Risk assessment and quality management in AIO based on CT-linac for nasopharyngeal carcinoma: An improved FMEA and FTA approach

Background

All-in-one radiotherapy workflow (AIO) is a novel one-stop solution that integrates the multiple conventional radiotherapy steps from simulation, contouring, planning, image guidance, beam delivery, and in vivo dosimetry into a single device (integrated computed tomography linac, the uRT-linac 506c), making the treatment process more efficient and convenient while reducing errors for cancer patients' initial radiotherapy. Despite its numerous advantages, the implementation of AIO faces challenges such as interdisciplinary coordination, software and hardware complexity, and reliance on artificial intelligence. To ensure its safety and effectiveness, it is necessary to conduct a risk assessment and identify appropriate quality management measures.

Purpose

To perform risk assessment on the AIO for nasopharyngeal carcinoma using failure mode and effects analysis (FMEA) and fault tree analysis (FTA), and to validate the effectiveness of the quality management measures.

Methods

A flowchart was established for the AIO of nasopharyngeal carcinoma. FMEA analysis was conducted based on the flowchart, and quantitative assessments of each failure mode (FM) were performed to obtain $O$ (occurrence), $S$ (severity), and $D$ (Detectability). Weighted $O_{wi}$, $S_{wi}$, and $D_{wi}$ were obtained using the similarity aggregation method (SAM), and the final risk priority number (RPN) was calculated by multiplying these values. The FMs were then evaluated into two groups based on whether quality management (QM) measures were implemented, and sorted by the RPN. Finally, FTA analysis was conducted on the highest-risk FMs identified through ranking.

Results

A flowchart of AIO for nasopharyngeal carcinoma was established, consisting of 5 main steps and 28 sub-steps. After FMEA analysis, 86 FMs were identified. In the group without implementing QM measures (QM-free group), the RPN of FMs ranged from 13.5 to 186.2, with a threshold of 94.6 for the top 20% RPN scores, resulting in 17 high-risk FMs. Additionally, 21 FMs had $S_{wi}\ge 8$, with a cumulative total of 25 high-risk FMs after removing duplicates. In the group that implemented QM measures (QM group), the RPN of FMs ranged from 3.0 to 46.7, showing an overall decrease compared to the QM-free group. There was a statistically significant difference in RPN between the QM-free (55.80 ± 38.40) and QM (16.17 ± 10.99) groups (p < 0.001), validating the effectiveness of the QM measures. Finally, FTA analysis was performed on the highest-risk step identified in the QM-free group with the highest RPN.

Conclusion

The improved FMEA and FTA analysis methods are practical and operational, allowing for a comprehensive analysis of potential failures and risks in the AIO for nasopharyngeal carcinoma. They can effectively assist in establishing and evaluating QM standards for AIO of nasopharyngeal carcinoma. Moreover, the analytical methods and QM measures of this study can be effectively applied to AIO for tumors in other sites.