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Introduction A novel approach of in-advance preparatory respiratory training and practice for deep inspiration breath holding (DIBH) has been shown to further reduce cardiac dose in breast cancer radiotherapy patients, enabled by deeper (extended) DIBH. Here we investigated the consistency and stability of such training-induced extended DIBH after training completion and throughout the daily radiotherapy course. Methods Daily chestwall motion from real-time surface tracking transponder data was analysed in 67 left breast radiotherapy patients treated in DIBH. Twenty-seven received preparatory DIBH training/practice (prep Trn) 1-2 weeks prior to CT simulation, resulting in an extended DIBH (ext DIBH) and reduced cardiac dose at simulation. Forty had only conventional immediate pre-procedure DIBH instruction without prep Trn and without extended DIBH (non-Trn group). Day-to-day variability in chestwall excursion pattern during radiotherapy was compared among the groups. Results The average of daily maximum chestwall excursions was overall similar, 2.5 ± 0.6 mm for prep Trn/ext DIBH vs. 2.9 ± 0.8 mm for non-Trn patients (P = 0.24). Chestwall excursions beyond the 3-mm tolerance threshold were less common in the prep Trn/ext DIBH group (18.8% vs. 37.5% of all fractions within the respective groups, P = 0.038). Among patients with cardiopulmonary disease those with prep Trn/ext DIBH had fewer chestwall excursions beyond 3 mm (9.4% vs. 46.7%, P = 0.023) and smaller average maximum excursions than non-Trn patients (2.4 ± 0.3 vs. 3.0 ± 0.6 mm, P = 0.047, respectively). Conclusion Similar stability of daily DIBH among patients with and without preparatory training/practice suggests that the training-induced extended DIBH and cardiac dose reductions were effectively sustained throughout the radiotherapy course. Training further reduced beyond-tolerance chestwall excursions, particularly in patients with cardiopulmonary disease.

One of the common challenges in delivering complex healthcare procedures such as radiation oncology is the organization and sharing of information in ways that facilitate workflow and prevent treatment delays. Within the major vendors of Oncology Information Systems (OIS) is a lack of tools and displays to assist in task timing and workflow processes. To address this issue, we developed an electronic whiteboard integrated with a local OIS to track, record, and evaluate time frames associated with clinical radiation oncology treatment planning processes.We developed software using an R environment hosted on a local web-server at Seattle Cancer Care Alliance (SCCA) in 2017. The planning process was divided into stages, and time-stamped moves between planning stages were recorded automatically via Mosaiq (Elekta, Sweden) Quality Check Lists (QCLs). Whiteboard logs were merged with Mosaiq-extracted diagnostic factors and evaluated for significance. Interventional changes to task time expectations were evaluated for 6 months in 2021 and compared with 6 month periods in 2018 and 2019.Whiteboard/Mosaiq data from the SCCA show that treatment intent, number of prescriptions, and nodal involvement were main factors influencing overall time to plan completion. Contouring and Planning times were improved by 2.6 days (p10This report establishes the utility of real-time task tracking tools in a radiotherapy planning process. The whiteboard results provide data-driven evidence to add justification for practice change implementations.

Artificial intelligence (AI) applications have recently been proposed to detect errors in radiotherapy plans. External validation of such systems is essential to assess their performance and safety before applying them to clinical practice. We collected data from 5238 patients treated at Maastro Clinic and introduced a range of common radiotherapy plan errors for the model to detect. We estimated the model's discrimination by calculating the area under the receiver-operating characteristic curve (AUC). We also assessed its clinical usefulness as an alert system that could reduce the need for manual checks by calculating the percentage of values flagged as errors and the positive predictive value (PPV) for a range of high sensitivities (95 %-99 %) and error prevalence. The AUC when considering all variables was 67.8% (95% CI, 65.6%-69.9%). The AUC varied widely for different types of errors (from 90.4% for table angle errors to 54.5% for planning tumor volume-PTV dose errors). The percentage of flagged values ranged from 84% to 90% for sensitivities between 95% and 99% and the PPV was only slightly higher than the prevalence of the errors. The model's performance in the external validation was significantly worse than that in its original setting (AUC of 68% versus 89%). Its usefulness as an alert system to reduce the need for manual checks is questionable due to the low PPV and high percentage of values flagged as potential errors to achieve a high sensitivity. We analyzed the apparent limitations of the model and we proposed actions to overcome them.

