Poster - Wed Eve-49: Patient Specific IMRT QC Tolerance Criteria and the Detection of Systematic Errors in MLC Leaf Position
Bibliographic record
Abstract
Patient specific IMRT QC remains standard practice in most clinics. We have evaluated the feasibility of detecting systematic errors in MLC leaf position with IMRT QC based on diode and aSi area detectors. 12 head and neck (H&N) and 14 prostate IMRT fields were delivered using MLC files containing systematic errors (±1mm in 2 banks, ±0.5mm in 2 banks and 1mm in 1 bank of leaves). Planar dose maps were measured using both Mapcheck™ (Sun Nuclear Corp.) and the aS1000 EPID (Varian Medical Systems) and compared with maps produced with unperturbed leaves. Results were analyzed using several common criteria including absolute dose difference (AD), relative dose difference (RD), distance to agreement (DTA) and the gamma index (γ). Using Mapcheck™ and the change in percentage of passing points as a measure of sensitivity, the relative sensitivity of the criteria tested in descending order is 3% AD,3mm DTA; γ with 3% AD, 3mm DTA; 5% AD, 3mm DTA and 3% RD, 3mm DTA. This sequence applied to both H&N and prostate fields although the H&N fields, being more highly modulated, exhibited greater sensitivity to systematic MLC leaf offsets. The EPID study, which was software limited to the γ criterion, showed higher sensitivity with [2% AD/2mm DTA] γ criteria. Due to the distribution of passing rates in a population of IMRT fields we conclude that patient specific QC alone is not sufficient to identify potentially clinically significant systematic MLC offsets and must be supplemented with regular QC of the MLC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".