A snapshot of medical physics practice patterns

Design and implementation of automated notification systems and an electronic whiteboard for radiation therapy planning monitoring

PURPOSE

Radiotherapy (RT) treatment and treatment planning is a complex process prepared and delivered by a multidisciplinary team of specialists. Efficient communication and notification systems among different team members are therefore essential to ensure the safe, timely delivery of treatments to patients.

METHOD

To address this issue, we developed and implemented automated notification systems and an electronic whiteboard to track every CT simulation, contouring task, the new-start schedule, and physician's appointments and tasks, and notify team members of overdue and missing tasks and appointments. The electronic whiteboard was developed to have a straightforward view of current patients' planning workflow and to help different team members coordinate with each other. The systems were implemented and have been used at our center to monitor the progress of treatment-planning tasks for over 2 years.

RESULTS

The last-minute plans were relatively reduced by about 40% in 2023 compared to 2021 and 2022 with a p-value < 0.05. The overdue contouring tasks of more than 1 day decreased from 46.8% in 2019 and 33.6% in 2020 to 20%-26.4% in 2021-2023 with a p-value < 0.05 after the implementation of the notification system. The rate of plans with 1-3 day planning time decreased by 20.31%, 39.32%, and 24.08% with a p-value < 0.05 and the rate of plans with 1-3 day planning time due to the contouring task overdue more than 1 day decreased by 49.49%, 56.89%, and 46.52% with a p-value < 0.05 after the implementation. The rate of outstanding appointments that are overdue by more than 7 days decreased by more than 5% with a p-value < 0.05 following the implementation of the system.

CONCLUSIONS

Our experience shows that this system requires minimal human intervention, improves the treatment planning workflow and process by reducing errors and delays in the treatment planning process, positively impacts on-time treatment plan completion, and reduces the need for compressed or rushed treatment planning timelines.

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring

PURPOSE

Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a multidimensional evaluation method to test pretrained AI-based automated contouring solutions across a network of clinics.

METHODS AND MATERIALS

Curated data included 121 patient planning computed tomography (CT) scans with a total of 859 clinically approved contours used for treatment from 4 clinics. Regions of interest (ROIs) were generated with 3 commercial AI-based automated contouring software solutions (AI1, AI2, AI3) spanning the following disease sites: brain, head and neck (H&N), thorax, abdomen, and pelvis. Quantitative agreement between AI-generated and clinical contours was measured by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative assessment was performed by multiple experts scoring blinded AI-contours using a Likert scale. Workflow and usability surveying was also conducted.

RESULTS

AI1, AI2, and AI3 contours had high quantitative agreement in 27.8%, 32.8%, and 34.1% of cases (DSC >0.9), performing well in pelvis (median DSC = 0.86/0.88/0.91) and thorax (median DSC = 0.91/0.89/0.91). All 3 solutions had low quantitative agreement in 7.4%, 8.8%, and 6.1% of cases (DSC <0.5), performing worse in brain (median DSC = 0.65/0.78/0.75) and H&N (median DSC = 0.76/0.80/0.81). Qualitatively, AI1 and AI2 contours were acceptable (rated 1-2) with at most minor edits in 70.7% and 74.6% of ROIs (2906 ratings), higher for abdomen (AI1: 79.2%) and thorax (AI2: 90.2%), and lower for H&N (29.0/35.6%). An end-user survey showed strong user preference for full automation and mixed preferences for accuracy versus total number of structures generated.

CONCLUSIONS

Our evaluation method provided a comprehensive analysis of both quantitative and qualitative measures of commercially available pretrained AI autocontouring algorithms. The evaluation framework served as a roadmap for clinical integration that aligned with user workflow preference.