Purpose Surface-guided radiation therapy (SGRT) is a nonionizing imaging approach for patient setup guidance, intra-fraction monitoring, and automated breath-hold gating of radiation treatments. SGRT employs the premise that the external patient surface correlates to the internal anatomy, to infer the treatment isocenter position at time of treatment delivery. Deformations and posture variations are known to impact the correlation between external and internal anatomy. However, the degree, magnitude, and algorithm dependence of this impact are not intuitive and currently no methods exist to assess this relationship. The primary aim of this work was to develop a framework to investigate and understand how a commercial optical surface imaging system (C-RAD, Uppsala, Sweden), which uses a nonrigid registration algorithm, handles rotations and surface deformations. Methods A workflow consisting of a female torso phantom and software-introduced transformations to the corresponding digital reference surface was developed. To benchmark and validate the approach, known rigid translations and rotations were first applied. Relevant breast radiotherapy deformations related to breast size, hunching/arching back, distended/deflated abdomen, and an irregular surface to mimic a cover sheet over the lower part of the torso were investigated. The difference between rigid and deformed surfaces was evaluated as a function of isocenter location. Results For all introduced rigid body transformations, C-RAD computed isocenter shifts were determined within 1 mm and 1˚. Additional translational shifts to correct for rotations as a function of isocenter location were determined with the same accuracy. For yaw setup errors, the difference in shift corrections between a plan with an isocenter placed in the center of the breast (BrstIso) and one located 12 cm superiorly (SCFIso) was 2.3 mm/1˚ in lateral direction. Pitch setup errors resulted in a difference of 2.1 mm/1˚ in vertical direction. For some of the deformation scenarios, much larger differences up to 16 mm and 7˚ in the calculated shifts between BrstIso and SCFIso were observed that could lead to large unintended gaps or overlap between adjacent matched fields if uncorrected. Conclusions The methodology developed lends itself well for quality assurance (QA) of SGRT systems. The deformable C-RAD algorithm determined accurate shifts for rigid transformations, and this was independent of isocenter location. For surface deformations, the position of the isocenter had considerable impact on the registration result. It is recommended to avoid off-axis isocenters during treatment planning to optimally utilize the capabilities of the deformable image registration algorithm, especially when multiple isocenters are used with fields that share a field edge.

This retrospective study of left breast radiation therapy (RT) investigates the correlation between anatomical parameters and dose to heart or/and left lung in deep inspiration breath-hold (DIBH) compared to free-breathing (FB) technique. Anatomical parameters of sixty-seven patients, treated with a step-and-shoot technique to 50 Gy or 50.4 Gy were included. They consisted of the cardiac contact distances in axial (CCDax) and parasagittal (CCDps) planes, and the lateral heart-to-chest distance (HCD). Correlation analysis was performed to identify predictors for heart and lung dose sparing. Paired t-test and linear regression were used for data analysis with significance level of p = 0.05. All dose metrics for heart and lung were significantly reduced with DIBH, however 21% of patients analyzed had less than 1.0 Gy mean heart dose reduction. Both FB-CCDps distance and FB-HCD correlated with FB mean heart dose and mean DIBH heart dose reduction. The strongest correlation was observed for the ratio of FB-CCDps and FB-HCD with heart dose sparing. A FB-CCDps and FB-HCD model was developed to predict DIBH induced mean heart dose reduction, with 1.04 Gy per unit of FB-CCDps/FB-HCD. Variation between predicted and actual mean heart dose reduction ranged from −0.6 Gy to 0.6 Gy. In this study, FB-CCDps and FB-HCD distance served as predictors for heart dose reduction with DIBH equally, with FB-CCDps/FB-HCD as a stronger predictor. These parameters and the prediction model could be further investigated for use as a tool to better select patients who will benefit from DIBH.

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.

Purpose To characterize reproducibility of patient breath-hold positioning and compare tracking system performance for Deep Inspiration Breath Hold (DIBH) gated left breast radiotherapy. Methods 29 consecutive left breast DIBH patients (655 fractions) were treated under the guidance of Calypso surface beacons with audio-feedback and 35 consecutive patients (631 fractions) were treated using C-RAD Catalyst HD surface imaging with audiovisual feedback. The Calypso system tracks a centroid determined by two radio-frequency transponders, with a manually enforced institutional tolerance, while the surface image based CatalystHD system utilizes real-time biometric feedback to track a pre-selected point with an institutional tolerance enforced by the Elekta Response gating interface. DIBH motion data from Calypso was extracted to obtain the displacement of breath hold marker in ant/post direction from a set-zero reference point. Ant/post point displacement data from CatalystHD was interpreted by computing the difference between raw tracking points and the center of individual gating windows. Mean overall errors were compared using Welsh’s unequal variance t-test. Wilcoxon rank sum test were used for statistical analysis with P Results Mean overall error for Calypso and CatalystHD were 0.33 ± 1.17 mm and 0.22 ± 0.43 mm, respectively, with t-test comparison P-value 2 × 10 - 16 . Average standard deviation per fraction was found to be 0.74 ± 0.44 mm for Calypso patients versus 0.54 ± 0.22 mm for CatalystHD. Conclusion Reduced error distribution widths in overall positioning, deviation of position, and per fraction deviation suggest that the use of functionalities available in CatalystHD such as audiovisual biofeedback and patient surface matching improves accuracy and stability during DIBH gated left breast radiotherapy.

