J O'Daniel*, Y Cui, W Giles, J Adamson, Duke University Medical Center, Durham, NC

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  J O'Daniel

SU-F-BRA-6 (Sunday, 7/10/2022) 2:00 PM - 3:00 PM [Eastern Time (GMT-4)]

Ballroom A

Purpose: To quantitatively determine the most effective combination of pre-treatment patient specific QA (PSQA) tests for IMRT/VMAT.

Methods: First, a structured prospective risk assessment was performed to determine major failure modes (FMs) of IMRT/VMAT and assign occurrence (O) and severity (S) rankings, as well as a baseline detectability (BD) in absence of PSQA (secondary dose calculation and pre-treatment measurement). Second, we determined detectability of each FM for various PSQA tests: two secondary MU calculation methods and three dose measurement methods (point-based dose calculation, Monte-Carlo-based dose calculation, EPID measurement, a diode phantom array, and dosimetric analysis using log files). Third, we used various methods to determine the cumulative detectability score in addition to occurrence and severity to determine the optimal combination of PSQA tests for our institution.

Results: We identified 53 FMs, which originated at treatment planning, data transfer, and linear accelerator delivery. We focused on the top quartile of FM based on combined risk priority number prior to patient specific QA (O*S*BD) as well as those with highest severity ranking. For the PSQA tests evaluated, each was effective at detecting unique FMs, while certain FMs could not be caught at all (setup failures, random errors post-QA), which indicate the need for an additional QA approach. The lowest scoring QA combination was a point-based dose calculation with EPID measurement, which is the standard procedure in many clinics. The best scoring combination was either secondary MU calculation method with log file analysis and additional MLC QA (necessary to catch blind spots in log files).

Conclusion: An FMEA-based approach to compare combinations of QA tests has potential for optimizing radiotherapy QA strategies, with the goal of detecting all clinically significant FMs. The process also highlights FMs not caught by PSQA, warranting future consideration of more comprehensive QA approaches.

Keywords

Quality Assurance, Quality Control, Monte Carlo

Taxonomy

IM/TH- Formal Quality Management Tools: Failure modes and effects analysis

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