A structured FMEA approach to optimizing combinations of plan‐specific quality assurance techniques for IMRT and VMAT QA

Abstract

AbstractBackgroundMany commercial tools are available for plan‐specific quality assurance (QA) of radiotherapy plans, with their functionality assessed in isolation. However, multiple QA tools are required to review the full range of potential errors. It is important to assess their effectiveness in combination with each other to look for ways to both streamline the QA process and to make certain that errors of high impact and/or high occurrence are caught before reaching patient treatment.PurposeTo develop a structured method to assess the effective risk reduction of combinations of QA methods for IMRT/VMAT treatments.MethodsFirst, a structured prospective risk assessment was performed to establish the major failure modes (FMs) of IMRT/VMAT QA, and assign occurrence (O), severity (S), and baseline detectability (BD) rankings to them. The baseline assumed that chart checks and linear accelerator QA was performed, but no plan‐specific secondary dose calculation or measurement was done. Second, the detectability of each FM for two secondary dose calculation methods and four plan measurement methods (point‐based dose calculation, Monte‐Carlo‐based dose calculation, 2D fluence‐based measurement, 2.5D phantom‐based measurement, log file analysis with dose recalculation, and log file analysis combined with MLC QA) was determined. Third, we used a minimum detectability approach in addition to each FM's occurrence and severity to determine the optimal combination of QA methods. We analyzed the cumulative risk priority number of eight combinations of QA methods. The analysis was done on (1) all FMs, (2) FMs with high severity, (3) FMs with high‐risk priority numbers (RPN) of O*S*BD, and (4) on FMs with both high severity and high RPN.ResultsOur analysis resulted in 54 FMs, including commissioning, planning, data transfer, and linear accelerator failures. 1D secondary dose calculation plus measurement provided a 19%–22% risk reduction from baseline. 1D/3D secondary dose calculation plus log files created a 25%–32% reduction. 3D secondary dose calculation plus measurement resulted in a 27%–34% reduction. 3D secondary dose calculation plus log files with additional machine QA provided the greatest reduction of 31%–42%.ConclusionThis novel structured approach to comparing combinations of QA methods will allow us to optimize our procedures, with the goal of detecting all clinically significant FMs. Our results show that log‐file QA with 3D dose recalculation and supplemental machine QA provides better risk reduction than measurement‐based QA. This work builds evidence to justify reducing or eliminating measurement‐based PSQA with an independent 3D dose verification, log‐file measurement, and appropriate supplementation of machine QA. The process also highlights FMs that cannot be caught by pre‐treatment QA, prompting us to consider future directions for on‐treatment QA.