Radiation therapy is an effective treatment for primary orbital lymphomas. Lens shielding with electrons can reduce the risk of high-grade cataracts in patients undergoing treatment for superficial tumors. This work evaluates the dosimetric effects of a suspended eye shield, placement of bolus, and varying electron energies. Film (GafChromic EBT3) dosimetry and relative output factors were measured for 6, 8, and 10 MeV electron energies. A customized 5-cm diameter circle electron orbital cutout was constructed for a 6 × 6-cm applicator with a suspended lens shield (8-mm diameter Cerrobend cylinder, 2.2-cm length). Point doses were measured using a scanning electron diode in a solid water phantom at depths representative of the anterior and posterior lens. Depth dose profiles were compared for 0-mm, 3-mm, and 5-mm bolus thicknesses. At 5 mm (the approximate distance of the anterior lens from the surface of the cornea), the percent depth dose under the suspended lens shield was reduced to 15%, 15%, and 14% for electron energies 6, 8, and 10 MeV, respectively. Applying bolus reduced the benefit of lens shielding by increasing the estimated doses under the block to 27% for 3-mm and 44% for 5-mm bolus for a 6 MeV incident electron beam. This effect is minimized with 8 MeV electron beams where the corresponding values were 15.5% and 18% for 3-mm and 5-mm bolus. Introduction of a 7-mm hole in 5-mm bolus to stabilize eye motion during treatment altered lens doses by about 1%. Careful selection of electron energy and consideration of bolus effects are needed to account for electron scatter under a lens shield.

The delivery of radiation therapy shares many of the challenges encountered in imaging procedures. As in imaging, such as MRI, organ motion must be reduced to a minimum, often for lengthy time periods, to effectively target the tumor during imaging-guided therapy while reducing radiation dose to nearby normal tissues. For patients, radiation therapy is frequently a stress- and anxiety-provoking medical procedure, evoking fear from negative perceptions about irradiation, confinement from immobilization devices, claustrophobia, unease with equipment, physical discomfort, and overall cancer fear. Such stress can be a profound challenge for cancer patients' emotional coping and tolerance to treatment, and particularly interferes with advanced radiation therapy procedures where active, complex and repetitive high-level cooperation is often required from the patient.In breast cancer, the most common cancer in women worldwide, radiation therapy is an indispensable component of treatment to improve tumor control and outcome in both breast-conserving therapy for early-stage disease and in advanced-stage patients. High technological complexity and high patient cooperation is required to mitigate the known cardiac toxicity and mortality from breast cancer radiation by reducing the unintended radiation dose to the heart from left breast or left chest wall irradiation. To address this, radiation treatment in daily deep inspiration breath hold (DIBH), to create greater distance between the treatment target and the heart, is increasingly practiced. While holding the promise to decrease cardiac toxicity, DIBH procedures often augment patients' baseline stress and anxiety reaction toward radiation treatment. Patients are often overwhelmed by the physical and mental demands of daily DIBH, including the nonintuitive timed and sustained coordination of abdominal thoracic muscles for prolonged breath holding.While technologies, such as DIBH, have advanced to millimeter-precision in treatment delivery and motion tracking, the "human factor" of patients' ability to cooperate and perform has been addressed much less. Both are needed to optimally deliver advanced radiation therapy with minimized normal tissue effects, while alleviating physical and cognitive distress during this challenging phase of breast cancer therapy.This article discusses physical training and psychotherapeutic integrative health approaches, applied to radiation oncology, to leverage and augment the gains enabled by advanced technology-based high-precision radiation treatment in breast cancer. Such combinations of advanced technologies with training and cognitive integrative health interventions hold the promise to provide simple feasible and low-cost means to improve patient experience, emotional outcomes and quality of life, while optimizing patient performance for advanced imaging-guided treatment procedures - paving the way to improve cardiac outcomes in breast cancer survivors.

