Background Horizontal integration, a pedagogical model in which key concepts are included in curricula at multiple timepoints and in multiple contexts, has been best described in undergraduate medical education (UME), with evidence for better performance on summative assessments. In UME, horizontal integration is a required accreditation standard from the Liaison Committee on Medical Education. This pedagogical approach carries relevance to delivery of key topics in graduate medical education (GME). Radiation oncology GME delivers a high volume of new material through didactic and clinical curricula, including many key concepts with relevance across multiple disease sites. We aim to gauge the national status of integrated curricular delivery across four key concepts: social drivers of health, oligometastatic disease, radiopharmaceuticals, and re-irradiation. Methods Key concepts for inclusion were identified by consensus between authors. Survey items will assess (1) inclusion of concepts in formalized curricula, (2) horizontal integration of concepts in curricula, (3) existence of "stand alone" sessions on included concepts, (4) efforts to increase inclusion of concepts in formalized curricula, and (5) existence of dedicated service(s), teaching faculty, and/or strong referral base for relevant topics. Optional qualitative responses will be included for additional data collection. Survey peer review was completed by an institutional working group with expertise in survey design. Residency program director name and contact email were identified through the American Medical Association Fellowship and Residency Electronic Interactive Database Access (FREIDA) tool for 87 radiation oncology GME programs. For program directors without contact information in FREIDA, the American Society for Radiation Oncology member database was utilized to collect contact email. Collected demographics will be limited to program size to maintain anonymity. This study has been deemed exempt from Institutional Review Board approval. Results We expect to have preliminary results available for discussion at the time of presentation. Results are expected to demonstrate a low level of formalized horizontal integration with responses varied by both institution and concept. Conclusion Radiation oncology GME curricular delivery is impacted by many intrinsic and extrinsic factors including program faculty and resources, institutional referral base and practices, accreditation standards, and rapidly evolving practice. We believe there is high value in intentional and horizontally integrated delivery of key topics across didactic and clinical curricula, including those planned for assessment in our survey. As we undertake efforts to understand the current curricular landscape, assessment of specific models of horizontal integration are also needed to assess effective curricula.

Purpose: Contouring targets for stereotactic body radiation therapy (SBRT) requires expertise for each body site. Likewise, peer reviewers require sufficient expertise to provide an adequate review. In this work, we investigate physician self-reported expertise for performing peer review by body site and how the quality of SBRT peer review is impacted by the expertise of the reviewer. Methods and Materials: The results of 7 years of SBRT rounds, which included information on body site, attending and reviewing physicians, changes to targets, prescriptions, and planning target volume, were analyzed. We surveyed physicians on their expertise for reviewing each body site and defined them as being an expert by body site if they indicated a moderate or high level of competence. Multivariable logistic regression models were used to assess the association between reviewing physician expertise and planning data changes, and whether this varied by body site or by presenting physician expertise. Models were adjusted for physician and case characteristics, and generalized estimating equations were used to account for the correlation of cases reviewed by the same physician. Results: The survey response rate was 95% (20/21) with 4103 cases for analysis. Reviewing physician experts were more likely to make any change, gross target volume, and prescription compared with reviewing physicians who were nonexperts. Controlling for physician expertise and case characteristics, brain, liver, spine, and stereotactic radiosurgery cases have an increased odds of any change being made when compared to lung cases, with odds ratios of 2.42 (95% CI, 1.78-3.30), 1.55 (95% CI, 1.19-2.01), 1.7 (95% CI, 1.31-2.20), and 2.18 (95% CI, 1.73-2.77), respectively. Conclusions: The extent to which changes are made during contour review is associated with both peer reviewer disease-site expertise and disease site. In larger radiation oncology departments relying on a general coverage model, rather than review by disease-site experts, peer review results in variations in the outcome of the preplanning review.

