ABSTRACT
Cone-beam computed tomography (CBCT)-based online adaptation is increasingly being introduced into many clinics. Upon implementation of a new treatment technique, a prospective risk analysis is required and enhances workflow safety. We conducted a risk analysis using Failure Mode and Effects Analysis (FMEA) upon the introduction of an online adaptive treatment programme (Wegener et al., Z Med Phys. 2022). A prospective risk analysis, lacking in-depth clinical experience with a treatment modality or treatment machine, relies on imagination and estimates of the occurrence of different failure modes. Therefore, we systematically documented all irregularities during the first year of online adaptation, namely all cases in which quality assurance detected undesired states potentially leading to negative consequences. Additionally, the quality of automatic contouring was evaluated. Based on those quantitative data, the risk analysis was updated by an interprofessional team. Furthermore, a hypothetical radiation therapist-only workflow during adaptive sessions was included in the prospective analysis, as opposed to the involvement of an interprofessional team performing each adaptive treatment. A total of 126 irregularities were recorded during the first year. During that time period, many of the previously anticipated failure modes (almost) occurred, indicating that the initial prospective risk analysis captured relevant failure modes. However, some scenarios were not anticipated, emphasizing the limits of a prospective risk analysis. This underscores the need for regular updates to the risk analysis. The most critical failure modes are presented together with possible mitigation strategies. It was further noted that almost half of the reported irregularities applied to the non-adaptive treatments on this treatment machine, primarily due to a manual plan import step implemented in the institution's workflow.
ABSTRACT
PURPOSE: The recently introduced Varian Ethos system allows adjusting radiotherapy treatment plans to anatomical changes on a daily basis. The system uses artificial intelligence to speed up the process of creating adapted plans, comes with its own software solutions and requires a substantially different workflow. A detailed analysis of possible risks of the associated workflow is presented. METHODS: A prospective risk analysis of the adaptive workflow with the Ethos system was performed using Failure Modes and Effects Analysis (FMEA). An interprofessional team collected possible adverse events and evaluated their severity as well as their chance of occurrence and detectability. Measures to reduce the risks were discussed. RESULTS: A total of 122 events were identified, and scored. Within the 20 events with the highest-ranked risks, the following were identified: Challenges due to the stand-alone software solution with very limited connectivity to the existing record and verify software and digital patient file, unfamiliarity with the new software and its limitations and the adaption process relying on results obtained by artificial intelligence. The risk analysis led to the implementation of additional quality assurance measures in the workflow. CONCLUSIONS: The thorough analysis of the risks associated with the new treatment technique was the basis for designing details of the workflow. The analysis also revealed challenges to be addressed by both, the vendor and customers. On the vendor side, this includes improving communication between their different software solutions. On the customer side, this especially includes establishing validation strategies to monitor the results of the black box adaption process making use of artificial intelligence.
ABSTRACT
BACKGROUND: To analyze the accuracy and inter-observer variability of image-guidance (IG) using 3D or 4D cone-beam CT (CBCT) technology in stereotactic body radiotherapy (SBRT) for lung tumors. MATERIALS AND METHODS: Twenty-one consecutive patients treated with image-guided SBRT for primary and secondary lung tumors were basis for this study. A respiration correlated 4D-CT and planning contours served as reference for all IG techniques. Three IG techniques were performed independently by three radiation oncologists (ROs) and three radiotherapy technicians (RTTs). Image-guidance using respiration correlated 4D-CBCT (IG-4D) with automatic registration of the planning 4D-CT and the verification 4D-CBCT was considered gold-standard. Results were compared with two IG techniques using 3D-CBCT: 1) manual registration of the planning internal target volume (ITV) contour and the motion blurred tumor in the 3D-CBCT (IG-ITV); 2) automatic registration of the planning reference CT image and the verification 3D-CBCT (IG-3D). Image quality of 3D-CBCT and 4D-CBCT images was scored on a scale of 1-3, with 1 being best and 3 being worst quality for visual verification of the IGRT results. RESULTS: Image quality was scored significantly worse for 3D-CBCT compared to 4D-CBCT: the worst score of 3 was given in 19 % and 7.1 % observations, respectively. Significant differences in target localization were observed between 4D-CBCT and 3D-CBCT based IG: compared to the reference of IG-4D, tumor positions differed by 1.9 mm ± 0.9 mm (3D vector) on average using IG-ITV and by 3.6 mm ± 3.2 mm using IG-3D; results of IG-ITV were significantly closer to the reference IG-4D compared to IG-3D. Differences between the 4D-CBCT and 3D-CBCT techniques increased significantly with larger motion amplitude of the tumor; analogously, differences increased with worse 3D-CBCT image quality scores. Inter-observer variability was largest in SI direction and was significantly larger in IG using 3D-CBCT compared to 4D-CBCT: 0.6 mm versus 1.5 mm (one standard deviation). Inter-observer variability was not different between the three ROs compared to the three RTTs. CONCLUSIONS: Respiration correlated 4D-CBCT improves the accuracy of image-guidance by more precise target localization in the presence of breathing induced target motion and by reduced inter-observer variability.
