Poster — Thur Eve — 71: Improved dose accuracy for plan checking IMRT breast plans
Bibliographic record
Abstract
Dose verification as part of plan checking is a critical component of high quality patient care. IMSure QA is a software platform used at the BC Cancer Agency that facilitates dose verification for both conformal and IMRT plans. We have recently initiated treating breast tangents using IMRT at the Fraser Valley Centre and noted increased dose discrepancies (mean difference of -3%) between Eclipse and IMSure's QA module. We identified two potential sources of error: air flash and tissue heterogeneity. We extend our generated fluences 3cm past the breast contour and into air to account for breathing, set-up uncertainties and swelling. IMSure does not account for the fluence in air or air flash. We present an air-flash-correction factor based on the ratios of TMRs and Phantom Scatter Factors which use the field sizes of fields with and without the air flash. In addition, we present a method to improve the heterogeneity correction used by IMSure to better match that used by AAA. Effectively we remove the IMSure's inherent heterogeneity correction and manually apply a AAA-based heterogeneity-correction factor. We evaluated our correction factors on a sample of 8 patients (32 fields) using ANOVA methods to determine which dose corrections most accurately reproduce Eclipse's values. We found the air-flash correction coupled with IMSure's inherent-heterogeneity correction has the best dose accuracy (mean difference improved from -3% to 0.3%). The AAA-heterogeneity correction alone also improved the accuracy (mean difference improved from -3% to - 1.5%), which is acceptable for plan checking purposes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".