ROC analysis in patient specific quality assurance
Bibliographic record
Abstract
PURPOSE: This work investigates the use of receiver operating characteristic (ROC) methods in patient specific IMRT quality assurance (QA) in order to determine unbiased methods to set threshold criteria for γ-distance to agreement measurements. METHODS: A group of 17 prostate plans was delivered as planned while a second group of 17 prostate plans was modified with the introduction of random multileaf collimator (MLC) position errors that are normally distributed with σ ≈ ± 0.5, ± 1.0, ± 2.0, and ± 3.0 mm (a total of 68 modified plans were created). All plans were evaluated using five different γ-criteria. ROC methodology was applied by quantifying the fraction of modified plans reported as "fail" and unmodified plans reported as "pass." RESULTS: γ-based criteria were able to attain nearly 100% sensitivity/specificity in the detection of large random errors (σ > 3 mm). Sensitivity and specificity decrease rapidly for all γ-criteria as the size of error to be detected decreases below 2 mm. Predictive power is null with all criteria used in the detection of small MLC errors (σ < 0.5 mm). Optimal threshold values were established by determining which criteria maximized sensitivity and specificity. For 3%/3 mm γ-criteria, optimal threshold values range from 92% to 99%, whereas for 2%/2 mm, the range was from 77% to 94%. CONCLUSIONS: The optimal threshold values that were determined represent a maximized test sensitivity and specificity and are not subject to any user bias. When applied to the datasets that we studied, our results suggest the use of patient specific QA as a safety tool that can effectively prevent large errors (e.g., σ > 3 mm) as opposed to a tool to improve the quality of IMRT delivery.
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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 | 0.043 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".