Aims Deep inspiration breath hold (DIBH) reduces cardiac radiation exposure by creating cardiac–chest wall separation in breast cancer radiotherapy. DIBH requires sustaining chest wall expansion for up to 40 s and involves complex co-ordination of thoraco-abdominal muscles, which may not be intuitive to patients. We investigated the effect of in-advance preparatory DIBH coaching and home practice on cardiac doses. Materials and methods Successive patients from 1 February 2015 to 31 December 2016 with left-sided breast cancer who underwent tangential field radiotherapy utilising the DIBH technique were included. The study cohort consisted of patients treated by a physician who routinely provided DIBH coaching and home practice instructions at least 5 days before simulation. The control group included non-coached patients under another physician's care. Minimum, maximum and mean cardiac doses and V5, V10 and V30 from DIBH and free breathing simulation computed tomography scans were obtained from the planning system. DIBH and free breathing cardiac doses and volume exposures were compared between the coached and non-coached groups using the two-sample t -test, Fisher's exact test and the Mann–Whitney U -test. Results Twenty-seven coached and 42 non-coached patients were identified. The DIBH maximum cardiac dose was lower in coached patients at 13.1 Gy compared with 19.4 Gy without coaching ( P = 0.004). The percentage cardiac volume exposure in DIBH was lower in coached patients; the DIBH V10 was 0.5% without coaching and 0.1% with coaching ( P = 0.005). There was also a trend towards lower DIBH V5 in the coached group compared with the non-coached group (1.2% versus 1.9%, P = 0.071). No significant differences in patient cardiopulmonary comorbidity factors that might influence cardiac doses were found between the groups. Conclusions Our results suggest that cardiac dose sparing can potentially be further improved with a 5 day regimen of preparatory DIBH coaching and in-advance home practice before simulation. These hypothesis-generating findings should be confirmed in a larger study.

Ontologies are a formal, computer-compatible method for representing scientific knowledge about a given domain. They provide a standardized vocabulary, taxonomy and set of relations between concepts. When formatted in a standard way, they can be read and reasoned upon by computers as well as by humans. At the 2019 International Conference on the Use of Computers in Radiation Therapy, there was a session devoted to ontologies in radiation therapy. This paper is a compilation of the material presented, and is meant as an introduction to the subject. This is done by means of a didactic introduction to the topic followed by a series of applications in radiation therapy. The goal of this article is to provide the medical physicist and related professionals with sufficient background that they can understand their construction as well as their practical uses.

The recent explosion in machine learning efforts in the quality assurance (QA) space has produced a variety of proofs-of-concept many with promising results. Expected outcomes of model implementation include improvements in planning time, plan quality, advanced dosimetric QA, predictive machine maintenance, increased safety checks, and developments key for new QA paradigms driven by adaptive planning. In this article, we outline several areas of research and discuss some of the unique challenges each area presents.

Purpose To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders. Methods and Materials A total of 4431 external beam radiation therapy orders from 2008 to 2017 at the authors’ institution were obtained from clinical treatment management systems and divided into 3 groups: single prescription, concurrent boost, and sequential boost. Multiple BNs were developed for each group to detect errors in new orders using joint posterior probabilities of the order parameters, given disease information. Each BN was trained with a group of orders using a Bayesian learning algorithm. A procedure was developed to select the optimal BN for each treatment site in each group and to determine site-specific parameters and error detection thresholds. Potential clinical errors, created both manually and automatically, were applied to test error detection performance. Results The average true-positive rate (TPR) and false-positive rate (FPR) of error detection were 95.72% and 1.99%, respectively, for the single-prescription cohort with 9 treatment sites. For the concurrent-boost cohort, the TPR and FPR were 92.94% and 14.53%, respectively. For the sequential-boost cohort, the TPR and FPR were 100% and 9.48%, respectively, for the prescribed dose values and 100% and 4.34%, respectively, for the remaining order parameters. For the patient simulation and imaging parameters for 9 treatment sites, the TPR and FPR were 100% and 4.96%, respectively. Conclusions The probabilistic BN method was able to perform physician order error detection at a higher accuracy than previously reported in a variety of complex prescription instances, thus warranting further development in incorporating BNs into clinical error detection tools to assist manual physician order checks.

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: 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.

The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network's conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

We evaluate a photon convolution-superposition algorithm used to model a fast neutron therapy beam in a commercial treatment planning system (TPS). The neutron beam modeled was the Clinical Neutron Therapy System (CNTS) fast neutron beam produced by 50 MeV protons on a Be target at our facility, and we implemented the Pinnacle3 dose calculation model for computing neutron doses. Measured neutron data were acquired by an IC30 ion chamber flowing 5 cc/min of tissue equivalent gas. Output factors and profile scans for open and wedged fields were measured according to the Pinnacle physics reference guide recommendations for photon beams in a Wellhofer water tank scanning system. Following the construction of a neutron beam model, computed doses were then generated using 100 monitor units (MUs) beams incident on a water-equivalent phantom for open and wedged square fields, as well as multileaf collimator (MLC)-shaped irregular fields. We compared Pinnacle dose profiles, central axis doses, and off-axis doses (in irregular fields) with 1) doses computed using the Prism treatment planning system, and 2) doses measured in a water phantom and having matching geometry to the computation setup. We found that the Pinnacle photon model may be used to model most of the important dosimetric features of the CNTS fast neutron beam. Pinnacle-calculated dose points among open and wedged square fields exhibit dose differences within 3.9 cGy of both Prism and measured doses along the central axis, and within 5 cGy difference of measurement in the penumbra region. Pinnacle dose point calculations using irregular treatment type fields showed a dose difference up to 9 cGy from measured dose points, although most points of comparison were below 5 cGy. Comparisons of dose points that were chosen from cases planned in both Pinnacle and Prism show an average dose difference less than 0.6%, except in certain fields which incorporate both wedges and heavy blocking of the central axis. All clinical cases planned in both Prism and Pinnacle were found to be comparable in terms of dose-volume histograms and spatial dose distribution following review by the treating clinicians. Variations were considered minor and within clinically acceptable limits by the treating clinicians. The Pinnacle TPS has sufficient computational modeling ability to adequately produce a viable neutron model for clinical use in treatment planning.

