The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management

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138

PurposeTo validate a machine learning approach to Virtual intensity‐modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

Section: Introductionmentioning

confidence: 99%

IMRT QA using machine learning: A multi‐institutional validation

Valdés

Chan

Lim

et al. 2017

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138

“…For each of these data errors, a root cause analysis (RCA) was performed. This type of procedure is a well‐established technique to manage errors in healthcare and involves starting at the clinical incident where the error was discovered and tracing backwards through each step of the treatment workflow until a root cause is identified 2, 18, 19. RCA analysis was based on a review of pertinent files and databases related to each of the data errors.…”

Section: Methodsmentioning

confidence: 99%

“…This process has become more challenging for the physicist to perform in the modern radiotherapy era for a number of reasons. First, the number of treatment methods being offered along with their complexity continues to grow 2. In fact, it was estimated in a 2009 study that progressing from consult to treatment delivery for an external beam radiotherapy patient required approximately 270 different steps.…”

Section: Introductionmentioning

confidence: 99%

Early detection of potential errors during patient treatment planning

Lack

Liang

Benedetti

et al. 2018

PurposeData errors caught late in treatment planning require time to correct, resulting in delays up to 1 week. In this work, we identify causes of data errors in treatment planning and develop a software tool that detects them early in the planning workflow.MethodsTwo categories of errors were studied: data transfer errors and TPS errors. Using root cause analysis, the causes of these errors were determined. This information was incorporated into a software tool which uses ODBC‐SQL service to access TPS's Postgres and Mosaiq MSSQL databases for our clinic. The tool then uses a read‐only FTP service to scan the TPS unix file system for errors. Detected errors are reviewed by a physicist. Once confirmed, clinicians are notified to correct the error and educated to prevent errors in the future. Time‐cost analysis was performed to estimate the time savings of implementing this software clinically.ResultsThe main errors identified were incorrect patient entry, missing image slice, and incorrect DICOM tag for data transfer errors and incorrect CT‐density table application, incorrect image as reference CT, and secondary image imported to incorrect patient for TPS errors. The software has been running automatically since 2015. In 2016, 84 errors were detected with the most frequent errors being incorrect patient entry (35), incorrect CT‐density table (17), and missing image slice (16). After clinical interventions to our planning workflow, the number of errors in 2017 decreased to 44. Time savings in 2016 with the software is estimated to be 795 h. This is attributed to catching errors early and eliminating the need to replan cases.ConclusionsNew QA software detects errors during planning, improving the accuracy and efficiency of the planning process. This important QA tool focused our efforts on the data communication processes in our planning workflow that need the most improvement.

“…In formulating focus topics we consulted the following resources: AAPM Report No. 249 on guidance for CAMPEP‐accredited residencies,2 the CAMPEP residency standards,3 the “Safety is No Accident” report from ASTRO,12 the report of AAPM Task Group 100,7 and the AAPM 2013 Summer School Proceedings on Quality and Safety 13. Additionally, the study of Dunscombe8 provides a broad summary and references to the literature, which is valuable.…”

Section: Methodsmentioning

confidence: 99%

“…Rigorous education in quality and safety is also needed if physicians and physicists are expected to be leaders in this arena, as has been suggested 5, 6. Additionally, the recent AAPM Task Group‐100 report advocates that safety and quality “need to be incorporated in training programs for all radiation oncology disciplines.”7 It is also called for in other reports providing recommendations around patient safety 8…”

Section: Introductionmentioning

confidence: 99%

A patient safety education program in a medical physics residency

Ford

Nyflot

Spraker

et al. 2017

Education in patient safety and quality of care is a requirement for radiation oncology residency programs according to accrediting agencies. However, recent surveys indicate that most programs lack a formal program to support this learning. The aim of this report was to address this gap and share experiences with a structured educational program on quality and safety designed specifically for medical physics therapy residencies. Five key topic areas were identified, drawn from published recommendations on safety and quality. A didactic component was developed, which includes an extensive reading list supported by a series of lectures. This was coupled with practice‐based learning which includes one project, for example, failure modes and effect analysis exercise, and also continued participation in the departmental incident learning system including a root‐cause analysis exercise. Performance was evaluated through quizzes, presentations, and reports. Over the period of 2014–2016, five medical physics residents successfully completed the program. Evaluations indicated that the residents had a positive experience. In addition to educating physics residents this program may be adapted for medical physics graduate programs or certificate programs, radiation oncology residencies, or as a self‐directed educational project for practicing physicists. Future directions might include a system that coordinates between medical training centers such as a resident exchange program.