Year: 2025
Type: article
Abstract: Background and Purpose The adapt‐to‐shape (ATS) workflow on the Unity MR‐Linac (Elekta AB, Stockholm, Sweden) allows for full replanning including recontouring and reoptimization 5 . Additional complexity to this workflow is added when the adaptation involves the use of MIM Maestro (MIM Software, Cleveland, OH) software in conjunction with Monaco (Elekta AB, Stockholm, Sweden). Given the interplay of various systems and the inherent complexity of the ATS workflow, a risk analysis would be instructive. Method Failure modes and effects analysis (FMEA) following Task Group 100 13 was completed to evaluate the ATS workflow. A multi‐disciplinary team was formed for this analysis. The team created a process map detailing the steps involved in ATS treating both the standard Monaco workflow and a workflow with the use of MIM software in parallel. From this, failure modes were identified, scored using three categories (likelihood of occurrence, severity, and detectability which multiplied create a risk priority number), and then mitigations for the top 20 th percentile of failure modes were found. Results Risk analysis found 264 failure modes in the ATS workflow. Of those, 82 were high‐ranking failure modes that ranked in the top 20 th percentile for risk priority number and severity scores. Although high‐ranking failure modes were identified in each step in the process, 62 of them were found in the contouring and planning steps, highlighting key differences from adapt‐to‐position (ATP), where the importance of these steps are minimized. Mitigations are suggested for all high‐ranking failure modes. Conclusion The flexibility of the ATS workflow, which enables reoptimization of the treatment plan, also introduces potential critical points where errors can occur. There are more opportunities for error in ATS that can create unintentionally negative dosimetric impact. FMEA can help mitigate these risks by identifying and addressing potential failure points in the ATS process.
DOI: 10.1002/acm2.70095
Authors: Jiayi Liang, Eric Aliotta, Neelam Tyagi, Paola Godoy Scripes, Nicolas Côté, Ergys Subashi, Qijie Huang, Lian Sun, Ching‐Yun Chan, Angela Ng, Theresa Wunner, Victoria Brennan, Kaveh Zakeri, James Mechalakos
Institution: Memorial Sloan Kettering Cancer Center
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $900
Funder: National Cancer Institute
Award: Cancer Center Support Grant
OpenAlex ID: W4409556940