Purpose: Tracking patient doses in radiation oncology is challenging because of disparate electronic systems from various vendors. Treatment planning systems (TPS), radiation oncology information systems (ROIS), and electronic health records (EHR) lack uniformity, complicating dose tracking and reporting. To address this, we examined practices in multiple radiation oncology settings and proposed guidelines for current systems. Methods and Materials: A survey was conducted among members of various professional groups to understand dose reporting practices in TPS, ROIS, and EHR systems. The aim was to identify consistent components and develop guidelines. Results: We identified 6 treatment scenarios where current ROIS defaults fail to accurately represent dose totals. A standardized approach involving 3 reference point types-primary treatment plan reference, dose check, and prescription tracking-was proposed to address these scenarios. Standardizing naming conventions for reference points was also recommended for easier integration with EHRs. The approach requires minimal modifications to existing systems and facilitates easier data transfer and display in EHRs. Conclusions: Standardizing reference points in commercial TPS and ROIS can bridge infrastructure gaps and improve dose tracking in complex clinical scenarios. This standardization, aligned with the American Association of Physicists in Medicine's Task Group (TG) 263, paves the way for continual development of automated, standardized, interoperable tools, enhancing the ease of sharing reference point information. (c) 2024 American Society for Radiation Oncology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

While standardization has been shown to improve patient safety and improve the efficiency of workflows, implementation of standards can take considerable effort and requires the engagement of all clinical stakeholders. Engaging team members includes increasing awareness of the proposed benefit of the standard, a clear implementation plan, monitoring for improvements, and open communication to support successful implementation. The benefits of standardization often focus on large institutions to improve research endeavors, yet all clinics can benefit from standardization to increase quality and implement more efficient or automated workflow. The benefits of nomenclature standardization for all team members and institution sizes, including success stories, are discussed with practical implementation guides to facilitate the adoption of standardized nomenclature in radiation oncology.

Abstract Stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT), collectively termed SRS-SBRT, are advanced treatment modalities delivering high doses of radiation in a single treatment or condensed treatment phase. Due to the small margins and steep dose gradient used in SRS-SBRT, the technical and safety considerations are more stringent than traditional radiation therapy and may include more advanced simulation, patient immobilization, treatment planning, and treatment delivery techniques. Respiratory motion management and intrafraction motion monitoring are often used during SRS-SBRT to ensure treatments are robust to both internal organ motion and patient movement during treatment. To ensure optimal treatment quality, SRS-SBRT programs should use multidisciplinary coordination of care to ensure patient-specific treatment strategies are used for optimal patient outcomes. Quality and safety considerations are presented, including peer review and external validation, for optimizing quality and adhering to national guidelines for stereotactic techniques.

PurposeAAPM Task Group No. 263U1 (Update to Report No. 263 - Standardizing Nomenclatures in Radiation Oncology) disseminated a survey to receive feedback on utilization, gaps, and means to facilitate further adoption.MethodsThe survey was created by TG-263U1 members to solicit feedback from physicists, dosimetrists, and physicians working in radiation oncology. Questions on the adoption of the TG-263 standard were coupled with demographic information, such as clinical role, place of primary employment (e.g., private hospital, academic center), and size of institution. The survey was emailed to all AAPM, AAMD, and ASTRO members.ResultsThe survey received 463 responses with 310 completed survey responses used for analysis, of whom most had the clinical role of medical physicist (73%) and the majority were from the United States (83%). There were 83% of respondents who indicated that they believe that having a nomenclature standard is important or very important and 61% had adopted all or portions of TG-263 in their clinics. For those yet to adopt TG-263, the staffing and implementation efforts were the main cause for delaying adoption. Fewer respondents had trouble adopting TG-263 for organs at risk (29%) versus target (44%) nomenclature. Common themes in written feedback were lack of physician support and available resources, especially in vendor systems, to facilitate adoption.ConclusionsWhile there is strong support and belief in the benefit of standardized nomenclature, the widespread adoption of TG-263 has been hindered by the effort needed by staff for implementation. Feedback from the survey is being utilized to drive the focus of the update efforts and create tools to facilitate easier adoption of TG-263.