Current status of the educational environment to acquire and maintain the professional skills of radiotherapy technology and medical physics specialists in Japan: a nationwide survey

This study aimed to investigate the educational environment of radiotherapy technology and medical physics specialists (RTMP) in Japan. We conducted a nationwide questionnaire survey in radiotherapy institutions between June and August 2022. Participants were asked questions regarding the educational system, perspectives on updating RTMP's skills and qualifications, and perspectives on higher education for RTMP at radiotherapy institutions. The results were then analyzed in detail according to three factors: whether the hospital was designed for cancer care, whether it was a Japanese Society for Radiation Oncology (JASTRO)-accredited hospital, and whether it was an intensity-modulated radiation therapy charged hospital. Responses were obtained from 579 (69%) nationwide radiation therapy institutions. For non-qualified RTMP, 10% of the institutions had their own educational systems, only 17% of institutions provided on-the-job training, and 84% of institutions encouraged participation in educational lectures and workshops in academic societies. However, for qualified RTMP, 3.0% of institutions had their own educational systems, only 8.9% of the institutions provided on-the-job training, and 83% encouraged participation in academic conferences and workshops. Less than 1% of the facilities offered salary increases for certification, whereas 8.2% offered consideration for occupational promotion. Regarding the educational environment, JASTRO-accredited hospitals were better than general hospitals. Few institutions have their own educational systems for qualified and non-qualified RTMP, but they encourage them to attend educational seminars and conferences. It is desirable to provide systematic education and training by academic and professional organizations to maintain the skills of individuals.

Dosimetric characterization of foam padding with posterior fields in palliative radiation therapy

Patients undergoing external beam radiation therapy for the palliative treatment of painful bony metastases may have difficulty maintaining a still position on a rigid uncovered couch top, both during CT simulation as well as during patient setup, image guidance, and treatment on the linear accelerator. For these patients, a thin foam pad or mattress is sometimes used to mitigate patient discomfort. It was desired to quantify the effect of the padding in cases in which the patient is to be treated supine with posterior beams when the majority of the beam weighting traverses both the couch and the pad. Ion chamber measurements in-phantom were acquired with 6 MV, 10 MV, and 15 MV photon beams. At depths of maximum dose, the pad resulted in a difference of signal collected ≤1%. At the phantom surface, the pad resulted in an increase in signal ranging from 1% to 6.5% for the measured beams. CT data of the pad, both with and without applied pressure, indicated that the pad had average HU values close to air.

Survey on the interest and commitment of AIFM members to scientific activities (SicAS) - The initiative of the FutuRuS working group

PURPOSE

The "FutuRuS" working group of the Italian Association of Medical Physics and Health Physics (AIFM) designed a survey (SicAS) to get feedback from its members regarding their interests and their experience in taking part in scientific activities and events, with the objective of focusing future efforts of the AIFM towards increasing the scientific activity of the medical physics expert (MPE).

METHODS

SicAS was sent out in March 2022 to all AIFM members by newsletter and official communication. SicAS was structured into three sections: personal information and institution of affiliation information, involvement in scientific activities, interest in and commitment to scientific activities. Responses were collected in a fully anonymised mode from the Google Forms platform and analysed with descriptive statistics.

RESULTS

Out of 1289 members (active at the end of 2021), 467 responded to the Survey (response rate of 36%). The Survey results highlighted that AIFM members ranked the involvement of the MPE in scientific activities as highly relevant to the profession. However, 34.7% indicated devoting less than 10% of their working time to scientific activities. 67.5% of the respondents were dissatisfied with the time spent on scientific activities. The primary barrier was the lack of time (77%), followed by a lack of mentoring (32%).

CONCLUSIONS

SicAS highlighted the need for AIFM initiatives to support members' scientific activities. National societies should help develop and support networks between members, create links among universities, hospitals, research institutions and industries, and provide guidelines and learning platforms for enhancing the MPEs' involvement in scientific activities.

Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning

Background

Accelerated partial breast irradiation (APBI) yields similar rates of recurrence and cosmetic outcomes as compared to whole breast radiation therapy (RT) when patients and treatment techniques are appropriately selected. APBI combined with stereotactic body radiation therapy (SBRT) is a promising technique for precisely delivering high levels of radiation while avoiding uninvolved breast tissue. Here we investigate the feasibility of automatically generating high quality APBI plans in the Ethos adaptive workspace with a specific emphasis on sparing the heart.

Methods

Nine patients (10 target volumes) were utilized to iteratively tune an Ethos APBI planning template for automatic plan generation. Twenty patients previously treated on a TrueBeam Edge accelerator were then automatically replanned using this template without manual intervention or reoptimization. The unbiased validation cohort Ethos plans were benchmarked via adherence to planning objectives, a comparison of DVH and quality indices against the clinical Edge plans, and qualitative reviews by two board-certified radiation oncologists.

