Treatment Plan Quality Assurance Research Articles

Characterizing devices for validation of dose, dose rate, and LET in ultra high dose rate proton irradiations.

Ultra high dose rate (UHDR) radiotherapy using ridge filter is a new treatment modality known as conformal FLASH that, when optimized for dose, dose rate (DR), and linear energy transfer (LET), has the potential to reduce damage to healthy tissue without sacrificing tumor killing efficacy via the FLASH effect. Clinical implementation of conformal FLASH proton therapy has been limited by quality assurance (QA) challenges, which include direct measurement of UHDR and LET. Voxel DR distributions and LET spectra at planning target margins are paramount to the DR/LET-related sparing of organs at risk. We hereby present a methodology to achieve experimental validation of these parameters. Dose, DR, and LET were measured for a conformal FLASH treatment plan involving a 250-MeV proton beam and a 3D-printed ridge filter designed to uniformly irradiate a spherical target. We measured dose and DR simultaneously using a 4D multi-layer strip ionization chamber (MLSIC) under UHDR conditions. Additionally, we developed an "under-sample and recover (USRe)" technique for a high-resolution pixelated semiconductor detector, Timepix3, to avoid event pile-up and to correct measured LET at high-proton-flux locations without undesirable beam modifications. Confirmation of these measurements was done using a MatriXX PT detector and by Monte Carlo (MC) simulations. MC conformal FLASH computed doses had gamma passing rates of>95% (3mm/3% criteria) when compared to MatriXX PT and MLSIC data. At the lateral margin, DR showed average agreement values within 0.3% of simulation at 100Gy/s and fluctuations ∼10% at 15Gy/s. LET spectra in the proximal, lateral, and distal margins had Bhattacharyya distances of<1.3%. Our measurements with the MLSIC and Timepix3 detectors shown that the DR distributions for UHDR scenarios and LET spectra using USRe are in agreement with simulations. These results demonstrate that the methodology presented here can be used effectively for the experimental validation and QA of FLASH treatment plans.

Patient-specific quality assurance of dynamically-collimated proton therapy treatment plans.

The dynamic collimation system (DCS) provides energy layer-specific collimation for pencil beam scanning (PBS) proton therapy using two pairs of orthogonal nickel trimmer blades. While excellent measurement-to-calculation agreement has been demonstrated for simple cube-shaped DCS-trimmed dose distributions, no comparison of measurement and dose calculation has been made for patient-specific treatment plans. To validate a patient-specific quality assurance (PSQA) process for DCS-trimmed PBS treatment plans and evaluate the agreement between measured and calculated dose distributions. Three intracranial patient cases were considered. Standard uncollimated PBS and DCS-collimated treatment plans were generated for each patient using the Astroid treatment planning system (TPS). Plans were recalculated in a water phantom and delivered at the Miami Cancer Institute (MCI) using an Ion Beam Applications (IBA) dedicated nozzle system and prototype DCS. Planar dose measurements were acquired at two depths within low-gradient regions of the target volume using an IBA MatriXX ion chamber array. Measured and calculated dose distributions were compared using 2D gamma analysis with 3%/3mm criteria and low dose threshold of 10% of the maximum dose. Median gamma pass rates across all plans and measurement depths were 99.0% (PBS) and 98.3% (DCS), with a minimum gamma pass rate of 88.5% (PBS) and 91.2% (DCS). The PSQA process has been validated and experimentally verified for DCS-collimated PBS. Dosimetric agreement between the measured and calculated doses was demonstrated to be similar for DCS-collimated PBS to that achievable with noncollimated PBS.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

The Impact of Dose Calibration Point Differences Between the Treatment Plan and Patient-specific Quality Assurance in Passive Scattering Proton Beam Therapy.