Purpose/Objective(s) Patients with cancer who use cannabis frequently note pain as a reason for their cannabis use. Available data do support cannabis use for management of pain in some settings, though the effectiveness of cannabis for cancer-associated pain is less clear. Based on limited data, some have suggested that cannabis might be used as an alternative to opiates for management of cancer-related pain. We sought to determine the relationship between cannabis use and opioid use in a multicenter cohort of patients undergoing radiotherapy for bone metastases. Materials/Methods On January 1, 2021, questions about cannabis use were added to Michigan Radiation Oncology Quality Consortium (MROQC) questionnaires for bone metastasis patients. Pain scores, opioid use, social, demographic, and disease characteristics were also prospectively collected. A multivariable model using logistic regression identified associations between recent cannabis use and opioid use, accounting for relevant patient and disease characteristics. Results Since questions on cannabis were introduced, 2,096 patients have been enrolled. A total of 1143 of 2096 (54.5%) completed questionnaires about recent cannabis use; 1912 of 2096 (91%) completed questionnaires about current opioid use; and 1064 of 2096 (51%) completed both. Among those who completed both, 132 of 1064 (12%) reported recent opioid and cannabis use, 320 of 1064 (30%) reported recent opioid but not cannabis use, 57 of 1064 (5%) reported no recent opioid but recent cannabis use, 281 of 1064 (26%) reported no recent opioid or cannabis use, and the remaining individuals (274/1064 [26%]) declined to answer cannabis use questions by selecting “decline to answer”. In a multivariable model, cannabis use [OR = 2.11 (95% CI = 1.37, 3.26) P = 0.001], along with pain score [Score 1-3 vs 0, OR = 2.32 (95% CI = 1.36, 3.94); Score 4-7 vs 0, OR = 6.55 (95% CI = 4.06, 10.6); Score 8-10 vs 0, OR = 11.20 (95% CI = 6.32, 19.8), P < 0.001], NSAID use [OR = 1.66 (95% CI = 1.17, 2.37) P = 0.005], prior systemic therapy [OR = 0.54 (95% CI = 0.37, 0.78) P = 0.005], and number of metastatic lesions [3-5 vs 1-2, OR = 1.57 (95% CI = 0.95, 2.26); 5-10 vs 1-2, OR = 1.54 (95% CI = 0.91, 2.59); 11+ vs 1-2, OR = 3.26 (95% CI = 2.06, 5.15) P < 0.001] predicted opiate use while age, gender, and race did not. Conclusion Patients with bone metastases frequently use cannabis, opioids, or both. Though it has been suggested that cannabis availability might reduce opioid use among patients with cancer, our finding that cannabis use predicts opioid use does not support this hypothesis. These data suggest a more complex relationship between cannabis use and opioid use in this population. Further study is needed to assess risks of concurrent cannabis and opioid use and to explore patient rationale for concurrent usage.

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.

