Prioritizing clinical trial quality assurance for photons and protons: A failure modes and effects analysis (FMEA) comparison. (May 2023) - Taylor, Paige A.; Miles, Elizabeth; Hoffmann, Lone; Kelly, Sarah M.; Kry, Stephen F.; Sloth Møller, Ditte; Palmans, Hugo; Akbarov, Kamal; Aznar, Marianne C.; Clementel, Enrico; Corning, Coreen; Effeney, Rachel; et al.
Highlights: High risk radiotherapy failures were identified by global clinical trial QA groups. Proton therapy failures have higher risk scores than same failures in photons. Risk scores can help radiotherapy community tailor QA to prevent high risk errors. Abstract: Background and Purpose: The Global Clinical Trials RTQA Harmonization Group (GHG) set out to evaluate and prioritize clinical trial quality assurance. Methods: The GHG compiled a list of radiotherapy quality assurance (QA) tests performed for proton and photon therapy clinical trials. These tests were compared between modalities to assess whether there was a need for different types of assessments per modality. A failure modes and effects analysis (FMEA) was performed to assess the risk of each QA failure. Results: The risk analysis showed that proton and photon therapy shared four out of five of their highest-risk failures (end-to-end anthropomorphic phantom test, phantom tests using respiratory motion, pre-treatment patient plan review of contouring/outlining, and on-treatment/post-treatment patient plan review of dosimetric coverage). While similar trends were observed, proton therapy had higher risk failures, driven by higher severity scores. A sub-analysis of occurrence × severity scores identified high-risk scores to prioritize for improvements in RTQA detectability. A novel severity scaler was introduced to account for the number of patients affected by each failure. This scaler did not substantially alter the ranking of tests, but it elevated the QA program evaluation to the top 20th percentile. This is the first FMEA performed for clinical trial quality assurance. Conclusion: The identification of high-risk errors associated with clinical trials is valuable to prioritize and reduce errors in radiotherapy and improve the quality of trial data and outcomes, and can be applied to optimize clinical radiotherapy QA.