Passive scattering proton beam therapy (PSPT) is performed by taking actual measurements of all pre-designated fields in a treatment plan followed by appropriate adjustments to the prescribed dose. For this reason, it is necessary to ensure precision management of the measurements (patient-specific calibration) in the administration of a planned dose. Therefore, this study investigated the impact on dose distribution in treatment planning when the patient calibration point differs from the normalized point in a treatment plan. A total of 16 cases were selected, where the patient calibration point and normalized point did not match, and the normalized point used in the treatment plan was changed to the patient calibration point using a treatment planning system (VQA ver. 2.01, HITACHI). At this point, the displacement of the relative dose at the isocenter was estimated as an error owing to the difference compared to the patient calibration point. Overall, the error was within the range of ±1.5%, with the exception of orbit cases. Calibrated points also tended to be lower than the normalized points in the treatment plan. In terms of treatment sites, a greater deviation was observed for head cases. Cases with a large deviation in sites other than the head were attributed to poor flatness within the radiation field owing to a narrower opening of the patient collimator. Dose measurement errors in PSPT due to differing calibration points were generally within ±1.5%, with higher deviations observed in head treatments because of complex structures and narrow collimator openings. A γ analysis for significant deviations showed a 98.7% passing rate, suggesting limited overall impact. It is important to select stable calibration points in dosimetry to ensure high precision in dose administration, particularly in complex treatment areas.

Knowledge-based model building for treatment planning for prostate cancer using commercial treatment planning quality assurance software tools.

This study devised a method to efficiently launch the RapidPlan model for volumetric-modulated arc therapy for prostate cancer in small- and medium-sized facilities using high-quality treatment plans with the PlanIQ software as a reference. Treatment plans were generated for 30 patients with prostate cancer to construct the RapidPlan model using PlanIQ as a reference. In the context of PlanIQ-referenced treatment planning, treatment plans were developed, such that the feasibility dose-volume histogram of each organ-at-risk fell within F ≤ 0.1. For validation of the RapidPlan model, treatment plans were formulated for 20 patients using both RapidPlan and PlanIQ, and the differences were evaluated. The results of RapidPlan model validity assessment revealed that the RapidPlan-produced treatment plans exhibited higher quality in 11 of 20 patients. No significant differences were found between the treatment plans. In conclusion, high-quality treatment plans formulated using PlanIQ as reference facilitated efficient implementation of RapidPlan modeling.

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software.

The quality of treatment planning for stage III non-small cell lung cancer varies within and between facilities due to the different professions involved in planning. Dose estimation parameters were calculated using a feasibility dose-volume histogram (FDVH) implemented in the treatment planning quality assurance software PlanIQ. This study aimed to evaluate differences in treatment planning between occupations using manual FDVH-referenced treatment planning to identify their characteristics. The study included ten patients with stage III non-small cell lung cancer, and volumetric-modulated arc therapy was used as the treatment planning technique. Fifteen planners, comprising five radiation oncologists, five medical physicists, and five radiological technologists, developed treatment strategies after referring to the FDVH. Medical physicists had a higher mean dose at D98% of the planning target volume (PTV) and a lower mean dose at D2% of the PTV than those in other occupations. Medical physicists had the lowest irradiation lung volumes (V5 Gy and V13 Gy) compared to other professions, and radiation oncologists had the lowest V20 Gy and mean lung dose. Radiological technologists had the highest irradiation volumes for dose constraints at all indexes on the normal lung volume. The quality of the treatment plans developed in this study differed between occupations due to their background expertise, even when an FDVH was used as a reference. Therefore, discussing and sharing knowledge and treatment planning techniques among professionals is essential to determine the optimal treatment plan for each facility and patient.

MO-0308 AI-based radiotherapy treatment planning quality assurance: A multi-institutional study

MO-0308 AI-based radiotherapy treatment planning quality assurance: A multi-institutional study

Optimization of the Dose Rate Effect in Tetrazolium Gellan Gel Dosimeters.

Tetrazolium salts provide an appealing candidate for 3D gel dosimeters as they exhibit a low intrinsic color, no signal diffusion and excellent chemical stability. However, a previously developed commercial product (the ClearView 3D Dosimeter) based on a tetrazolium salt dispersed within a gellan gum matrix presented a noticeable dose rate effect. The goal of this study was to find out whether ClearView could be reformulated in order to minimize the dose rate effect by optimizing of the tetrazolium salt and gellan gum concentrations and by the addition a thickening agent, ionic crosslinkers, and radical scavengers. To that goal, a multifactorial design of experiments (DOE) was conducted in small-volume samples (4-mL cuvettes). It showed that the dose rate could be effectively minimized without sacrificing the integrity, chemical stability, or dose sensitivity of the dosimeter. The results from the DOE were used to prepare candidate formulations for larger-scale testing in 1-L samples to allow for fine-tuning the dosimeter formulation and conducting more detailed studies. Finally, an optimized formulation was scaled-up to a clinically relevant volume of 2.7 L and tested against a simulated arc treatment delivery with three spherical targets (diameter 3.0 cm), requiring different doses and dose rates. The results showed excellent geometric and dosimetric registration, with a gamma passing rate (at 10% minimum dose threshold) of 99.3% for dose difference and distance to agreement criteria of 3%/2 mm, compared to 95.7% in the previous formulation. This difference may be of clinical importance, as the new formulation may allow the quality assurance of complex treatment plans, relying on a variety of doses and dose rates; thus, expanding the potential practical application of the dosimeter.

