A failure modes and effects analysis quality management framework for image‐guided small animal irradiators: A change in paradigm for radiation biology
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".