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Purpose: New technologies are continuously emerging in radiation oncology. Inherent technological limitations can result in health care disparities in vulnerable patient populations. These limitations must be considered for existing and new technologies in the clinic to provide equitable care., Materials and Methods: We created a health disparity risk assessment metric inspired by failure mode and effects analysis. We provide sample patient populations and their potential associated disparities, guidelines for clinics and vendors, and example applications of the methodology., Results: A disparity risk priority number can be calculated from the product of 3 quantifiable metrics: the percentage of patients impacted, the severity of the impact of dosimetric uncertainty or quality of the radiation plan, and the clinical dependence on the evaluated technology. The disparity risk priority number can be used to rank the risk of suboptimal care due to technical limitations when comparing technologies and to plan interventions when technology is shown to have inequitable performance in the patient population of a clinic., Conclusions: The proposed methodology may simplify the evaluation of how new technology impacts vulnerable populations, help clinics quantify the limitations of their technological resources, and plan appropriate interventions to improve equity in radiation treatments., Competing Interests: Disclosures Dr Stanley is an educational consultant for Varian Medical Systems. Dr Paradis has grants from Varian Medical Systems and the Michigan Medicine Office of Wellbeing that are unrelated to this work. Dr Covington has a grant from the Michigan Medicine Office of Health Equity and Inclusion unrelated to this work. Drs. Mierzwa and Covington co-lead the M-EQUAL committee of the Michigan Radiation Oncology Quality Consortium (MROQC) where Dr Moncion serves as the physic quality lead for breast. Their work with MROQC is unrelated to this project., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Treatment plan quality is a crucial component for a successful outcome of radiation therapy treatments. As the complexity of radiation therapy planning and delivery techniques increases, the role of the medical physicist in assessing treatment plan quality becomes more critical. Integrating plan quality review throughout the treatment planning process allows improvements without delaying treatment or rushing to produce changes at the last minute. In this work, we aim to provide practical check items for physicists to reference when assessing treatment plan quality with a critical eye, asking questions such as "is this the best dose distribution feasible for this patient?," "could we change any planning parameters to improve plan quality?," and "could we change the planning strategy for this particular patient or for future patients?"; and to work with planners and physicians to create a multidisciplinary collaborative culture that achieves the best plan feasible for every patient. We tabulate the features that affect plan quality in each process step and check details for individual items. This report is aimed at medical physicists, planners, radiation oncologists, and other professionals who are involved in treatment planning., Competing Interests: Disclosures James A. Kavanaugh reports receiving honoraria from Varian Medical. Dustin Jacqmin reports receiving payment from ASTRO/Veteran's Health Administration and holds an unpaid leadership role as Chair of the Working Group on Treatment Planning at AAPM. Sean L. Berry reports received honoraria from RAMPS, the NYC chapter of the AAPM. The other authors have no conflicts of interest to declare., (Copyright © 2024 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Purpose: With recent clinical adoption of online adaptive radiation therapy (oART) and the increased workload associated with adaptive radiation therapy (RT), proper staffing for medical physicists is paramount to safe clinical operation. However, there is currently no consensus on the full-time equivalent (FTE) requirements for safe administration of cone beam computed tomography (CBCT)-guided oART. This study aimed to quantitatively assess medical physics workload and staffing needs of a CBCT-guided oART program., Methods and Materials: We conducted a detailed analysis of the CBCT-guided adaptive planning and treatment workflows, encompassing tasks such as patient consultation, treatment planning, plan review, training, quality assurance, and treatment delivery. Using data from machine logs, clinical database queries, and staff surveys, we present a framework for estimating FTE values for different staffing scenarios, considering medical physicists' roles as planners, adaptors, or both., Results: FTE calculations, based on an example workload of 100 adaptive and 200 nonadaptive patients per year, for 3 staffing scenarios were provided: medical physicists as planners and adaptors (2.9 FTE), medical physicists as planners but not adaptors (2.6 FTE), and medical physicists as adaptors but not planners (1.4 FTE). These findings offer calculation guidance and benchmarks for staffing requirements in CBCT-guided oART programs, emphasizing the need for specific staffing models to accommodate the complexities of adaptive RT., Conclusions: This study outlines a framework for calculating FTE requirements for medical physicists in a CBCT-guided oART program. By analyzing the processes for 3 common adaptive RT workflows, this work can provide effective workforce planning and resource allocation estimates. This analysis can be used either before the implementation of an oART program, for program development, or as a review of current practices to ensure operational efficiency and proper staffing levels are maintained., Competing Interests: Disclosures Richard A. Popple has contracts and honoraria for presentations with Varian Medical Systems and receives royalties for a patent licensed with UAB Research Foundation to Varian Medical Systems. Xenia Ray's institution has grants or contracts from Varian Medical Systems, receives payment for lectures, presentations, speakers bureaus, manuscript writing or educational events, and support for attending meetings from Varian Medical Systems, receives consulting fees from KM Pharmaceutical consulting LLC, and serves in a leadership or fiduciary role on American Association of Physicists in Medicine's TG-395 as Vice Chair. Dennis N. Stanley receives consulting fees, payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing, or educational events, and support for attending meetings and/or travel from Varian Medical Systems as an Educational Consultant. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans., Methods and Materials: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists., Results: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D 1% ,D 95% , and D 99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs., Conclusions: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)