Background and Purpose The clinical introduction of on-table adaptive radiotherapy with Magnetic Resonance (MR)-guided linear accelerators (Linacs) yields new challenges and potential risks. Since the adapted plan is created within a highly interdisciplinary workflow with the patient in treatment position, time pressure or erroneous communication may lead to various possibly hazardous situations. To identify risks and implement a safe workflow, a proactive risk analysis has been conducted. Materials and Methods A process failure mode, effects and criticality analysis (P-FMECA) was performed within a group of radiation therapy technologists, physicians and physicists together with an external moderator. The workflow for on-table adaptive MR-guided treatments was defined and for each step potentially hazardous situations were identified. The risks were evaluated within the team in order to homogenize risk assessment. The team elaborated and discussed possible mitigation strategies and carried out their implementation. Results In total, 89 risks were identified for the entire MR-guided online adaptive workflow. After mitigation, all risks could be minimized to an acceptable level. Overall, the need for a standardized workflow, clear-defined protocols together with the need for checklists to ensure protocol adherence were identified among the most important mitigation measures. Moreover, additional quality assurance processes and automated plan checks were developed. Conclusions Despite additional workload and beyond the fulfilment of legal requirements, execution of the P-FMECA within an interdisciplinary team helped all involved occupational groups to develop and foster an open culture of safety and to ensure a consensus for an efficient and safe online adaptive radiotherapy workflow.

Online adaption of treatment plans on a magnetic resonance (MR)‐Linac enables the daily creation of new (adapted) treatment plans using current anatomical information of the patient as seen on MR images. Plan quality assurance (QA) relies on a secondary dose calculation (SDC) that is required because a pretreatment measurement is impossible during the adaptive workflow. However, failure mode and effect analysis of the adaptive planning process shows a large number of error sources, and not all of them are covered by SDC. As the complex multidisciplinary adaption process takes place under time pressure, additional software solutions for pretreatment per‐fraction QA need to be used. It is essential to double‐check SDC input to ensure a safe treatment delivery. Here, we present an automated treatment plan check tool for adaptive radiotherapy (APART) at a 0.35 T MR‐Linac. It is designed to complement the manufacturer‐provided adaptive QA tool comprising SDC. Checks performed by APART include contour analysis, electron density map examinations, and fluence modulation complexity controls. For nine of 362 adapted fractions (2.5%), irregularities regarding missing slices in target volumes and organs at risks as well as in margin expansion of target volumes have been found. This demonstrates that mistakes occur and can be detected by additional QA measures, especially contour analysis. Therefore, it is recommended to implement further QA tools additional to what the manufacturer provides to facilitate an informed decision about the quality of the treatment plan.

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