Purpose Bayesian networks (BNs) are graphical representations of probabilistic knowledge that offer normative reasoning under uncertainty and are well suited for use in medical domains. Traditional knowledge-based network development of BN topology requires that modeling experts establish relevant dependency links between domain concepts by searching and translating published literature, querying domain experts, or applying machine learning algorithms on data. For initial development these methods are time-intensive and this cost hinders the growth of BN applications in medical decision making. Further, this approach fails to utilize knowledge representation in medical fields to automate network development. Our research alleviates the challenges surrounding BN modeling in radiation oncology by leveraging an ontology based hub and spoke system for BN construction. Methods We implement a hub and spoke system by developing 1) an ontology of knowledge in radiation oncology (the hub) which includes dependency semantics similar to BN relations and 2) a software tool that operates on ontological semantics using deductive reasoning to create BN topologies (the spokes). We demonstrate that network topologies built using the software are terminologically consistent and form networks that are topologically compatible with existing ones. We do this first by merging two different BN models for prostate cancer radiotherapy prediction which contain domain cross terms. We then use the logic to perform discovery of new causal chains between radiation oncology concepts. Results From the radiation oncology (RO) ontology we successfully reconstructed a previously published prostate cancer radiotherapy Bayes net using up-to-date domain knowledge. Merging this model with another similar prostate cancer model in the RO domain produced a larger, highly interconnected model representing the expanded scope of knowledge available regarding prostate cancer therapy parameters, complications, and outcomes. The causal discovery resulted in an automatically-built causal network model of all ontologized radiotherapy concepts between a ‘Mucositis’ complication and anatomic tumor location. Conclusions The proposed model building approach lowers barriers to developing probabilistic models relevant to real-world clinical decision making, and offers a solution to the consistency and compatibility problems. Further, the knowledge representation in this work demonstrates potential for broader radiation oncology applications outside of Bayes nets. This article is protected by copyright. All rights reserved.

The purpose of this study was to evaluate the dosimetric and practical effects of the Monaco treatment planning system “max arcs-per-beam” optimization parameter in pelvic radiotherapy treatments. We selected for this study a total of 17 previously treated patients with a range of pelvic disease sites including prostate (9), bladder (1), uterus (3), rectum (3), and cervix (1). For each patient, 2 plans were generated, one using an arc-per-beam setting of “1” and another with an arc-per-beam setting of “2” using the volumes and constraints established from the initial clinical treatments. All constraints and dose coverage objects were kept the same between plans, and all plans were normalized to 99.7% to ensure 100% of the planning target volume (PTV) received 95% of the prescription dose. Plans were evaluated for PTV conformity, homogeneity, number of monitor units, number of control points, and overall plan acceptability. Treatment delivery time, patient-specific quality assurance procedures, and the impact on clinical workflow were also assessed. We found that for complex-shaped target volumes (small central volumes with extending arms to cover nodal regions), the use of 2 arc-per-beam (2APB) parameter setting achieved significantly lower average dose-volume histogram values for the rectum V20 (p = 0.0012) and bladder V30 (p = 0.0036) while meeting the high dose target constraints. For simple PTV shapes, we found reduced monitor units (13.47%, p = 0.0009) and control points (8.77%, p = 0.0004) using 2APB planning. In addition, we found a beam delivery time reduction of approximately 25%. In summary, the dosimetric benefit, although moderate, was improved over a 1APB setting for complex PTV, and equivalent in other cases. The overall reduced delivery time suggests that the use of mulitple arcs per beam could lead to reduced patient-on-table time, increased clinical throughput, and reduced medical physics quality assurance effort.

