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PURPOSE: Medical physics staffing models require periodic review due to the rapid evolution of technology and clinical techniques in radiation oncology. We present an update to a grid-based physics staffing algorithm for radiation oncology (originally published in 2012) that has been widely used in Canada over the last decade. MATERIALS AND METHODS: The physics staffing algorithm structure was modified to improve the clarity and consistency of input data. We collected information on clinical procedures, equipment inventory, and teaching activities from 15 radiation treatment centers in the province of Ontario from April 1, 2018, to March 31, 2019. Using these data sets, the algorithm's weighting parameters were adjusted to align the prediction of full-time equivalent (FTE) personnel with actual staffing levels in Ontario. The algorithm computes FTE estimates for medical physicists, physics assistants, engineering (electrical and mechanical), and information technology (IT) support. The performance of the algorithm was also tested in eight Canadian cancer centers outside of Ontario. RESULTS: The mean difference between the algorithm and actual staffing for the 23 Canadian cancer centers did not exceed 0.5 FTE for any staffing group. The results were slightly better in Ontario than in other provinces, as expected since the algorithm was optimized using Ontario data. There was a linear correlation between the algorithm predictions and the number of annual-treated cases for physicists, and physicists plus physics assistants. For other staff categories, the algorithm weighting parameters were not significantly altered, except for a reduction in mechanical engineering staff. Comparison with other published models suggests that the updated algorithm should be considered as a minimum recommended staffing level for the clinical support of radiation oncology programs. CONCLUSIONS: We support the use of grid-based physics staffing algorithms that account for clinical workload with flexibility to adapt to local conditions with variable academic and research demands.
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Radioterapia (Especialidade) , Algoritmos , Canadá , Física Médica , Humanos , Física , Recursos HumanosRESUMO
The Canadian Organization of Medical Physicists (COMP), in close partnership with the Canadian Partnership for Quality Radiotherapy (CPQR) has developed a series of Technical Quality Control (TQC) guidelines for radiation treatment equipment. These guidelines outline the performance objectives that equipment should meet in order to ensure an acceptable level of radiation treatment quality. The TQC guidelines have been rigorously reviewed and field tested in a variety of Canadian radiation treatment facilities. The development process enables rapid review and update to keep the guidelines current with changes in technology. This announcement provides an introduction to the guidelines, describing their scope and how they should be interpreted. Details of recommended tests can be found in separate, equipment specific TQC guidelines published in the JACMP (COMP Reports), or the website of the Canadian Partnership for Quality Radiotherapy (www.cpqr.ca).
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Física Médica , Erros Médicos/prevenção & controle , Guias de Prática Clínica como Assunto/normas , Controle de Qualidade , Radioterapia (Especialidade)/organização & administração , Radioterapia (Especialidade)/normas , Relatório de Pesquisa , Canadá , HumanosRESUMO
A close partnership between the Canadian Partnership for Quality Radiotherapy (CPQR) and the Canadian Organization of Medical Physicist's (COMP) Quality Assurance and Radiation Safety Advisory Committee (QARSAC) has resulted in the development of a suite of Technical Quality Control (TQC) guidelines for radiation treatment equipment; they outline specific performance objectives and criteria that equipment should meet in order to assure an acceptable level of radiation treatment quality. The adopted framework for the development and maintenance of the TQCs ensures the guidelines incorporate input from the medical physics com-munity during development, measures the workload required to perform the QC tests outlined in each TQC, and remain relevant (i.e., "living documents") through subsequent planned reviews and updates. The framework includes consolidation of existing guidelines and/or literature by expert reviewers, structured stages of public review, external field-testing, and ratification by COMP. This TQC develop-ment framework is a cross-country initiative that allows for rapid development of robust, community-driven living guideline documents that are owned by the com-munity and reviewed to keep relevant in a rapidly evolving technical environment. Community engagement and uptake survey data shows 70% of Canadian centers are part of this process and that the data in the guideline documents reflect, and are influencing, the way Canadian radiation treatment centers run their technical quality control programs. For a medium-sized center comprising six linear accelerators and a comprehensive brachytherapy program, we evaluate the physics workload to 1.5 full-time equivalent physicists per year to complete all QC tests listed in this suite.
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Atenção à Saúde/normas , Aceleradores de Partículas/instrumentação , Guias de Prática Clínica como Assunto/normas , Controle de Qualidade , Radioterapia/instrumentação , Radioterapia/normas , HumanosRESUMO
PURPOSE: Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer. METHODS AND MATERIALS: Three contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation. RESULTS: Mean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows. CONCLUSION: Auto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose-volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.
