Improving linear accelerator service response with a real- time electronic event reporting system

Digital skills of therapeutic radiographers/radiation therapists - Document analysis for a European educational curriculum

INTRODUCTION

It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Radiographers/Radiation Therapists (TR/RTTs) have a key role to play in patient management. It is essential for TR/RTTs to keep abreast with new technologies and continuously develop the digital skills necessary for safe RT practice. The RT profession and education is not regulated at European Union level, which leads to heterogeneity in the skills developed and practised among countries. This study aimed to explore the white and grey literature to collate data on the relevant digital skills required for TR/RTTs practice.

METHODS

An exhaustive systematic search was conducted to identify literature discussing digital skills of TR/RTTs; relevant grey literature was also identified. A thematic analysis was performed to identify and organise these skills into themes and sub-themes.

RESULTS

195 digital skills were identified, organised in 35 sub-themes and grouped into six main themes: (i) Transversal Digital Skills, (ii) RT Planning Image, (iii) RT Treatment Planning, (iv) RT Treatment Administration, (v) Quality, Safety and Risk Management, and (vi) Management, Education and Research.

CONCLUSION

This list can be used as a reference to close current gaps in knowledge or skills of TR/RTTs while anticipating future needs regarding the rapid development of new technologies (such as Artificial Intelligence or Big Data).

IMPLICATIONS FOR PRACTICE

It is imperative to align education with current and future RT practice to ensure that all RT patients receive the best care. Filling the gaps in TR/RTTs skill sets will improve current practice and provide TR/RTTs with the support needed to develop more advanced skills.

Does advance contact with research participants increase response to questionnaires: an updated systematic review and meta-analysis

Background

Questionnaires remain one of the most common forms of data collection in epidemiology, psychology and other human-sciences. However, results can be badly affected by non-response. One way to potentially reduce non-response is by sending potential study participants advance communication. The last systematic review to examine the effect of questionnaire pre-notification on response is 10 years old, and lacked a risk of bias assessment.

Objectives

Update the section of the Cochrane systematic review, Edwards et al. (2009), on pre-notification to include 1) recently published studies, 2) an assessment of risk of bias, 3) Explore if heterogeneity is reduced by: delay between pre-contact and questionnaire delivery, the method of pre-contact, if pre-contact and questionnaire delivery differ, if the pre-contact includes a foot-in-the-door manipulation, and study’s the risk of bias.

Methods

Inclusion criteria: population: any population, intervention: comparison of some type of pre-notification, comparison group: no pre-notification, outcome: response rates. Study design: randomised controlled trails. Exclusion criteria: NA. Data sources: Studies which cited or were included in Edwards et al. (2009); We additionally searched: CINAHL, Web of Science, PsycInfo, MEDLINE, EconLit, EMBASE, Cochrane Central, Cochrane CMR, ERIC, and Sociological Abstracts. The searches were implemented in June 2018 and May 2021. Study screening: a single reviewer screened studies, with a random 10% sample independently screened to ascertain accuracy. Data extraction: data was extracted by a single reviewer twice, with a week between each extraction. Risk of Bias: within studies bias was assessed using the Cochrane Risk of Bias tool (ROB1) by a single unblinded reviewer, across studies bias was assessed using funnel plots. Synthesis Method: study results were meta-analysed with a random effects model using the final response rate as the outcome. Evaluation of Uncertainty: Uncertainty was evaluated using the GRADE approach.

Results

One hundred seven trials were included with 211,802 participants. Over-all pre-notification increased response, OR = 1.33 (95% CI: 1.20–1.47). However, there was a large amount of heterogeneity (I² = 97.1%), which was not explained by the subgroup analyses. In addition, when studies at high or unclear risk of bias were excluded the effect was to reduced OR = 1.09 (95% CI: 0.99–1.20). Because of the large amount of heterogeneity, even after restricting to low risk of bias studies, there is still moderate uncertainty in these results.

Conclusions

Using the GRADE evaluation, this review finds moderate evidence that pre-notification may not have an effect on response rates.

Funding

Economic and Social Research Council.

Preregistration

None.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01435-2.

Operational Insights From the Longitudinal Analysis of a Linear Accelerator Machine Log

Purpose

This study aimed to perform a longitudinal analysis of linear accelerator (linac) technical faults reported with a cloud-based Machine Log system in use in a busy academic clinic and derive operational insights related to linac reliability, clinical utilization, and performance.

Methods

We queried the Machine Log system for the following parameters: linac type, number of reported technical faults, types of fault, number of faults where the linac was disabled, and estimated clinical downtime. The number of fractions treated and monitor units (MU) delivered were obtained from the record and verify system as metrics of linac utilization and to normalize the number of reported linac faults, facilitating inter-comparison. Two Varian TrueBeam C-arm linacs (Varian Medical Systems, Palo Alto, CA), one Varian 21iX C-arm linac (Varian Medical Systems, Palo Alto, CA), and one newly installed Varian Halcyon ring gantry linac (Varian Medical Systems, Palo Alto, CA) were evaluated. The linacs were studied over a 30-month period from September 2017 to March 2020. 

Results

Over 30 months, comprising 677 clinical days, 1234 faults were reported from all linacs, including 153 “linac down” events requiring rescheduling or cancellation of treatments. The TrueBeam linacs reported nearly twice as many imaging, multileaf collimator (MLC), and beam generation faults per fraction, and MU as the Halcyon. Halcyon experienced fewer beam generation/steering, accessory, and cooling-related faults than the other linacs but reported more computer and networking issues. Although it employs a relatively new MLC design compared to the C-arm linacs and delivers primarily intensity-modulated treatments, Halcyon reported fewer MLC faults than the other linacs. The 21iX linac had the fewest software-related faults but was subject to the most cooling-related faults, which we attributed to extensive use of this linac for treatment techniques with extended beam-on times.