Results

85% (17/20) of automated validation cohort plans met all planning objectives; three plans did not achieve the contralateral lung V1.5Gy objective, but all other objectives were achieved. Compared to the Eclipse generated plans, the proposed Ethos template generated plans with greater evaluation planning target volume (PTV_Eval) V100% coverage (p = 0.01), significantly decreased heart V1.5Gy (p< 0.001), and increased contralateral breast V5Gy, skin D0.01cc, and RTOG conformity index (p = 0.03, p = 0.03, and p = 0.01, respectively). However, only the reduction in heart dose was significant after correcting for multiple testing. Physicist-selected plans were deemed clinically acceptable without modification for 75% and 90% of plans by physicians A and B, respectively. Physicians A and B scored at least one automatically generated plan as clinically acceptable for 100% and 95% of planning intents, respectively.

Conclusions

Standard left- and right-sided planning templates automatically generated APBI plans of comparable quality to manually generated plans treated on a stereotactic linear accelerator, with a significant reduction in heart dose compared to Eclipse generated plans. The methods presented in this work elucidate an approach for generating automated, cardiac-sparing APBI treatment plans for daily adaptive RT with high efficiency.

Automated planning stage tracking and analysis through an integrated whiteboard system

PURPOSE

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.

METHODS

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.

RESULTS

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 (p<10^-14) and 2.5 days (p<10^-11), respectively. Overall time to plan completion was reduced by 33% (5.1 days; p<10^-11).

CONCLUSIONS

This 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.

Impact of an integrated whiteboard system on physics QA turnaround time

Purpose

To evaluate the impact of a digital whiteboard system integrated with data from the oncology information system (OIS) on the urgency of physics quality assurance (QA) tasks in the radiation oncology department.

Methods

Quality check list (QCL) items in the Mosaiq OIS corresponding to eight discrete, sequential steps in the treatment planning process were created. A whiteboard to graphically display active QCLs automatically and in real time was implemented in March 2020 using R shiny. QCL data with completion status were collected in two 12‐month time periods before and after whiteboard implementation: January 2019–December 2019 and July 2020–June 2021. For all plans requiring patient‐specific QA, we recorded when each plan was available for physics QA and which treatments started the following day. We further classified those plans into four categories (urgency levels 1–4 with 4 being the most urgent) depending on how much time was available to perform QA. We compared the proportion of these next‐day QAs in each category between time periods accounting for plan type, day of the week, and time of year.

Results

Overall QA numbers were similar between time periods with 797 and 765 QAs total. The total proportion of next‐day QA decreased by 27% and the proportions of urgency levels 1 and 4 both showed significant decreases after whiteboard implementation of 29.2% and 54.9%, respectively (p<0.05). All plan types had reduced proportions of next‐day QAs, especially nonstereotactic body radiation therapy (non‐SBRT) (30.3% decrease, p<0.05). Fridays and the months of October–December had the highest proportion of next‐day QAs but showed significant reductions of 19.1% and 40.6% in the proportion of next‐day QAs, respectively (p<0.05).

Conclusions

The integrated whiteboard system significantly reduced the proportion of last‐minute physics work, increasing patient safety. Advantages of the integrated whiteboard were low cost, low overhead with automatic interface to the OIS, and concurrent user support.

MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy

The report of the American Association of Physicists in Medicine (AAPM) Task Group No. 132 published in 2017 reviewed rigid image registration and deformable image registration (DIR) approaches and solutions to provide recommendations for quality assurance and quality control of clinical image registration and fusion techniques in radiotherapy. However, that report did not include the use of DIR for advanced applications such as dose warping or warping of other matrices of interest. Considering that DIR warping tools are now readily available, discussions were hosted by the Medical Image Registration Special Interest Group (MIRSIG) of the Australasian College of Physical Scientists & Engineers in Medicine in 2018 to form a consensus on best practice guidelines. This position statement authored by MIRSIG endorses the recommendations of the report of AAPM task group 132 and expands on the best practice advice from the 'Deforming to Best Practice' MIRSIG publication to provide guidelines on the use of DIR for advanced applications.