Purpose/Objective(s) Clinicians iteratively adjust treatment approaches to improve outcomes, but to date, automatable approaches for continuous learning of risk factors as these adjustments are made are lacking. We combined a large-scale, comprehensive real-world Learning Health System infrastructure (LHSI), with automated statistical profiling, visualization, and artificial intelligence (AI) approach to test evidence-based discovery of clinical factors for three endpoints: dysphagia, xerostomia, and 3-year survival for head and neck cancer patients. Materials/Methods Records for 964 patients treated for head and neck cancers with conventional fractionation between 2017 and 2022 were used. Combined information on demographics, diagnosis and staging, social determinants of health measures, chemotherapy, radiation therapy dose volume histogram curves, treatment details, laboratory values, and outcomes from the LHSI to winnow evidence for 485 candidate features. Univariate statistical profiling was performed using bootstrap resampling to detail confidence intervals for the following thresholds and metrics: area under the curve (AUC), sensitivity (SN), specificity (SP), F1, diagnostic odds ratio (DOR), P values for Wilcoxon Rank Sum (WRS), Kolmogorov-Smirnov (KS), and logistic fits of distributions detailed predictive evidence of individual features. Parsimonious XGBoost models were constructed with 10-fold cross validation using training (70%), validation (10%), and test (20%) sets. Probabilistic models utilizing statistical profiling logistic fits of distributions were used to benchmark XGBoost models. Results Incidence of dysphagia ≥ grade 3 within 1 year of treatment was low (11%). Xerostomia ≥ grade 2 (39% to 16%) and survival ≤ 3 years decreased (25% to 15%) over the time range. The strongest grade 2 xerostomia predictor was Glnd_Submand_Low: D15% [Gy] ≥ 45.2 with a logistic model quantifying a gradual rather than an abrupt increase in probability (13.5 + 0.18 (x-41.0 Gy)). Strongest predictive factors for lower likelihood of death by 3 years were GTV_High: Volume [cc] ≤ 21.1, GTV_Low: Volume [cc] ≤ 57.5, Baseline Neutrophil-Lymphocyte Ratio (NLR) ≤ 5.6, Monocyte-Lymphocyte Ratio (MLR) ≤0.56, Platelet-Lymphocyte ratio (PLR) ≤ 202.5. All predictors had WRS and KS P values < 0.02. Statistical profiling enabled detailing gains of XGBoost models with respect to individual features. Time period reductions in distribution of GTV volumes correlated with reductions in death by 3 years. Conclusion Combined use of LHSI, Statistical Profiling and Artificial Intelligence provided a basis for automating evidence-based discovery. Benchmarking AI models with simple probabilistic models provided a means of understanding when results are driven by general areas of overall risk vs. more complex interactions. The method can form a new approach to continuous learning and evidence-based development of clinical trial testable hypothesis and stratifications.

The increasing complexity of radiation therapy treatment presents new potentials for error and suboptimal care. High-performing programs thus not only require adherence to, but also ongoing improvement of, key safety and quality practices. In this article, we review these practices including standardization, risk analysis, peer review, and maintenance of strong safety culture, while also describing recent innovations and promising future directions. We specifically highlight the growing role of artificial intelligence in radiation oncology, both as a tool to deliver safe, high-quality care and as a potential new source of safety challenges.

PurposeWomen remain underrepresented in medical physics in the United States, and determinants of persisting disparities remain unclear. Here, we performed a detailed investigation of American Association of Physicists in Medicine (AAPM) membership trajectories to evaluate trends in Full membership with respect to gender, age, and highest degree.MethodsMembership data, including gender, date of birth, highest degree, membership type, and years of active membership for 1993-2023 were obtained from AAPM. Group I included Full members who joined AAPM in 1993 or later. A subset of Group I including only members who joined and left AAPM since 1993 (former members, Group IF) was used to calculate age at membership cessation and duration. Results were compared by gender and highest degree. A Kaplan-Meier analysis was also used to evaluate membership “survival” by age and highest degree.ResultsComplete data were available for 6,647 current and former Full members (Group I), including 2,211 former members (Group IF). On average, women became Full members at a significantly younger age than men (34.6 vs. 37.5 years of age, p<0.001) and ended their memberships (if applicable) at a significantly younger age than men (46.1 vs. 50.1 years of age, p<0.001). The Kaplan-Meier “survival” analysis showed that for a given age, women were at a significantly greater risk of membership cessation than men, and women with master's degrees had the lowest membership survival of any gender/degree subgroup. When analyzing by membership duration, there was no difference in survival by gender alone. Still, women with PhDs were found to have the greatest membership survival among gender/degree subgroups.ConclusionBoth gender and degree type influenced AAPM membership trajectories. While we have offered a discussion of possible explanations, qualitative data collected from both continuing and departing AAPM members will be critical in the ongoing journey toward gender parity in the profession of medical physics.

The first of its kind in radiation oncology, the OORO is a professional society-based, multi-stakeholder, consensus driven informatics standard. The iterative and collaborative approach to ontology development and refinement aims to ensure that OORO serves as a « living » guidance document, facilitating incremental expansion of data elements over time, as disease site-specific standards are set and RT concepts evolve. Supporting construction of comprehensive "real-world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), OORO holds the potential to revolutionize patient management and improve outcomes.