Conclusions

A longitudinal analysis of a cloud-based Machine Log system yielded operational insights into the utilization, performance, and technical reliability of the linacs in use at our institution. Several trends in linac sub-system reliability were identified and could be attributed to either age, design, clinical use, or operational demands. The results of this analysis will be used as a basis for designing linac quality assurance schedules that reflect actual linac usage and observed sub-system reliability. Such a practice may contribute to a clinic workflow subject to fewer disruptions from linac faults, ultimately improving efficiency and patient safety.

Benchmarking failure mode and effects analysis of electronic brachytherapy with data from incident learning systems

PURPOSE

Failure modes and effects analysis (FMEA) is a prospective risk assessment tool for identifying failure modes in equipment or processes and informing the design of quality control systems. This work aims to benchmark the performance of FMEAs for electronic brachytherapy (eBT) of the skin and for breast by comparing predicted versus actual failure modes reported in multiple incident learning systems (ILS).

METHODS AND MATERIALS

Two public and our institution's internal ILS were queried for Xoft Axxent eBT-related events over 9 years. The failure modes and Risk Priority Numbers (RPNs) were taken from FMEAs previously performed for Xoft eBT of nonmelanoma skin cancer and breast intraoperative radiation therapy (IORT). For each event, the treatment site and primary failure mode was compared with the failure modes and RPNs from that site's FMEA.

RESULTS

49 events involving Xoft eBT were identified. Thirty-one (63.3%) involved breast IORT, and 18 (36.7%) involved the skin. Three events could not be linked to an FMEA failure mode. In 87.7% of events, the primary failure mode ranked in the FMEA top 10 by RPNs. In 83.3% of skin events, the failure modes ranked in the top 10 by RPN or severity. In 90.3% of IORT events, the failure modes ranked within the top 10 by RPN or severity.

CONCLUSIONS

Evaluating FMEA failure modes against ILS data demonstrates that FMEA is effective at predicting failure modes but can be dependent on user experience. ILS data can improve FMEA by identifying potential failure modes and suggesting realistic occurrence, detectability, and severity values.

Streamlining Regulatory Activities Within Radiation Therapy Departments Using QATrack

Radiation therapy departments are faced with the challenge of tracking numerous quality control tests as well as monitoring service events affecting radiation therapy treatment units. Service events, in particular, pose a challenge since the clinic must be able to provide evidence to the regulatory body that both the service work and any required follow-up tests were recorded and authorized by the appropriate staff. This article presents an integrated approach to tracking quality control tests and service event logs using QATrack+. The newly developed version of this quality assurance software integrates quality control tracking with the service event log, allowing a direct link between a service event and any initiating routine tests or follow-up tests that are performed. This improves the ability of a licensee to ensure compliance with regulations and permits a simple platform from which to access all machine equipment tests and service events. Furthermore, this improves the ability of a department to assess the service record of equipment and to identify trends in failure modes.

Technical Note: Assessing the performance of monthly CBCT image quality QA

PURPOSE

To assess the performance of routine cone-beam computed tomography (CBCT) quality assurance (QA) at predicting and diagnosing clinically recognizable linac CBCT image quality issues.

METHODS

Monthly automated linac CBCT image quality QA data were acquired on eight Varian linacs (Varian Medical Systems, Palo Alto, CA) using the CATPHAN 500 series phantom (The Phantom Laboratory, Inc., Greenwich, NY) and Total QA software (Image Owl, Inc., Greenwich, NY) over 34 months between July 2014 and May 2017. For each linac, the following image quality metrics were acquired: geometric distortion, spatial resolution, Hounsfield Unit (HU) constancy, uniformity, and noise. Quality control (QC) limits were determined by American Association of Physicists in Medicine (AAPM) expert consensus documents Task Group (TG-142 and TG-179) and the manufacturer acceptance testing procedure. Clinically recognizable CBCT issues were extracted from the in-house incident learning system (ILS) and service reports. The sensitivity and specificity of CATPHAN QA at predicting clinically recognizable image quality issues was investigated. Sensitivity was defined as the percentage of clinically recognizable CBCT image quality issues that followed a failing CATPHAN QA. Quality assurance results are categorized as failing if one or more image quality metrics are outside the QC limits. The specificity of CATPHAN QA was defined as one minus the fraction of failing CATPHAN QA results that did not have a clinically recognizable CBCT image quality issue in the subsequent month. Receiver operating characteristic (ROC) curves were generated for each image quality metric by plotting the true positive rate (TPR) against the false-positive rate (FPR).

RESULTS

Over the study period, 18 image quality issues were discovered by clinicians while using CBCT to set up the patient and five were reported prior to x-ray tube repair. The incidents ranged from ring artifacts to uniformity problems. The sensitivity of the TG-142/179 limits was 17% (four of the prior monthly QC tests detected a clinically recognizable image quality issue). The area under the curve (AUC) calculated for each image quality metric ROC curve was: 0.85 for uniformity, 0.66 for spatial resolution, 0.51 for geometric distortion, 0.56 for noise, 0.73 for HU constancy, and 0.59 for contrast resolution.

CONCLUSION

Automated monthly QA is not a good predictor of CBCT image quality issues. Of the available metrics, uniformity has the best predictive performance, but still has a high FPR and low sensitivity. The poor performance of CATPHAN QA as a predictor of image quality problems is partially due to its reliance on region-of-interest (ROI) based algorithms and a lack of a global algorithm such as correlation. The manner in which image quality issues occur (trending toward failure or random) is still not known and should be studied further. CBCT image quality QA should be adapted based on how CBCT is used clinically.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.