Application of repeat image analysis to radiation therapy imaging modalities as a quality improvement tool for image guided radiotherapy
Bibliographic record
Abstract
BACKGROUND: Repeat images contribute to excess patient dose and workflow inefficiencies and can be analyzed to identify potential areas for improvement within a program. Although routinely used in diagnostic imaging, repeat image analysis is not widely used in radiation therapy imaging, despite the role of imaging in the delivery of precise radiation treatments. PURPOSE: Repeat image analysis was performed for on-board cone beam CT imagers and CT simulators within a radiation therapy department. Both the rate of repeat images and the reasons for the repeat images were analyzed. METHODS: Data from nine conventional linear accelerators and three CT simulators were analyzed retrospectively over a 5-month period. Repeated images that were not part of the standard of care were considered repeat images. The repeat rate was expressed as the number of repeat scans as percentage of the total number of scans performed. The reasons for the repeats were collected and classified as either patient preparation, patient setup, patient motion, or machine error. These reasons were further classified into sub-categories. RESULTS: The overall repeat rate across the linear accelerators was 3.3%, with a maximum of 5% on any single machine. The repeat rate for the three CT simulators was 1.5%. The most common reasons for repeat images were patient preparation (incorrect bladder or rectal filling) and patient setup or positioning. Greater positioning challenges led to higher repeat rates on units that treat a large number of breast patients, palliative patients, or pediatric patients. CONCLUSIONS: Repeat image analysis can be applied within a radiation therapy department. Establishing baseline repeat rates and analyzing reasons for the repeat images can identify opportunities for improvements in terms of patient dose reduction and workflow efficiency for the program. Periodic repeat image analysis also permits monitoring the program for changes and for comparison against rates at other institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".