Room: Exhibit Hall
Purpose: Frameless stereotactic radiosurgery/fractionated radiotherapy (SRS/FSRT) using the Gamma Knife Iconáµ€á´¹ (GKI) treatment planning system with onboard cone-beam computed tomography (CBCT) imaging has introduced a new treatment workflow for frameless gamma knife radiosurgery. To assess the risk associated with frameless GKI treatment we implemented the recommendations of the AAPM Task Group 100 for failure modes and effects analysis (FMEA) at our institution.
Methods: A process tree for frameless GKI was mapped, identifying three focus areas specific to the frameless workflow (Simulation, Treatment Planning, and Treatment). Potential failure modes were identified for each of the focus areas excluding all processes related to framed Gamma Knife treatment. Failure modes will be assessed using the TG-100 FMEA grading system specific for radiotherapy outcomes and observations. Briefly, each member of the FMEA team evaluates likelihood of occurrence, severity, and detectability, to generate a risk priority number.
Results: Three subprocesses comprising forty-five steps were identified during the preliminary FMEA evaluation. Twenty-five steps displayed equivalency to the Gamma Knife Perfexion framed stereotactic radiosurgery previously published and were not included in the analysis. Potentially hazardous failure modes during simulation comprised improper mask fabrication, missing setup documentation, and CBCT registration errors. During treatment planning, skull definition based on the MRI segmentation, improper registration to the simulation CBCT and verification of dose were identified as failure modes. With regards to treatment, failure modes associated with patient registration and dose alignment as compared to the planning images (CBCT or MRI), intrafractional patient motion and collisions during treatment (sweat on cap, arms colliding etc.) were prominent.
Conclusion: FMEA analysis is a robust method to identify risk associated with frameless Gamma Knife SRS/FSRT. Implementation of this approach at our institution based on the recommendations of AAPM Task Group 100 will enable a better understanding of the frameless GKI and improve overall workflow.