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Updated: Jun 24, 2025

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
Published on: June 7, 2015
Clinical effects of re-evaluating a lung SBRT failure mode and effects analysis in a radiotherapy department
Sergi Benavente1, Alexandra Giraldo2, Alejandro Seoane3
1Department of Radiation Oncology, Vall d'Hebron University Hospital Campus, Barcelona, Spain. sergi.benavente@vallhebron.cat.
Purpose:
The increasing complexity of radiation treatments can hinder its clinical success. This study aimed to better understand evolving risks by re-evaluating a Failure Mode and Effects Analysis (FMEA) in lung SBRT.
Methods:
An experienced multidisciplinary team conducted an FMEA and made a reassessment 3 years later. A process map was developed with potential failure modes (FMs) identified. High-risk FMs and their possible causes and corrective actions were determined. The initial FMEA analysis was compared to gain a deeper perspective.
Results:
We identified 232 FMs. The high-risk processes were plan approval, target contouring, and patient evaluation. The corrective measures were based on stricter standardization of plan approval, pre-planning peer review, and a supporting pretreatment checklist, which substantially reduced the risk priority number in the revised FMEA. In the FMEA reassessment, we observed that the increased complexity and number of patients receiving lung SBRT conditioned a more substantial presence of human factors and communication errors as causal conditions and a potential wrong dose as a final effect.
Conclusions:
Conducting a lung SBRT FMEA analysis has identified high-risk conditions that have been effectively mitigated in an FMEA reanalysis. Plan approval has shown to be a weak link in the process. The increasing complexity of treatments and patient numbers have shifted causal factors toward human failure and communication errors. The potential of a wrong dose as a final effect augments in this scenario. We propose that digital and artificial intelligence options are needed to mitigate potential errors in high-complexity and high-risk RT scenarios.
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