Report of the ACPSEM radiation oncology medical physics workforce modelling project task group

The ACPSEM radiation oncology medical physics workforce modelling project task group was formed to acquire a snapshot of practices in Australia and New Zealand and to develop an activity-based workforce model. To achieve this, two surveys were carried out, capturing the work practices of 98 radiation oncology departments and 182 college members. The member survey provided a snapshot of the current workforce: their demographics, work conditions, professional recognition, and future plans. The facility survey provided an Australian and New Zealand contextualisation of the volume-based activities defined in the International Atomic Energy Agency activity-based radiation oncology staffing model at a granular level. An ACPSEM ROMP workforce model was developed to be a modelling tool applicable at both the facility and sector levels.

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow

Purpose:

We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process.

Methods:

We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified.

Results:

Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans.

Conclusion:

The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.

Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms

Purpose

To investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms.

Methods

Eleven scans from patients with head‐and‐neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low‐dose simulation software. The impact of these imaging parameters on two in‐house auto‐contouring algorithms, one convolutional neural network (CNN)‐based and one multiatlas‐based system (MACS) was investigated for 183 reconstructed scans. For each algorithm, auto‐contours for organs‐at‐risk were compared with auto‐contours from scans with 3 mm slice thickness, 0.977 mm pixel size, and 100% CT dose using Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD).

Results

Increasing the slice thickness from baseline value of 3 mm gave a progressive reduction in DSC and an increase in HD and MSD on average for all structures. Reducing the CT dose only had a relatively minimal effect on DSC and HD. The rate of change with respect to dose for both auto‐contouring methods is approximately 0. Changes in pixel size had a small effect on DSC and HD for CNN‐based auto‐contouring with differences in DSC being within 0.07. Small structures had larger deviations from the baseline values than large structures for DSC. The relative differences in HD and MSD between the large and small structures were small.

Conclusions

Auto‐contours can deviate substantially with changes in CT acquisition and reconstruction parameters, especially slice thickness and pixel size. The CNN was less sensitive to changes in pixel size, and dose levels than the MACS. The results contraindicated more restrictive values for the parameters should be used than a typical imaging protocol for head‐and‐neck.

Dose accuracy improvement on head and neck VMAT treatments by using the Acuros algorithm and accurate FFF beam calibration

Background

The purpose of this study was to assess dose accuracy improvement and dosimetric impact of switching from the anisotropic analytical algorithm (AA) to the Acuros XB algorithm (AXB) when performing an accurate beam calibration in head and neck (H&N) FFF-VMAT treatments.

Materials and methods

Twenty H&N cancer patients treated with FFF-VMAT techniques were included. Calculations were performed with the AA and AXB algorithm (dose-to-water - AXB_w- and dose-to-medium - AXB_m-). An accurate beam calibration was used for AXB calculations. Dose prescription to the tumour (PTV70) and at-risk-nodal region (PTV58.1) were 70 Gy and 58.1 Gy, respectively. A PTV70_{_bone} including bony structures in PTV70 was contoured. Dose-volume parameters were compared between the algorithms. Statistical tests were used to analyze the differences in mean values and the correlation between compliance with the D95 > 95% requirement and occurrence of local recurrence.

Results

AA systematically overestimated the dose compared to AXB algorithm with mean dose differences within 1.3 Gy/2%, except for the PTV70_{_bone} (2.2 Gy/3.2%). Dose differences were significantly higher for AXB_m calculations when including accurate beam calibration (maximum dose differences up to 2.8 Gy/4.1% and 4.2 Gy/6.3% for PTV70 and PTV70_{_bone}, respectively). 80% of AA-calculated plans did not meet the D95 > 95% requirement after recalculation with AXB_m and accurate beam calibration. The reduction in D95 coverage in the tumour was not clinically relevant.

Conclusions

Using the AXB_m algorithm and carefully reviewing the beam calibration procedure in H&N FFF-VMAT treatments ensures (1) dose accuracy increase by approximately 3%; (2) a consequent dose increase in targets; and (3) a dose reporting mode that is consistent with the trend of current algorithms.

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

BACKGROUND

The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated?