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream.

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.

Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

Predicting the complexity of head-and-neck volumetric-modulated arc therapy planning using a radiation therapy planning quality assurance software.

The more complex the treatment plan, the higher the possibility of errors in dose verification. Recently, a treatment planning quality assurance (QA) software (PlanIQ) with a function to objectively evaluate the quality of volumetric-modulated arc therapy (VMAT) treatment plans by scoring and calculating the ideal dose-volume histogram has been marketed. This study aimed to assess the association between the scores of ideal treatment plans identified using PlanIQ and the results of dose verification and to investigate whether the results of dose verification can be predicted based on the complexity of treatment plans. Dose verification was performed using an ionization chamber dosimeter, a radiochromic film, and a three-dimensional dose verification system, Delta4 PT. Correlations between the ideal treatment plan scores obtained by PlanIQ and the results of the absolute dose verification and dose distribution verification were obtained, and it was examined whether dose verifications could be predicted from the complexity of the treatment plans. Even when the score from the ideal treatment plan was high, the results of absolute dose verification and dose distribution verification were sometimes poor. However, even when the score from the ideal treatment plan was low, the absolute volume verification and dose distribution verification sometimes yielded good results. Treatment plan complexity can be determined in advance from the ideal treatment plan score calculated by PlanIQ. However, it is difficult to predict the results of dose verification using an ideal treatment plan.

The role of artificial intelligence in veterinary radiation oncology.

Veterinary radiation oncology regularly deploys sophisticated contouring, image registration, and treatment planning optimization software for patient care. Over the past decade, advances in computing power and the rapid development of neural networks, open-source software packages, and data science have been realized and resulted in new research and clinical applications of artificial intelligent (AI) systems in radiation oncology. These technologies differ from conventional software in their level of complexity and ability to learn from representative and local data. We provide clinical and research application examples of AI in human radiation oncology and their potential applications in veterinary medicine throughout the patient's care-path: from treatment simulation, deformable registration, auto-segmentation, automated treatment planning and plan selection, quality assurance, adaptive radiotherapy, and outcomes modeling. These technologies have the potential to offer significant time and cost savings in the veterinary setting; however, since the range of usefulness of these technologies have not been well studied nor understood, care must be taken if adopting AI technologies in clinical practice. Over the next several years, some practical and realizable applications of AI in veterinary radiation oncology include automated segmentation of normal tissues and tumor volumes, deformable registration, multi-criteria plan optimization, and adaptive radiotherapy. Keys in achieving success in adopting AI in veterinary radiation oncology include: establishing "truth-data"; data harmonization; multi-institutional data and collaborations; standardized dose reporting and taxonomy; adopting an open access philosophy, data collection and curation; open-source algorithm development; and transparent and platform-independent code development.

Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.

This study aimed to investigate the feasibility of using a knowledge‐based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose–volume histogram (DVH) prediction models using a commercial knowledge‐based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose–volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper–lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re‐generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge‐based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically‐generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic.

