Performance assessment of a 2D array of plastic scintillation detectors for IMRT quality assurance
Bibliographic record
Abstract
The purposes of this work are to assess the performance of a 2D plastic scintillation detectors array prototype for quality assurance in intensity-modulated radiation therapy (IMRT) and to determine its sensitivity and specificity to positioning errors of one multileaf collimator (MLC) leaf and one MLC leaf bank by applying the principles of signal detection theory. Ten treatment plans (step-and-shoot delivery) and one volumetric modulated arc therapy plan were measured and compared to calculations from two treatment-planning systems (TPSs) and to radiochromic films. The averages gamma passing rates per beam found for the step-and-shoot plans were 95.8% for the criteria (3%, 2 mm), 97.8% for the criteria (4%, 2 mm), and 98.1% for the criteria (3%, 3 mm) when measurements were compared to TPS calculations. The receiver operating characteristic curves for the one leaf errors and one leaf bank errors were determined from simulations (theoretical upper limits) and measurements. This work concludes that arrays of plastic scintillation detectors could be used for IMRT quality assurance in clinics. The use of signal detection theory could improve the quality of dosimetric verifications in radiation therapy by providing optimal discrimination criteria for the detection of different classes of errors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".