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

We report progress on a program of gravitational physics experiments using cryogenic torsion pendula undergoing large-amplitude torsion oscillation. This program includes tests of the gravitational inverse square law and of the weak equivalence principle. Here we describe our ongoing search for inverse-square-law violation at a strength down to $10^{-5}$ of standard gravity. The low-vibration environment provided by the Battelle Gravitation Physics Laboratory (BGPL) is uniquely suited to this study., Comment: To be published in The Proceedings of the Francesco Melchiorri Memorial Conference as a special issue of New Astronomy Reviews

Purpose: A standard tool for ensuring the quality of radiation therapy treatments is the initial physics plan review. However, little is known about its performance in practice. The goal of this study is to measure the effectiveness of physics plan review by introducing simulated errors into “mock” treatment plans and measuring the performance of plan review by physicists. Methods: We generated six mock treatment plans containing multiple errors. These errors were based on incident learning system data both within the department and internationally (SAFRON). These errors were scored for severity and frequency. Those with the highest scores were included in the simulations (13 errors total). Observer bias was minimized using a multiple co-correlated distractor approach. Eight physicists reviewed these plans for errors, with each physicist reviewing, on average, 3/6 plans. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. Results: Simulated errors were detected in 65% of reviews [51–75%] (95% confidence interval [CI] in brackets). The following error scenarios had the highest detection rates: incorrect isocenter in DRRs/CBCT (91% [73–98%]) and a planned dose different from the prescribed dose (100% [61–100%]). Errors with low detection rates involved incorrect field parameters in record and verify system (38%, [18–61%]) and incorrect isocenter localization in planning system (29% [8–64%]). Though pre-treatment QA failure was reliably identified (100%), less than 20% of participants reported the error that caused the failure. Conclusion: This is one of the first quantitative studies of error detection. Although physics plan review is a key safety measure and can identify some errors with high fidelity, others errors are more challenging to detect. This data will guide future work on standardization and automation. Creating new checks or improving existing ones (i.e., via automation) will help in detecting those errors with low detection rates.

Purpose: This retrospective study of left sided whole breast radiation therapy (RT) patients investigates possible predictive parameters correlating to cardiac and left lung dose sparing by deep inspiration breath-hold (DIBH) technique compared to free-breathing (FB). Methods: Thirty-one patients having both DIBH and FB CT scans were included in the study. All patients were planned with a standard step-and-shoot tangential technique using MV photons, with prescription of 50Gy or 50.4Gy. The displacement of the breath hold sternal mark during DIBH, the cardiac contact distances of the axial (CCDax) and parasagittal (CCDps) planes, and lateral-heart-to-chest (LHC) distance on FB CT scans were measured. Lung volumes, mean dose and dose-volume histograms (V5, V10 and V20) were analyzed and compared for heart and left lung for both FB and DIBH techniques. Correlation analysis was performed to identify the predictors for heart and left lung dose sparing. Two-tailed Student's t-test and linear regression were used for data analysis with significance level of P≤0.05. Results: All dosimetric metrics for the heart and left lung were significantly reduced (P

Purpose: The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information. Methods In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error-free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network. Results: We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ∼0.08 seconds. Conclusion: The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and automated plan checks. The senior author received research grants from ViewRay Inc. and Varian Medical System.

Purpose: The purpose of this study was to evaluate the dosimetric and practical effects of the Monaco treatment planning system “max arcs-per-beam” optimization parameter in pelvic radiotherapy treatments. Methods: A total of 17 previously treated patients were selected for this study with a range of pelvic disease site including prostate(9), bladder(1), uterus(3), rectum(3), and cervix(1). For each patient, two plans were generated, one using a arc-per-beam setting of ‘1’ and another with setting of ‘2’. The setting allows the optimizer to add a gantry direction change, creating multiple arc passes per beam sequence. Volumes and constraints established from the initial clinical treatments were used for planning. All constraints and dose coverage objects were kept the same between plans, and all plans were normalized to 99.7% to ensure 100% of the PTV received 95% of the prescription dose. We evaluated the PTV conformity index, homogeneity index, total monitor units, number of control points, and various dose volume histogram (DVH) points for statistical comparison (alpha=0.05). Results: We found for the 10 complex shaped target volumes (small central volumes with extending bilateral ‘arms’ to cover nodal regions) that the use of 2 arcs-per-beam achieved significantly lower average DVH values for the bladder V20 (p=0.036) and rectum V30 (p=0.001) while still meeting the high dose target constraints. DVH values for the simpler, more spherical PTVs were not found significantly different. Additionally, we found a beam delivery time reduction of approximately 25%. Conclusion: In summary, the dosimetric benefit, while moderate, was improved over a 1 arc-per-beam setting for complex PTVs, and equivalent in other cases. The overall reduced delivery time suggests that the use of multiple arcs-per-beam could lead to reduced patient on table time, increased clinical throughput, and reduced medical physics quality assurance effort.