Purpose: Gender-based discrimination and sexual harassment have been well-studied in the fields of science, technology, engineering, math, and medicine. However, less is known about these topics and their effect within the profession of medical physics. We aimed to better understand and clarify the views and experiences of practicing medical physicists and medical physics residents regarding gender-based discrimination and sexual harassment. Methods and Materials: We conducted in-depth, semistructured, and confidential interviews with 32 practicing medical physicists and medical physics residents across the United States. The interviews were broad and covered the topics of discrimination, mentorship, and work/life integration. All participants were associated with a department with a residency program accredited by the Commission on Accreditation of Medical Physics Education Programs and had appointments with a clinical component. Results: Participants shared views about gender-based discrimination and sexual harassment that were polarized. Some perceived that discrimination and harassment were a current concern within medical physics, while some either perceived that they were not a concern or that discrimination positively affected women and minoritized populations. Many participants shared personal experiences of discrimination and harassment, including those related to unequal compensation, discrimination against mothers, discrimination during the hiring process, gender-biased assumptions about behaviors or goals, communication biases, and overt and persistent sexual harassment.

Introduction Clinicians iteratively adjust treatment approaches to improve outcomes but to date, automatable approaches for continuous learning of risk factors as these adjustments are made are lacking. We combined a large-scale comprehensive real-world Learning Health System infrastructure (LHSI), with automated statistical profiling, visualization, and artificial intelligence (AI) approach to test evidence-based discovery of clinical factors for three use cases: dysphagia, xerostomia, and 3-year survival for head and neck cancer patients. Our hypothesis was that the combination would enable automated discovery of prognostic features generating testable insights. Methods Records for 964 patients treated at a single instiution for head and neck cancers with conventional fractionation between 2017 and 2022 were used. Combined information on demographics, diagnosis and staging, social determinants of health measures, chemotherapy, radiation therapy dose volume histogram curves, and treatment details, laboratory values, and outcomes from the LHSI to winnow evidence for 485 candidate prognostic features. Univariate statistical profiling using benchmark resampling to detail confidence intervals for thresholds and metrics: area under the curve (AUC), sensitivity (SN), specificity (SP), F 1 , diagnostic odds ratio (DOR), p values for Wilcoxon Rank Sum (WRS), Kolmogorov-Smirnov (KS), and logistic fits of distributions detailed predictive evidence of individual features. Statistical profiling was used to benchmark, parsimonious XGBoost models were constructed with 10-fold cross validation using training (70%), validation (10%), and test (20%) sets. Probabilistic models utilizing statistical profiling logistic fits of distributions were used to benchmark XGBoost models. Results Automated standardized analysis identified novel features and clinical thresholds. Validity of automated findings were affirmed with supporting literature benchmarks. Average incidence of dysphagia ≥grade 3 within 1 year of treatment was low (11%). Xerostomia ≥ grade 2 (39% to 16%) and survival ≤ 3 years decreased (25% to 15%) over the time range. Standard planning constraints used limited contribution of those features:: Musc_Constrict_S: Mean[Gy] < 50, Glnd_Submand_High: Mean[Gy] ≤ 30, Glnd_Submand_Low: Mean[Gy] ≤ 10, Parotid_High: Mean[Gy] ≤ 24, Parotid_Low: Mean[Gy] ≤ 10 Additional prognostic features identified for dysphagia included Glnd_Submand_High:D1%[Gy] ≥ 71.1, Glnd_Submand_Low:D4%[Gy] ≥ 55.1, Musc_Constric_S:D10%[Gy] ≥ 56.5, GTV_Low:Mean[Gy] ≥ 71.3. Strongest grade 2 xerostomia feature was Glnd_Submand_Low: D15%[Gy] ≥ 45.2 with a logistic model quantifying a gradual rather than an abrupt increase in probability 13.5 + 0.18 (x-41.0 Gy). Strongest prognostic factors for lower likelihood of death by 3 years were GTV_High: Volume[cc] ≤ 21.1, GTV_Low: Volume[cc] ≤ 57.5, Baseline Neutrophil-Lymphocyte Ratio (NLR) ≤ 5.6, Monocyte-Lymphocyte Ratio (MLR) ≤0.56, Platelet-Lymphocyte ratio (PLR) ≤ 202.5. All predictors had WRS and KS p values < 0.02. Statistical profiling enabled detailing gains of XGBoost models with respect to individual features. Time period reductions in distribution of GTV volumes correlated with reductions in death by 3 years. Discussion Confirming our hypothesis, automated, standardized statistical profiling of a set of statistical metrics and visualizations supported detailing predictive strength and confidence intervals of individual features, benchmarking of subsequent AI models, and clinical assessment. Association of high dose values to submandibular gland volumes, highlighted relevance as surrogate measures for proximal un-contoured muscles including digastric muscles. Higher values of PLR, NLR, and MLR were associated with lower survival rates. Combined use of Learning Health System Infrastructure, Statistical Profiling and Artificial Intelligence provided a basis for faster, more efficient evidence-based continuous learning of risk factors and development of clinical trial testable hypothesis. Benchmarking AI models with simple probabilistic models provided a means of understanding when results are driven by general areas of overall risk vs. more complex interactions.

