TG 198: An implementation guide for TG-142 quality assurance of medical accelerators deviates from the TG mold we’re all used to. The specific charges of the task group were:
- To provide specific procedural guidelines for performing the tests recommended in TG-142
- To provide estimates of the range of time, appropriate personnel, and qualifications necessary to complete the tests in TG-142
- To provide sample daily, weekly, monthly, or annual quality assurance (QA) forms
In addition to these charges, the task group acknowledged and addressed technologies that were not specifically included in TG-142. In that regard, the clinical adoption of volumetric modulated arc therapy (VMAT) and its specific tests and criteria is one good example.
Published in 2021, the task group is unique in one big way. At merely a surface level, a task group created to implement another task group protocol is not the norm. Generally, task groups are less focused on specific tasks and more focused on the goals that should be accomplished or guidance for emerging technology. The more practical how-to documents are often addressed by the Medical Physics Practice Guidelines (MPPGs), which give physicists an idea of a minimum practical standard as well as insights into implementing said standards.
I know what you’re thinking: wasn’t there already an MPPG Report on this topic? Indeed, MPPG 8.a.: Linear accelerator performance tests was published in 2017. The relationship between TG-142, MPPG 8.a., and TG-198 is fairly unique. But ultimately, the combination of the three guidance documents equips the medical physicist with strong resources when assessing their department’s quality assurance protocols.
Goal: To build upon the recommendations of TG-40 for QA of medical linear accelerators, including the technologies and procedures that have been developed since the publication of TG-40.
Goal: To provide a list of critical performance tests to assist the Qualified Medical Physicist (QMP) in establishing and maintaining a safe and effective QA program that matches the clinical use of the accelerator. The QMP is responsible for choosing and implementing appropriate tests.
Goal: To provide specific examples of how to perform the tests recommended in TG-142 and to provide estimates of the range of time and personnel resources required to complete the tests.
In March 2022, the MedPhys Listserv shared a survey on TG-198. When I saw the questions initially, I expected the responses to be vastly different from the final survey results. Upon reading TG-198 initially, I found it to be a thorough and fantastic resource for someone referencing a particular test for their quality assurance protocol. But as it turns out, the survey showed that TG-198 had a mixed response from the community. Bear in mind that the number of responses was 39 and may not represent the true sentiments of the broader community.
Shockingly—at least to me—59% of those who responded were utterly unaware that the report was published. Of those who responded, only 13% said they had adopted the recommendations outlined in TG-198, with another 23% stating that they have partially adopted the recommendations somehow. When asked to choose among TG-142, MPPG 8.a., and TG-198 as a resource for questions regarding machine QA, 65% of respondents said they would first reach for TG-142, followed by TG-198 at 19%, and then MPPG 8.a. at 16%. Full survey results can be found here.
I can see how a physicist may look at an implementation guide on a topic they may have already implemented as redundant. Still, I think questioning our practices regularly and assessing new guidance to understand best methods is healthy and the only way to truly assure quality.
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Regardless of what combination of task groups and MPPGs you’re using for machine QA, we all want to do it in the most efficient, effective way. Right? Now we can.
With RadMachine, you can capture and analyze your daily, weekly, monthly, and annual QA data in keeping with guidance recommendations with integrated image analysis, reporting, trending, and scheduling. A truly multidisciplinary and vendor-neutral option, you can bring all your QA data into one place. No more spreadsheets. RadMachine meets all your QA needs in one platform.
Austin Skinner is a medical physics MS student at Hofstra University, anticipating graduation in May 2022. His work is centered around marketing, including blog posts and case studies. His interests include cooking and reading, as well as the occasional round of golf.
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