Effect of total body irradiation lung block parameters on lung doses using three-dimensional dosimetry.

PURPOSE: Total body irradiation (TBI) is an integral part of stem cell transplant. However, patients are at risk of treatment-related toxicities, including radiation pneumonitis. While lung dose is one of the most crucial aspects of TBI dosimetry, currently available data are based on point doses. As volumetric dose distribution could be substantially altered by lung block parameters, we used 3D dosimetry in our treatment planning system to estimate volumetric lung dose and measure the impact of various lung block designs. MATERIALS AND METHODS: We commissioned a TBI beam model in RayStation that matches the measured tissue-phantom ratio under our clinical TBI setup. Cerrobend blocks were automatically generated in RayStation on thoracic Computed Tomography (CT) scans from three anonymized patients using the lung, clavicle, spine, and diaphragmatic contours. The margin for block edge was varied to 0, 1, or 2 cm from the superior, lateral, and inferior thoracic borders, with a uniform margin 2.5 cm lateral to the vertebral bodies. The lung dose was calculated and compared with a prescription dose of 1200 cGy in six fractions (three with blocks and three without). RESULT: The point dose at midplane under the block and the average lung dose are at the range of 73%-76% and 80%-88% of prescription dose respectively regardless of the block margins. In contrast, the percent lung volume receiving 10 Gy increased by nearly two-fold, from 31% to 60% over the margins from 0 to 2 cm. CONCLUSIONS: The TPS-derived 3D lung dose is substantially different from the nominal dose assumed with HVL lung blocks. Point doses under the block are insufficient to accurately gauge the relationship between dose and pneumonitis, and TBI dosimetry could be highly variable between patients and institutions as more descriptive parameters are not included in protocols. Much progress remains to be made to optimize and standardize technical aspects of TBI, and better dosimetry could provide more precise dosimetric predictors for pneumonitis risk.

Directional optical switching and transistor functionality using optical parametric oscillation in a spinor polariton fluid.

Over the past decade, spontaneously emerging patterns in the density of polaritons in semiconductor microcavities were found to be a promising candidate for all-optical switching. But recent approaches were mostly restricted to scalar fields, did not benefit from the polariton's unique spin-dependent properties, and utilized switching based on hexagon far-field patterns with 60° beam switching (i.e. in the far field the beam propagation direction is switched by 60°). Since hexagon far-field patterns are challenging, we present here an approach for a linearly polarized spinor field, that allows for a transistor-like (e.g., crucial for cascadability) orthogonal beam switching, i.e. in the far field the beam is switched by 90°. We show that switching specifications such as amplification and speed can be adjusted using only optical means.

AAPM WGPE report 394: Simulated error training for the physics plan and chart review.

BACKGROUND: Simulated error training is a method to practice error detection in situations where the occurrence of error is low. Such is the case for the physics plan and chart review where a physicist may check several plans before encountering a significant problem. By simulating potentially hazardous errors, physicists can become familiar with how they manifest and learn from mistakes made during a simulated plan review. PURPOSE: The purpose of this project was to develop a series of training datasets that allows medical physicists and trainees to practice plan and chart reviews in a way that is familiar and accessible, and to provide exposure to the various failure modes (FMs) encountered in clinical scenarios. METHODS: A series of training datasets have been developed that include a variety of embedded errors based on the risk-assessment performed by American Association of Physicists in Medicine (AAPM) Task Group 275 for the physics plan and chart review. The training datasets comprise documentation, screen shots, and digital content derived from common treatment planning and radiation oncology information systems and are available via the Cloud-based platform ProKnow. RESULTS: Overall, 20 datasets have been created incorporating various software systems (Mosaiq, ARIA, Eclipse, RayStation, Pinnacle) and delivery techniques. A total of 110 errors representing 50 different FMs were embedded with the 20 datasets. The project was piloted at the 2021 AAPM Annual Meeting in a workshop where participants had the opportunity to review cases and answer survey questions related to errors they detected and their perception of the project's efficacy. In general, attendees detected higher-priority FMs at a higher rate, though no correlation was found between detection rate and the detectability of the FMs. Familiarity with a given system appeared to play a role in detecting errors, specifically when related to missing information at different locations within a given software system. Overall, 96% of respondents either agreed or strongly agreed that the ProKnow portal and training datasets were effective as a training tool, and 75% of respondents agreed or strongly agreed that they planned to use the tool at their local institution. CONCLUSIONS: The datasets and digital platform provide a standardized and accessible tool for training, performance assessment, and continuing education regarding the physics plan and chart review. Work is ongoing to expand the project to include more modalities, radiation oncology treatment planning and information systems, and FMs based on emerging techniques such as auto-contouring and auto-planning.

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

Purpose: 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. Methods: 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). Results: 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. Conclusion: 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.

Impact of lung block shape on cardiac dose for total body irradiation.

Evaluating cardiac dose during total body irradiation (TBI) is of increasing interest. A three-dimensional beam model for TBI was commissioned and lung shielding was simulated in a treatment planning system with the cardiac silhouette partially blocked and unblocked. When blocked, the median heart dose decreased by 6% (IQR = 6%) and the median cardiac V12Gy decreased by 27% (IQR = 17%). The median left anterior descending artery dose decreased 20% (IQR = 12%) for blocked cases. Because using partial heart shielding may result in considerable changes in dose to cardiac structures, TBI protocols should explicitly consider lung block design parameters and their potential effects.

Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning.

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.

Ontologies in radiation oncology.

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.

Characterization of a Bayesian network-based radiotherapy plan verification model.

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.