Details

Autor(en) / Beteiligte

• Takemori, Mihiro
• Nakamura, Satoshi
• Sofue, Toshimitsu
• Ito, Mikiko
• Goka, Tomonori
• Miura, Yuki
• Iijima, Kotaro
• Chiba, Takahito
• Nakayama, Hiroki
• Nakaichi, Tetsu
• Mikasa, Shohei
• Takano, Yuki
• Kon, Mitsuhiro
• Shuto, Yasunori
• Urago, Yuka
• Nishitani, Masato
• Kashihara, Tairo
• Takahashi, Kana
• Murakami, Naoya
• Nishio, Teiji
• Okamoto, Hiroyuki
• Chang, Weishan
• Igaki, Hiroshi

Titel

Failure modes and effects analysis study for accelerator‐based Boron Neutron Capture Therapy

Ist Teil von

• Medical physics (Lancaster), 2023-01, Vol.50 (1), p.424-439

Ort / Verlag

United States

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• Background Boron Neutron Capture Therapy (BNCT) has recently been used in clinical oncology thanks to recent developments of accelerator‐based BNCT systems. Although there are some specific processes for BNCT, they have not yet been discussed in detail. Purpose The aim of this study is to provide comprehensive data on the risk of accelerator‐based BNCT system to institutions planning to implement an accelerator‐based BNCT system. Methods In this study, failure mode and effects analysis (FMEA) was performed based on a treatment process map prepared for the accelerator‐based BNCT system. A multidisciplinary team consisting of a medical doctor (MD), a registered nurse (RN), two medical physicists (MP), and three radiologic technologists (RT) identified the failure modes (FMs). Occurrence (O), severity (S), and detectability (D) were scored on a scale of 10, respectively. For each failure mode (FM), risk priority number (RPN) was calculated by multiplying the values of O, S, and D, and it was then categorized as high risk, very high risk, and other. Additionally, FMs were statistically compared in terms of countermeasures, associated occupations, and whether or not they were the patient‐derived. Results The identified FMs for BNCT were 165 in which 30 and 17 FMs were classified as high risk and very high risk, respectively. Additionally, 71 FMs were accelerator‐based BNCT‐specific FMs in which 18 and 5 FMs were classified as high risk and very high risk, respectively. The FMs for which countermeasures were “Education” or “Confirmation” were statistically significantly higher for S than the others (p = 0.019). As the number of BNCT facilities is expected to increase, staff education is even more important. Comparing patient‐derived and other FMs, O tended to be higher in patient‐derived FMs. This could be because the non‐patient‐derived FMs included events that could be controlled by software, whereas the patient‐derived FMs were impossible to prevent and might also depend on the patient's condition. Alternatively, there were non‐patient‐derived FMs with higher D, which were difficult to detect mechanically and were classified as more than high risk. In O, significantly higher values (p = 0.096) were found for FMs from MD and RN associated with much patient intervention compared to FMs from MP and RT less patient intervention. Comparing conventional radiotherapy and accelerator‐based BNCT, although there were events with comparable risk in same FMs, there were also events with different risk in same FMs. They could be related to differences in the physical characteristics of the two modalities. Conclusions This study is the first report for conducting a risk analysis for BNCT using FMEA. Thus, this study provides comprehensive data needed for quality assurance/quality control (QA/QC) in the treatment process for facilities considering the implementation of accelerator‐based BNCT in the future. Because many BNCT‐specific risks were discussed, it is important to understand the characteristics of BNCT and to take adequate measures in advance. If the effects of all FMs and countermeasures are discussed by multidisciplinary team, it will be possible to take countermeasures against individual FMs from many perspectives and provide BNCT more safely and effectively.