TOPAS Monte Carlo simulation for a scanning proton therapy system in SPHIC

PurposeThis study aims to use TOPAS Monte Carlo (MC) code to model the beamline of a scanned proton therapy system in Shanghai Proton and Heavy Ion Center (SPHIC). This tool can be useful for treatment plan quality assurance. Methods and materialsIn-air lateral profiles of single proton spots at five positions along the Z-axis were measured, which can then be used to calculate the angular variance, spatial angular covariance, and spatial variance at the beam exit position. The mean energy and energy spread of the primary incident beam for 15 energies were tuned by comparing with measured relative integrated depth dose at the central axis. Three spread-out Bragg peak plans in homogeneous water cubes and one plan with a half anthropomorphic head phantom were created to verify the accuracy of the model. In addition, the comparison was also performed to RayStation TPS with an MC dose calculation engine for the plans. ResultsThe average difference of spot sizes was 1.3% for all five energies. For all 15 energies, the average range (D90) difference was 0.04 mm, the mean distal-fall-off (DDF) difference was 0.04 mm, and the mean full width at half maximum (FWHM) difference was 0.10 mm. For the three homogeneous cube plans, a maximum difference of 0.2 mm for the D90 was observed, and the absolute differences in the lateral penumbras for each plan all agreed within 1 mm. For the plan on the anthropomorphic half head phantom, the mean dose deviation is less than 1.2% compared to measurements. ConclusionWe successfully developed an MC model for the proton scanning system used in SPHIC, which can be helpful for dose verification of the Treatment Planning System (TPS) and quality assurance in the future.

Tissue-equivalent dosimeters based on copper doped lithium tetraborate single crystals for radiotherapy

Advancements in radiotherapy techniques demand the development of stringent quality assurance (QA) protocols and new dosimeters for achieving better accuracy in the measurement of the delivered dose. The objective of the present study is to assess performance of in-house developed tissue equivalent lithium tetraborate (Li2B4O7) single crystal based thermoluminescent dosimeters (TLDs) for medical dosimetry in radiotherapy. For this, single crystals of copper doped lithium tetraborate, abbreviated as LTB:Cu, were grown using the Czochralski crystal pulling technique and transparent TLDs of the dimensions 3×3x0.6 mm3 in chip form were fabricated in our laboratory. Complete dosimetric characterization of TLDs was done to assess its suitability as a dosimeter for radiotherapy dosimetry. The LTB:Cu TLDs were studied for dosimetric properties such as linearity of response with delivered dose, fading, energy dependence, angular dependence and dose rate dependence. The central-axis depth dose parameters such as percentage depth dose (PDD) and output factors were measured using the developed LTB:Cu TLDs for the clinically relevant depths and field sizes and were compared with the response of commercially established TLD-100 and reference values measured by ionisationchamber.Performance of LTB:Cu TLDs has been assessed for their reliable use in dose verification of treatment planning system (TPS) and QA in radiotherapy. The TLDs were used in the clinical set up for measuring the doses to target and critical organs in the in-house developed thorax phantom and the results were compared with the TPS calculated doses. The doses were delivered using three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) techniques. The planned and measured doses agreed within the acceptable limits of 3%. These studies have shown the great potential for the applicability of the developed TLDs in the radiotherapy dosimetry applications.

Understanding machine learning classifier decisions in automated radiotherapy quality assurance

The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.

Improving the Quality of Care in Radiation Oncology using Artificial Intelligence

Radiation therapy is a complex process involving multiple professionals and steps from simulation to treatment planning to delivery, and these procedures are prone to error. Additionally, the imaging and treatment delivery equipment in radiotherapy is highly complex and interconnected and represents another risk point in the quality of care. Numerous quality assurance tasks are carried out to ensure quality and to detect and prevent potential errors in the process of care. Recent developments in artificial intelligence provide potential tools to the radiation oncology community to improve the efficiency and performance of quality assurance efforts. Targets for artificial intelligence enhancement include the quality assurance of treatment plans, target and tissue structure delineation used in the plans, delivery of the plans and the radiotherapy delivery equipment itself. Here we review recent developments of artificial intelligence applications that aim to improve quality assurance processes in radiation therapy and discuss some of the challenges and limitations that require further development work to realise the potential of artificial intelligence for quality assurance.

Evaluation of target autocrop function in nasopharyngeal carcinoma SIB IMRT plan.