We evaluate a photon convolution‐superposition algorithm used to model a fast neutron therapy beam in a commercial treatment planning system (TPS). The neutron beam modeled was the Clinical Neutron Therapy System (CNTS) fast neutron beam produced by 50 MeV protons on a Be target at our facility, and we implemented the Pinnacle3 dose calculation model for computing neutron doses. Measured neutron data were acquired by an IC30 ion chamber flowing 5 cc/min of tissue equivalent gas. Output factors and profile scans for open and wedged fields were measured according to the Pinnacle physics reference guide recommendations for photon beams in a Wellhofer water tank scanning system. Following the construction of a neutron beam model, computed doses were then generated using 100 monitor units (MUs) beams incident on a water‐equivalent phantom for open and wedged square fields, as well as multileaf collimator (MLC)‐shaped irregular fields. We compared Pinnacle dose profiles, central axis doses, and off‐axis doses (in irregular fields) with 1) doses computed using the Prism treatment planning system, and 2) doses measured in a water phantom and having matching geometry to the computation setup. We found that the Pinnacle photon model may be used to model most of the important dosimetric features of the CNTS fast neutron beam. Pinnacle‐calculated dose points among open and wedged square fields exhibit dose differences within 3.9 cGy of both Prism and measured doses along the central axis, and within 5 cGy difference of measurement in the penumbra region. Pinnacle dose point calculations using irregular treatment type fields showed a dose difference up to 9 cGy from measured dose points, although most points of comparison were below 5 cGy. Comparisons of dose points that were chosen from cases planned in both Pinnacle and Prism show an average dose difference less than 0.6%, except in certain fields which incorporate both wedges and heavy blocking of the central axis. All clinical cases planned in both Prism and Pinnacle were found to be comparable in terms of dose‐volume histograms and spatial dose distribution following review by the treating clinicians. Variations were considered minor and within clinically acceptable limits by the treating clinicians. The Pinnacle TPS has sufficient computational modeling ability to adequately produce a viable neutron model for clinical use in treatment planning. PACS numbers: 87.53 Bn, 28.20.Pr, 87.53.Bn

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the use of probabilistic models into clinical chart checking for improved detection of potential errors in RT plans.

Purpose: In vivo dosimetry (IVD) assessment of treatment dose is important when delivering total body irradiation (TBI). One method is to average AP and PA surface diode measurements and compare them to prescribed midline doses. We designed phantom studies to examine the impact of patient thickness on surface IVD measurements under TBI conditions. Methods: Phantom studies were designed to assess the effects of patient thickness on diode IVD. Sun Nuclear QED diodes with inherent buildup were placed on anterior and posterior surfaces of a solid water phantom. Phantom thickness was varied between 20 and 40 cm. A PTW farmer chamber was inserted in the center of the phantom at 425 SSD to reflect prescribed midline dose, and 50 cGy was delivered to midline with 18 MV photons. Averaged entrance and exit diode doses were then compared to farmer chamber measurements of phantom midline dose. Results: A trend of increased deviation with increasing umbilicus thickness was observed between averaged surface diodes and midline farmer chamber measurements. Averaged surface diode dose ranged from 49.6 cGy (20 cm thickness) to 52.1 cGy (40 cm thickness). Interpolation of diode measurements to midline resulted in linear overestimation of delivered dose relative to farmer chamber measurements at midline, up to 6.8% at 40 cm umbilicus thickness. Conclusion: Accurate in vivo dosimetry at time of patient TBI is important to allow individual correction of MU exposure and tissue compensation. Without patient thickness correction, overresponse of surface diodes may lead to unnecessary clinical intervention to treatment MU or compensation and insufficient midline dose. Additionally, SAD setup is preferable to SSD setup to minimize thickness non‐linearity. In conclusion, thickness correction factors should be used to generate expected diode readings for patients with thickness greater than 30 cm.

Purpose: Examine the feasibility and biological significance of increasing machine output rates in extended‐distance Total Body Irradiation (TBI). Methods: Biologically equivalent doses for TBI are derived from the LQ cell survival model. Effective dose rates and corrected fraction size for a mini‐TBI (i.e., 1 fraction × 2 Gy to patient mid‐line) delivered using 4 and 8 beams are computed as a function of machine output rate. The number of beams used for a TBI tends to increase with increasing patient size because of the clinical constraint of no more than 1000 MU per beam. All of the reported results include a 1 minute gap between beams and a (conservative) 5 minute gap for patient repositioning. Results: For a 4 beam treatment, increasing the machine output rate from 200 to 400 MU/minute reduced the fraction delivery time by 36% (28 to 18 minutes), and only increased the effective dose rate from 7 to 11 cGy/minute. For 8 beams, the fraction delivery times for 200 and 400 MU/min are 52 minutes (3.85 cGy/min) to 20 minutes (6.16 cGy/min), respectively. Increasing the machine output rate from 200 to 400 MU/min is unlikely to require more than 2–5% reduction in fraction size (alpha/beta = 1 Gy). The size of the required correction decreases as alpha/beta increases. Conclusions: Patient‐specific planning to correct for differences in beam number (patient size) are unlikely to alter the determination of biologically equivalent fraction sizes by more than 5%, which is well within the +/− 10% uniformity specified in many TBI protocols. A clinical trial to evaluate patient safety and treatment efficacy for machine output rates as high as 600–700 MU per minute are reasonable. Such a trial has the potential to substantially to increase patient comfort and reduce facility and labor costs without jeopardizing treatment effectiveness or safety.