SUMMARY

In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

Physician review of image registration and normal structure delineation

Introduction

Methods

Results

Conclusion

Simple phantom fabrication for MRI-based HDR brachytherapy applicator commissioning

A new high dose rate (HDR) brachytherapy program was initiated in a community hospital setting, with the goal of using magnetic resonance (MR) images with the implant in place during the planning process. Physics acceptance testing and commissioning was completed for key program components, including multiple applicators. To image new applicators for MRI‐based planning prior to use with patients, agar gel doped with copper sulfate was created using simple, MR‐safe household materials as a practical and inexpensive alternative to custom‐machined precision phantoms. Applicators in‐phantom were scanned in a 1.5 T MRI scanner using the same sequences developed for the brachytherapy program, then rigidly registered to high‐resolution computed tomography (CT) images to assess distortion, artifact, and geometric displacement. To date, Varian tandem and ring sets, segmented cylinders, cervical probes, endometrial applicators; and third‐party plastic needles, tandems, and vaginal guides have been imaged in phantom and are available for use clinically.

An international survey on the clinical use of rigid and deformable image registration in radiotherapy

OBJECTIVES

Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration.

METHODS

A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018.

RESULTS

There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively).

CONCLUSIONS

This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.

An IAEA survey of radiotherapy practice including quality assurance extent and depth

Background: The IAEA recommends a quality assurance program in radiotherapy to ensure safe and effective treatments. In this study, radiotherapy departments were surveyed on their current practice including the extent and depth of quality assurance activities.Methods: Radiotherapy departments were voluntarily surveyed in three stages, firstly, in basic facility information, secondly, in quality assurance activities and treatment techniques, and thirdly, in a snapshot of quality assurance, departmental and treatment activities.Results: The IAEA received completed surveys from 381 radiotherapy departments throughout the world with 100 radiotherapy departments completing all three surveys. Dominant patterns were found in linac-based radiotherapy with access to treatment planning systems for 3D-CRT and 3D imaging. Staffing levels for major staff groups were on average in the range recommended by the IAEA. The modal patient workload per EBRT unit was as expected in the range of 21-30 patients per day, however significant instances of high workload (more than 50 patients per day per treatment unit) were reported. Staffing levels were found to correlate with amount of treatment equipment and patient workload. In a self-assessment of quality assurance performance, most radiotherapy departments reported that they would perform at least 60% of the quality assurance activities itemized in the second survey, with particular strength in equipment quality control. In a snapshot survey of quality assurance performance, again equipment quality control practice was well developed, particularly for the treatment equipment.Conclusions: The IAEA surveys provide a snapshot of current radiotherapy practice including quality assurance activities.

A survey of surface imaging use in radiation oncology in the United States

Surface imaging (SI) has been rapidly integrated into radiotherapy clinics across the country without specific guidelines and recommendations on its commissioning and use aside from vendor-provided information. A survey was created under the auspices of AAPM TG-302 to assess the current status of SI to identify if there is need for formal guidance. The survey was designed to determine the institutional setting of responders, availability and length of its use, commissioning procedures, and clinical applications. This survey was created in REDCap, and approved as IRB exempt to collect anonymized data. Questions were reviewed by multiple physicists to ensure concept validity and piloted by a small group of independent physicists to ensure process validity. All full members of AAPM self-identified as "therapy" or "other" were sent the survey link by email. The survey was active from February to March 2018. Of 3677 members successfully contacted, 439 completed responses; the summary of these responses provides insight on current surface imaging clinical practices, though they should not be assumed to be representative of radiation oncology as a whole. Results showed that 53.3% of respondents have SI in their clinics, mostly in treatment rooms, rarely in simulation rooms. Half of those without SI plan on purchasing it within 3 years. Over 10% have SI but do not use it clinically, 36.8% classify themselves as "expert" users, and 85.5% agreed/strongly agreed that SI guidelines are needed. Initial positioning with SI is most common for breast/chestwall and SRS/SBRT treatments, least common for pediatrics. Use of SI for intra-fraction monitoring follows a similar distribution. Gating with SI is most prevalent for breast/chestwall (66.0%) but also used in SBRT (33.0%), and non-SBRT lung/abdomen (<30%) treatments. SI is a rapidly growing technology in the field with widespread use for several anatomic sites. Guidelines and recommendations on commissioning and clinical use are warranted.