A new target autocrop function was introduced in the Varian Eclipse™ treatment planning software (version 15.5 above) (Lohynská in Klin Onkol 33(4):288-294, 2020). The study aimed to evaluate this new target autocrop impact on nasopharyngeal carcinoma (NPC) plan quality and delivery efficiency. Randomly 66 approved NPC simultaneous integrated boost (SIB) intensity-modulated radiation therapy (IMRT) treatment plans were retrospectively studied. The manual cropping-based plans served as reference and were designed using sliding-window IMRT. Reference plans were re-optimized with identical plan parameters following the institutional clinical protocol, except for the redundant optimization objective of the manual cropping targets deleted. Additionally, each target within 5mm of another had one minimum objective at 100% volume and one maximum objective at 0% volume for the autocrop plans. Plan quality was assessed based on selected parameters, including TCP (tumor control probability), NTCP (normal tissue complication probability), conformality index (CI), homogeneity index (HI), and dose-volume characteristics. Additionally, the delivery efficiency, the total plan treatment time defined as a sum of monitor units (MUs) for each treated field, and delivery accuracy, γ passing rate of treatment plan quality assurance (QA) also were compared. Both the manual cropping plans and the autocrop plans could be approved by an experienced oncologist. Overall, the autocrop plans could provide approximately a 13% reduction in linac MU while maintaining comparable plan quality, radiobiological ranking, and accuracy to the manual cropping plans. The new target autocrop tip facilitated the SIB IMRT plans for nasopharyngeal cancer patients. The autocrop could guarantee the quality and delivery accuracy of the radiotherapy plan and improved the planning efficiency, treatment efficiency, and reduced machine wear and tear. It was a promising tool for optimal plan selection for NPC SIB IMRT.

Failure mode and effect analysis for linear accelerator-based paraspinal stereotactic body radiotherapy.

IntroductionParaspinal stereotactic body radiotherapy (SBRT) involves risks of severe complications. We evaluated the safety of the paraspinal SBRT program in a large academic hospital by applying failure modes and effects analysis.MethodsThe analysis was conducted by a multidisciplinary committee (two therapists, one dosimetrist, four physicists, and two radiation oncologists). The paraspinal SBRT workflow was segmented into four phases (simulation, treatment planning, delivery, and machine quality assurance (QA)). Each phase was further divided into a sequence of sub‐processes. Potential failure modes (PFM) were identified from each subprocess and scored in terms of the frequency of occurrence, severity and detectability, and a risk priority number (RPN). High‐risk PFMs were identified based on RPN and were studied for root causes using fault tree analysis.ResultsOur paraspinal SBRT process was characterized by eight simulations, 11 treatment planning, nine delivery, and two machine QA sub‐processes. There were 18, 29, 19, and eight PFMs identified from simulation, planning, treatment, and machine QA, respectively. The median RPN of the PFMs was 62.9 for simulation, 68.3 for planning, 52.9 for delivery, and 22.0 for machine QA. The three PFMs with the highest RPN were: previous radiotherapy outside the institution is not accurately evaluated (RPN: 293.3), incorrect registration between diagnostic magnetic resonance imaging and simulation computed tomography causing incorrect contours (273.0), and undetected patient movement before ExacTrac baseline (217.8). Remedies to the high RPN failures were implemented, including staff education, standardized magnetic resonance imaging acquisition parameters, and an image fusion process, and additional QA on beam steering.ConclusionsA paraspinal SBRT workflow in a large clinic was evaluated using a multidisciplinary and systematic risk analysis, which led to feasible solutions to key root causes. Treatment planning was a major source of PFMs that systematically affect the safety and quality of treatments. Accurate evaluation of external treatment records remains a challenge.

ACR–ABS–ASTRO practice parameter for the performance of radionuclide-based high-dose-rate brachytherapy

purposeThis practice parameter aims to detail the processes, qualifications of personnel, patient selection, equipment, patient and personnel safety, documentation, and quality control and improvement necessary for an HDR brachytherapy program. Methods and MaterialsThis practice parameter was revised collaboratively by the American College of Radiology (ACR), the American Brachytherapy Society (ABS), and the American Society for Radiation Oncology (ASTRO). ResultsBrachytherapy is a radiotherapeutic modality in which radionuclide or electronic sources are used to deliver a radiation dose at a distance of up to a few centimeters by surface, intracavitary, intraluminal, or interstitial application. Brachytherapy alone or combined with external beam radiotherapy plays an important role in the management and treatment of patients with cancer. High-dose-rate (HDR) brachytherapy uses radionuclides, such as iridium-192, at dose rates of ≥12 Gy/hr to a designated target point or volume, and it is an important treatment for a variety of malignant and benign conditions. Its use allows for application of high doses of radiation to defined target volumes with relative sparing of adjacent critical structures. ConclusionsHDR brachytherapy requires detailed attention to personnel, equipment, patient and personnel safety, and continuing staff education. Coordination between the radiation oncologist and treatment planning staff and effective quality assurance procedures are important components of successful HDR brachytherapy programs.