Risk analysis of electronic intraoperative radiation therapy for breast cancer

Failure modes and effects analysis for surface-guided DIBH breast radiotherapy

Despite breast cancer prevalence and widespread adoption of deep inspiration breath‐hold (DIBH) radiation techniques, few data exist on the error risks related to using surface‐guided (SG) DIBH during breast radiation therapy (RT). Due to the increasingly technical nature of these methods and being a paradigm shift from traditional breast setups/treatments, the associated risk for error is high. Failure modes and effects analysis (FMEA) has been used in identifying risky RT processes yet is time‐consuming to perform. A subset of RT staff and a hospital patient‐safety representative performed FMEA to study SG‐DIBH RT processes. After this group (cohort 1) analyzed these processes, additional scoring data were acquired from RT staff uninvolved in the original FMEA (cohort 2). Cohort 2 received abbreviated FMEA training while using the same process maps that cohort 1 had created, which was done with the goal of validating our results and exploring the feasibility of expedited FMEA training and efficient implementation elsewhere. An extensive review of the SG‐DIBH RT process revealed 57 failure modes in 16 distinct steps. Risks deemed to have the highest priority, large risk priority number (RPN), and severity were addressed with policy changes, checklists, and standardization; of these, most were linked with operator error via manual inputs and verification. Reproducibility results showed that 5% of the average RPN between cohorts 1 and 2 was statistically different. Unexpected associations were noted between RPN and RT staff role; 12% of the physicist and therapist average scores were statistically different. Different levels of FMEA training yielded similar scoring within one RT department, suggesting a time‐savings can be achieved with abbreviated training. Scores between professions, however, yielded significant differences suggesting the importance of involving staff across disciplines.

Benchmarking failure mode and effects analysis of electronic brachytherapy with data from incident learning systems

PURPOSE

Failure modes and effects analysis (FMEA) is a prospective risk assessment tool for identifying failure modes in equipment or processes and informing the design of quality control systems. This work aims to benchmark the performance of FMEAs for electronic brachytherapy (eBT) of the skin and for breast by comparing predicted versus actual failure modes reported in multiple incident learning systems (ILS).

METHODS AND MATERIALS

Two public and our institution's internal ILS were queried for Xoft Axxent eBT-related events over 9 years. The failure modes and Risk Priority Numbers (RPNs) were taken from FMEAs previously performed for Xoft eBT of nonmelanoma skin cancer and breast intraoperative radiation therapy (IORT). For each event, the treatment site and primary failure mode was compared with the failure modes and RPNs from that site's FMEA.

RESULTS

49 events involving Xoft eBT were identified. Thirty-one (63.3%) involved breast IORT, and 18 (36.7%) involved the skin. Three events could not be linked to an FMEA failure mode. In 87.7% of events, the primary failure mode ranked in the FMEA top 10 by RPNs. In 83.3% of skin events, the failure modes ranked in the top 10 by RPN or severity. In 90.3% of IORT events, the failure modes ranked within the top 10 by RPN or severity.

CONCLUSIONS

Evaluating FMEA failure modes against ILS data demonstrates that FMEA is effective at predicting failure modes but can be dependent on user experience. ILS data can improve FMEA by identifying potential failure modes and suggesting realistic occurrence, detectability, and severity values.

A failure modes and effects analysis quality management framework for image-guided small animal irradiators: A change in paradigm for radiation biology

PURPOSE

Image-guided small animal irradiators (IGSAI) are increasingly being adopted in radiation biology research. These animal irradiators, designed to deliver radiation with submillimeter accuracy, exhibit complexity similar to that of clinical radiation delivery systems, including image guidance, robotic stage motion, and treatment planning systems. However, physics expertise and resources are scarcer in radiation biology, which makes implementation of conventional prescriptive QA infeasible. In this study, we apply the failure modes and effect analysis (FMEA) popularized by the AAPM task group 100 (TG-100) report to IGSAI and radiation biological research.

METHODS

Radiation biological research requires a change in paradigm where small errors to large populations of animals are more severe than grievous errors that only affect individuals. To this end, we created a new adverse effects severity table adapted to radiation biology research based on the original AAPM TG-100 severity table. We also produced a process tree which outlines the main components of radiation biology studies performed on an IGSAI, adapted from the original clinical IMRT process tree from TG-100. Using this process tree, we created and distributed a preliminary survey to eight expert IGSAI operators in four institutions. Operators rated proposed failure modes for occurrence, severity, and lack of detectability, and were invited to share their own experienced failure modes. Risk probability numbers (RPN) were calculated and used to identify the failure modes which most urgently require intervention.

RESULTS

Surveyed operators indicated a number of high (RPN >125) failure modes specific to small animal irradiators. Errors due to equipment breakdown, such as loss of anesthesia or thermal control, received relatively low RPN (12-48) while errors related to the delivery of radiation dose received relatively high RPN (72-360). Errors identified could either be improved by manufacturer intervention (e.g., electronic interlocks for filter/collimator) or physics oversight (errors related to tube calibration or treatment planning system commissioning). Operators identified a number of failure modes including collision between the collimator and the stage, misalignment between imaging and treatment isocenter, inaccurate robotic stage homing/translation, and incorrect SSD applied to hand calculations. These were all relatively highly rated (90-192), indicating a possible bias in operators towards reporting high RPN failure modes.

CONCLUSIONS

The first FMEA specific to radiation biology research was applied to image-guided small animal irradiators following the TG-100 methodology. A new adverse effects severity table and a process tree recognizing the need for a new paradigm were produced, which will be of great use to future investigators wishing to pursue FMEA in radiation biology research. Future work will focus on expanding scope of user surveys to users of all commercial IGSAI and collaborating with manufacturers to increase the breadth of surveyed expert operators.