SU‐E‐T‐357: Error Detection with a 3D Patient Specific QA System
Bibliographic record
Abstract
Purpose: To determine if our in‐house patient specific QA process, which utilizes the COMPASS (v2.1, IBA‐Dosimetry) system, can detect delivery errors introduced in clinical IMRT plans. Methods: The COMPASS system consists of a 2D chamber array mounted in the gantry, an independent gantry angle sensor and associated software. Patient specific QA at our clinic involves the creation of two QA structures, one consisting of all clinical PTVs, the other generated from the 20% isodose line. The gamma agreement index (GAI) within the two QA structures is used to compare 3D COMPASS reconstructed dose with planned dose distributions. Ten clinical DMLC‐IMRT plans (5 prostate, 5 head & neck) were included in this study. Plans were modified by systematically shifting leaf pair positions so that that all nonzero leaf gaps were increased by 2.0 mm and 1.0 mm for the two sites, respectively. Verification plans were created for both the original and modified plans in order to quantify the dosimetric effect of the introduced errors via in‐phantom ion chamber measurements. Finally, plans were delivered to COMPASS and 3D dose distributions were reconstructed on both the phantom and patient CT datasets. Reconstructed doses on the patient CT were analyzed as per our QA procedure. Results: On average, measured ion chamber values for the modified plans deviated from the original planning doses by 3.1 ± 1.2% (Range: 1.0%–5.3%). For each of the six modified plans in which this metric exceeded 3%, GAI values for at least one of the QA structures were below 95%, which would be flagged by our QA process. Only one of the other modified and none of the original plans had GAI values <95%. Conclusions: Gamma analysis of patient specific QA with COMPASS can effectively detect dosimetrically significant delivery errors introduced by systematically increasing leaf gaps in clinical IMRT plans. CancerCare Manitoba has a research agreement with IBA‐Dosimetry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".