibration had to be repeated up to three times on one of these units. For both units, rate of MLC interlocks was moderately associated with MLC servicing events. The strongest association with the MLC performance was observed between the MLC servicing events and the total number of out-of-control leaves. The average elapsed time for which the number of out-of-specification or out-of-control leaves was within a given performance threshold was computed and used to assess adequacy of MLC test frequency. CONCLUSIONS: A MLC performance monitoring system has been developed and implemented to acquire high-quality QC data at high frequency. This is enabled by the relatively short acquisition time for the images and automatic image analysis. The monitoring system was also used to record and track the rate of MLC-related interlocks and servicing events. MLC performances for two commercially available MLC models have been assessed and the results support monthly test frequency for widely accepted ± 1 mm specifications. Higher QC test frequency is however required to maintain tighter specification and in-control behavior.

Multicenter collaborative quality assurance program for the province of Ontario, Canada: first-year results.

PURPOSE: The objective of this work was to develop a collaborative quality assurance (CQA) program to assess the performance of intensity modulated radiation therapy (IMRT) planning and delivery across the province of Ontario, Canada. METHODS AND MATERIALS: The CQA program was designed to be a comprehensive end-to-end test that can be completed on multiple planning and delivery platforms. The first year of the program included a head-and-neck (H&N) planning exercise and on-site visit to acquire dosimetric measurements to assess planning and delivery performance. A single dosimeter was used at each institution, and the planned to measured dose agreement was evaluated for both the H&N plan and a standard plan (linear-accelerator specific) that was created to enable a direct comparison between centers with similar infrastructure. RESULTS: CQA program feasibility was demonstrated through participation of all 13 radiation therapy centers in the province. Planning and delivery was completed on a variety of infrastructure (treatment planning systems and linear accelerators). The planning exercise was completed using both static gantry and rotational IMRT, and planned-to-delivered dose agreement (pass rates) for 3%/3-mm gamma evaluation were greater than 90% (92.6%-99.6%). CONCLUSIONS: All centers had acceptable results, but variation in planned to delivered dose agreement for the same planning and delivery platform was noted. The upper end of the range will provide an achievable target for other centers through continued quality improvement, aided by feedback provided by the program through the use of standard plans and simple test fields.

A new metric for assessing IMRT modulation complexity and plan deliverability.

PURPOSE: To evaluate the utility of a new complexity metric, the modulation complexity score (MCS), in the treatment planning and quality assurance processes and to evaluate the relationship of the metric with deliverability. METHODS: A multisite (breast, rectum, prostate, prostate bed, lung, and head and neck) and site-specific (lung) dosimetric evaluation has been completed. The MCS was calculated for each beam and the overall treatment plan. A 2D diode array (MapCHECK, Sun Nuclear, Melbourne, FL) was used to acquire measurements for each beam. The measured and planned dose (PINNACLE3, Phillips, Madison, WI) was evaluated using different percent differences and distance to agreement (DTA) criteria (3%/ 3 mm and 2%/ 1 mm) and the relationship between the dosimetric results and complexity (as measured by the MCS or simple beam parameters) assessed. RESULTS: For the multisite analysis (243 plans total), the mean MCS scores for each treatment site were breast (0.92), rectum (0.858), prostate (0.837), prostate bed (0.652), lung (0.631), and head and neck (0.356). The MCS allowed for compilation of treatment site-specific statistics, which is useful for comparing different techniques, as well as for comparison of individual treatment plans with the typical complexity levels. For the six plans selected for dosimetry, the average diode percent pass rate was 98.7% (minimum of 96%) for 3%/3 mm evaluation criteria. The average difference in absolute dose measurement between the planned and measured dose was 1.7 cGy. The detailed lung analysis also showed excellent agreement between the measured and planned dose, as all beams had a diode percentage pass rate for 3%/3 mm criteria of greater than 95.9%, with an average pass rate of 99.0%. The average absolute maximum dose difference for the lung plans was 0.7 cGy. There was no direct correlation between the MCS and simple beam parameters which could be used as a surrogate for complexity level (i.e., number of segments or MU). An evaluation criterion of 2%/ 1 mm reliably allowed for the identification of beams that are dosimetrically robust. In this study we defined a robust beam or plan as one that maintained a diode percentage pass rate greater than 90% at 2%/ 1 mm, indicating delivery that was deemed accurate when compared to the planned dose, even under stricter evaluation criterion. MCS and MU threshold criteria were determined by defining a required specificity of 1.0. A MCS threshold of 0.8 allowed for identification of robust deliverability with a sensitivity of 0.36. In contrast, MU had a lower sensitivity of 0.23 for a threshold of 50 MU. CONCLUSIONS: The MCS allows for a quantitative assessment of plan complexity, on a fixed scale, that can be applied to all treatment sites and can provide more information related to dose delivery than simple beam parameters. This could prove useful throughout the entire treatment planning and QA process.

