Identifying high-risk components in online adaptive RT through a multi-centre FMEA to inform clinical trial QA needs

Online adaptive radiotherapy (oART) has recently gained traction due to its potential clinical benefits. However, it presents significant challenges in developing effective quality assurance (QA) programs, particularly within multi-centre, multi-platform clinical trials settings. In this study, a Failure Mode and Effects Analysis (FMEA) was conducted to identify high-risk components of oART across different centres using different platforms and workflows to inform the development of risk-based QA processes for multi-centre clinical trials.

The National Radiotherapy Trials Quality Assurance (RTTQA) Group and domain experts from five UK radiotherapy centres using oART conducted an FMEA following AAPM Task Group 100 (TG-100) guidelines. Through collaborative discussions, participants identified a comprehensive list of oART-specific failure modes (FMs) and agreed on scoring guidance for rating their occurrence (O), severity (S), and detectability (D). Finally, this standardised scoring system and guidance were used by multidisciplinary teams from different centres to independently score the FMs and the risk priority numbers were calculated as RPN = O*S*D. Median RPN scores were used to rank the FMs from high to low risk.

The two FMs representing the greatest risk in the oART workflow were linked to the daily outlining step; incorrect contouring due to missing or inaccurate clinical information RPN (Median [Range]) = 96 [16 - 120], followed by target or OAR delineation errors RPN = 56 [16 - 140]. These failures were mostly associated with insufficient manual review. Overall, contouring, imaging, dose calculation, and plan appraising FMs were rated as highest risk. These FMs were predominantly operator/workflow dependent.

FMEA identified critical oART-specific FMs requiring targeted QA within clinical trials. Mitigating variability is fundamental to ensuring the validity of clinical trial endpoints. Workflow variability matters: scoring differences across centres highlight the need for harmonisation or cross-validation via end-to-end testing.