Purpose: To determine if a photon convolution/superposition algorithm could be used to model a fast neutron therapy beam in a commercial treatment planning system. Methods: The beam to be modeled was the Clinical Neutron Therapy System (CNTS) fast neutron beam produced by 50 MeV protons on a Be target at the University of Washington(UW) Medical Center. The dose calculation model was that implemented in Pinnacle, 3 (Philips Medical Systems). Measured neutron dose data were acquired with an IC30 ion chamber with tissue equivalent gas. The neutron beam was modeled using the auto‐modeling tools available in the Pinnacle system for photon beams. Doses were then computed using a 100 MU beam incident on a water equivalent phantom for open and wedged square fields as well as MLC shaped irregular fields. Pinnacle generated profiles and central axis dose points were compared to two sets of doses: 1) doses computed with the UW PRISMtreatment planning system in the same geometry as the Pinnacle setup and 2) doses measured in a water tank. Results: The Pinnacle photonmodel incorporates most of the important dosimetric features of the neutron beam. Computed doses compared well to both the Prism TPS and measured data. We found that calculated dose points among open and wedged square fields were within 2% of both Prism and measured doses along the central axis, and within 5% of measurement in the penumbra region. Dose point calculations using irregular treatment type fields were within 3% of measured dose points. Conclusion: The Pinnacle TPS has sufficient computational modeling ability to adequately produce a viable neutronmodel for potential use in clinical treatment planning. Further testing of irregular fields must be performed and results analyzed prior to implementation in the clinic.

Purpose: To evaluate the effectiveness of a probabilistic mathematical model at tracking and predicting respiratory motion gated and motion compensated in radiation therapy.Method and Materials: A previously recorded set of fiducial marker positions were recorded at 30Hz for four patients and modeled into finite states representing the natural breathing states of Exhale, Inhale, and End of Exhale. State sequences and observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict a future set of sequences and new observables. Velocities and other parameters were clustered using a k‐means clustering algorithm to associate each state with a set of observables such that a prediction of state would enable a prediction of velocity. Positional results were then compared to several other published predictive models in terms of their overall RMS errors from actual position. System delay times from 33 to 1000 ms were considered and for additional comparison, a simple time average model which bases future sequence prediction on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the hidden Markov model prediction algorithm to set a theoretical limit of the predictive power of the model at ∼20% of the motion amplitude. Results: RMS errors for motion traces modeled by the hidden Markov model show improvement over linear and phase shift type prediction models at latencies higher than 500 ms, but do not reach the target lower limit. RMS errors for the time average model approach the theoretical limit of the HMM, even though their state sequences are poorly correlated with sequences known to fit the data. Conclusion: Hidden Markov models and their simpler probabilistic counterparts show much promise for use in online prediction of tumor motion for improved accuracy of radiation delivery.

Monthly QA is recommended to verify the constancy of high‐energy electron beams generated for clinical use by linear accelerators. The tolerances are defined as 2%/2 mm in beam penetration according to AAPM task group report 142. The practical implementation is typically achieved by measuring the ratio of readings at two different depths, preferably near the depth of maximum dose and at the depth corresponding to half the dose maximum. Based on beam commissioning data, we show that the relationship between the ranges of energy ratios for different electron energies is highly nonlinear. We provide a formalism that translates measurement deviations in the reference ratios into change in beam penetration for electron energies for six Elekta (6‐18 MeV) and eight Varian (6‐22 MeV) electron beams. Experimental checks were conducted for each Elekta energy to compare calculated values with measurements, and it was shown that they are in agreement. For example, for a 6 MeV beam a deviation in the measured ionization ratio of ±15% might still be acceptable (i.e., be within ±2 mm), whereas for an 18 MeV beam the corresponding tolerance might be ±6%. These values strongly depend on the initial ratio chosen. In summary, the relationship between differences of the ionization ratio and the corresponding beam energy are derived. The findings can be translated into acceptable tolerance values for monthly QA of electron beam energies. PACS number(s): 87.55, 87.56