An analysis of tolerance levels in IMRT quality assurance procedures
Bibliographic record
Abstract
Increased use of intensity modulated radiation therapy (IMRT) has resulted in increased efforts in patient quality assurance (QA). Software and detector systems intended to streamline the IMRT quality assurance process often report metrics, such as percent discrepancies between measured and computed doses, which can be compared to benchmark or threshold values. The purpose of this work is to examine the relationships between two different types of IMRT QA processes in order to define, or refine, appropriate tolerances values. For 115 IMRT plans delivered in a 3 month period, we examine the discrepancies between (a) the treatment planning system (TPS) and results from a commercial independent monitor unit (MU) calculation program; (b) TPS and results from a commercial diode-array measurement system; and (c) the independent MU calculation and the diode-array measurements. Statistical tests were performed to assess significance in the IMRT QA results for different disease site and machine models. There is no evidence that the average total dose discrepancy in the monitor unit calculation depends on the disease site. Second, the discrepancies in the two IMRT QA methods are independent: there is no evidence that a better--or worse--monitor unit validation result is related to a better--or worse--diode-array measurement result. Third, there is marginal benefit in repeating the independent MU calculation with a more suitable dose point, if the initial IMRT QA failed a certain tolerance. Based on these findings, the authors conclude at some acceptable tolerances based on disease site and IMRT QA method. Specifically, monitor unit validations are expected to have a total dose discrepancy of 3% overall, and 5% per beam, independent of disease site. Diode array measurements are expected to have a total absolute dose discrepancy of 3% overall, and 3% per beam, independent of disease site. The percent of pixels exceeding a 3% and 3 mm threshold in a gamma analysis should be greater than or equal to 95% for non-head and neck IMRT cases, and 88% for head and neck IMRT cases. The IMRT QA methodology described here is neither unique nor ubiquitous, and the ability to deliver a safe IMRT does not simply require IMRT QA tests to pass a given tolerance; however, the selection of a tolerance should be meaningful when assessing a complex plan. The methodology in defining appropriate tolerances, described in this article, is based on an interpretation of IMRT QA results from IMRT plans deemed safe to deliver.
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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 | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".