PURPOSE:Consistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature that enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263 (TG-263). Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263-compliant structure templates in English, Spanish, or French to facilitate data sharing. METHODS AND MATERIALS:Fifty-three premade structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant organs at risk (OARs) (eg, spleen for anatomically relevant sites such as the gastroesophageal junction or stomach), subsite- specific templates (eg, partial breast, whole breast, intact prostate, postoperative prostate, etc) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, although color selections are fully customizable within the program. RESULTS:The resulting program is usable on any Windows system and generates template files in practice-specific Digital Imaging and Communications In Medicine (DICOM) or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1, which ensures continuous access to up-to-date templates. CONCLUSIONS:We have developed a tool to easily create and name DICOM radiation therapy (DICOM-RT) structures sets that are TG-263-compliant for all planning systems using the DICOM standard. The program and source code are publicly available via GitHub to encourage feedback from community users for improvement and guide further development.

AbstractPurposeWith the clinical implementation of kV‐CBCT‐based daily online‐adaptive radiotherapy, the ability to monitor, quantify, and correct patient movement during adaptive sessions is paramount. With sessions lasting between 20–45 min, the ability to detect and correct for small movements without restarting the entire session is critical to the adaptive workflow and dosimetric outcome. The purpose of this study was to quantify and evaluate the correlation of observed patient movement with machine logs and a surface imaging (SI) system during adaptive radiation therapy.MethodsTreatment machine logs and SGRT registration data log files for 1972 individual sessions were exported and analyzed. For each session, the calculated shifts from a pre‐delivery position verification CBCT were extracted from the machine logs and compared to the SGRT registration data log files captured during motion monitoring. The SGRT calculated shifts were compared to the reported shifts of the machine logs for comparison for all patients and eight disease site categories.ResultsThe average (±STD) net displacement of the SGRT shifts were 2.6 ± 3.4 mm, 2.6 ± 3.5 mm, and 3.0 ± 3.2 in the lateral, longitudinal, and vertical directions, respectively. For the treatment machine logs, the average net displacements in the lateral, longitudinal, and vertical directions were 2.7 ± 3.7 mm, 2.6 ± 3.7 mm, and 3.2 ± 3.6 mm. The average difference (Machine–SGRT) was −0.1 ± 1.8 mm, 0.2 ± 2.1 mm, and −0.5 ± 2.5 mm for the lateral, longitudinal, and vertical directions. On average, a movement of 5.8 ± 5.6 mm and 5.3 ± 4.9 mm was calculated prior to delivery for the CBCT and SGRT systems, respectively. The Pearson correlation coefficient between CBCT and SGRT shifts was r = 0.88. The mean and median difference between the treatment machine logs and SGRT log files was less than 1 mm for all sites.ConclusionSurface imaging should be used to monitor and quantify patient movement during adaptive radiotherapy.