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Aprendizado Profundo , Humanos , Masculino , Órgãos em Risco , Próstata/diagnóstico por imagem , Planejamento da Radioterapia Assistida por Computador , Reto/diagnóstico por imagem , Estudos Retrospectivos , Bexiga UrináriaRESUMO
PURPOSE: Ontario Health (Cancer Care Ontario), formerly known as CCO, is the provincial governmental agency in Ontario, Canada responsible for developing radiation therapy-specific capital investment strategies, updated every 5 years, to ensure equitable access and to gain the highest value from these investments in infrastructure. These plans are informed by the changing landscape of health care delivery, technologic advancements affecting radiation therapy care, patient desire for care closer to home, and expected increases in utilization of radiation therapy services. In this article, we describe the development, model, and final recommendations of CCO's fifth radiation therapy capital investment strategy. METHODS AND MATERIALS: A panel of multidisciplinary provincial experts, in combination with 2 patient and family advisors, developed planning principles to guide the development of a patient-centered strategy. Adaption of the previously used model for radiation therapy planning was used. RESULTS: The development of the capital investment strategy took place from fall 2017 to fall 2018. The model included 3 main factors: patient demand (including utilization targets), machine throughput, and machine demand and supply. The final recommendation is for an investment of 26 new radiation therapy machines in the province by 2028. CONCLUSIONS: The strategy plans for continued province-wide access to quality radiation therapy care and ensures machines are added to the system at the right place and in the right time. Ongoing data collection throughout this period is necessary to ensure the strategy achieves its goals and to allow for planning of future strategies.
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The purpose of this work was to investigate the influence of a new transmission detector on 6 MV x-ray beam properties. The device, COMPASS (IBA Dosimetry, Germany), contains 1600 plane parallel ionization chambers with a detector spacing of 6.5 mm and an active volume of 0.02 cm3. Surface dose measurements were carried out using a Markus chamber and radiochromic film for a range of field sizes and source-to-surface distances (SSDs). The surface dose and dose in the build-up region for COMPASS fields were compared to open fields. For moderately narrow beam geometric conditions, the increase in surface dose was small. For the largest field size investigated (20x20 cm2) at a 90 cm SSD, the surface dose with the detector was 34.9% versus 26.8% in the open field. However, the increase in surface dose in COMPASS fields was less than that observed with a standard block tray in the field (38.7% in the above example). It was found that beyond dmax, the difference in relative dose (profiles and PDDs) between open and COMPASS fields was insignificant. The mean transmission factor of the detector was 0.967 (standard deviation=0.002) measured over a range of field sizes from 3x3 to 20x20 cm2 at SSDs from 70 cm to 90 cm. In summary, the transmission detector was found to increase the relative dose in the buildup region but had a negligible effect on the beam parameters beyond dmax.
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Algoritmos , Radiometria/instrumentação , Radioterapia Conformacional/instrumentação , Radioterapia de Alta Energia/instrumentação , Transdutores , Desenho Assistido por Computador , Desenho de Equipamento , Análise de Falha de Equipamento , Dosagem Radioterapêutica , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Raios XRESUMO
PURPOSE: To investigate the dosimetric outcome of brachytherapy in patients with small prostate volume (PV). METHODS AND MATERIALS: Forty-three patients with small PV (<25 cm(3)) as determined using transrectal ultrasound and 120 patients with non-small PV (>25 cm(3)) that had received (125)I seed implants were reviewed in a retrospective cohort study. Implantations were performed under transrectal ultrasound guidance, and the prescription dose was 145 Gy. A CT and MRI scan of the pelvis were performed 1 month after implantation for dosimetric study. RESULTS: Compared with non-small PV patients, patients with small PV experienced larger 1-month edema (p<0.001); lower dose to 90% (the isodose enclosing 90% of PV and representing a minimum dose to that volume of the prostate [D(90)]) of the prostate (p=0.03); higher intracapsular seed density (p<0.001); and were less likely to achieve D(90)>or=140 Gy (p=0.013) in a postimplant dosimetric study. The number of patients with D(90)<140 Gy decreased steadily in both subsets of patients as the implant program matured (odds ratio=0.56 per year, p<0.001), but the small prostate group exhibited more improvement compared with the non-small prostate patients over the same time period. Multivariate analysis revealed that brachytherapy team experience rather than the size of prostate was a more important predictive factor of implant quality (p<0.001). CONCLUSIONS: This single institution experience demonstrated a significant learning curve in the initial years of a prostate brachytherapy program, especially for patients with small prostates. A small prostate itself is not a contraindication of brachytherapy. The quality of implant for patients with small prostates depends more on the skill of the brachytherapy team.