Three-dimensional dosimetry of small megavoltage radiation fields using radiochromic gels and optical CT scanning.

The dosimetry of small fields as used in stereotactic radiotherapy, radiosurgery and intensity-modulated radiation therapy can be challenging and inaccurate due to partial volume averaging effects and possible disruption of charged particle equilibrium. Consequently, there exists a need for an integrating, tissue equivalent dosimeter with high spatial resolution to avoid perturbing the radiation beam and artificially broadening the measured beam penumbra. In this work, radiochromic ferrous xylenol-orange (FX) and leuco crystal violet (LCV) micelle gels were used to measure relative dose factors (RDFs), percent depth dose profiles and relative lateral beam profiles of 6 MV x-ray pencil beams of diameter 28.1, 9.8 and 4.9 mm. The pencil beams were produced via stereotactic collimators mounted on a Varian 2100 EX linear accelerator. The gels were read using optical computed tomography (CT). Data sets were compared quantitatively with dosimetric measurements made with radiographic (Kodak EDR2) and radiochromic (GAFChromic EBT) film, respectively. Using a fast cone-beam optical CT scanner (Vista), corrections for diffusion in the FX gel data yielded RDFs that were comparable to those obtained by minimally diffusing LCV gels. Considering EBT film-measured RDF data as reference, cone-beam CT-scanned LCV gel data, corrected for scattered stray light, were found to be in agreement within 0.5% and -0.6% for the 9.8 and 4.9 mm diameter fields, respectively. The validity of the scattered stray light correction was confirmed by general agreement with RDF data obtained from the same LCV gel read out with a laser CT scanner that is less prone to the acceptance of scattered stray light. Percent depth dose profiles and lateral beam profiles were found to agree within experimental error for the FX gel (corrected for diffusion), LCV gel (corrected for scattered stray light), and EBT and EDR2 films. The results from this study reveal that a three-dimensional dosimetry method utilizing optical CT-scanned radiochromic gels allows for the acquisition of a self-consistent volumetric data set in a single exposure, with sufficient spatial resolution to accurately characterize small fields.

Ionization chamber volume determination and quality assurance using micro-CT imaging.

Ion chamber dosimetry requires a high degree of precision, at all steps within the dosimetric process, in order to ensure accurate dose measurements. This work presents a novel technique for ion chamber volume determination and quality assurance, using micro-computed tomography (micro-CT). Four nominally identical Exradin A1SL chambers (0.056 cm(3)) (Standard Imaging, WI, USA) were imaged using a micro-CT system (GE Locus, GE Healthcare, London, Ontario) and irradiated in a 6 MV x-ray reference field. Air volumes were calculated from the CT datasets using 3D analysis software (Microview 2.1.1, General Electric Healthcare, London, Ontario). Differences in the volumes of each chamber determined using micro-CT images agreed with differences in the ionization response within 1% for each chamber. Calibration coefficients were then compared through cross-calibration with a calibrated ion chamber and from the CT-measured volumes. The average ratio of these values was found to be 0.958 +/- 0.009 indicating good correlation. The results demonstrate the promise of using micro-CT imaging for the absolute volumetric characterization of ion chambers. The images have the potential to be an important clinical tool for quality assurance of ion chamber construction and integrity after routine clinical usage.

The response of prototype plane-parallel ionization chambers in small megavoltage x-ray fields.

Accurate small-field dosimetry has become important with the use of multiple small fields in modern radiotherapy treatments such as IMRT and stereotactic radiosurgery. In this study, we investigate the response of a set of prototype plane-parallel ionization chambers, based upon the Exradin T11 chamber, with active volume diameters of 2, 4, 10, and 20 mm, exposed to 6 MV stereotactic radiotherapy x-ray fields. Our goal was to assess their usefulness for accurate small x-ray field dose measurements. The relative ionization response was measured in circular fields (0.5 to 4 cm diameter) as compared to a 10 x 10 cm2 reference field. A large discrepancy (approximately 40%) was found between the relative response in the smallest plane-parallel chamber and other small volume dosimeters (radiochromic film, micro-metal-oxide-semiconductor field-effect transistor and diode) used for comparison. Monte Carlo BEAMnrc simulations were used to simulate the experimental setup in order to investigate the cause of the under-response and to calculate appropriate correction factors that could be applied to experimental measurements. It was found that in small fields, the air cavity of these custom-made research chambers perturbed the secondary electron fluence profile significantly, resulting in decreased fluence within the active volume, which in turn produces a chamber under-response. It is demonstrated that a large correction to the p(fl) correction factor would be required to improve dosimetric accuracy in small fields, and that these factors could be derived using Monte Carlo simulations.