Poster - Thurs Eve-33: Initial implementation of a novel, measurement-based IMRT QA method
Bibliographic record
Abstract
Current measurement-based QA for IMRT typically involves a composite dose delivery to a phantom. However, this approach does not allow a direct dosimetric evaluation of the delivered treatment with respect to the patient anatomy. In this work we implement a novel, measurement-based IMRT QA method which provides an accurate reconstruction of the 3D-dose distribution in the patient model. The RPC Head&Neck phantom and two clinical prostate cases have been examined to date. Step & shoot plans were developed satisfying required dose metrics. A 2D-array of dose chambers (MatriXX, IBA Dosimetry) was mounted on a linear accelerator to capture delivered fluence. The measurement data were read directly by the control software (COMPASS, IBA Dosimetry), which also provides the ability to import patient plan data from the TPS. The COMPASS software also includes a dose calculation engine and head fluence model and requires beam commissioning procedures analogous to those of a TPS. Reconstructed doses and DVHs were compared to those calculated by the TPS. The beam model in the COMPASS software was able to predict percentage depth dose and X and Y profiles for MLC-defined apertures ranging from 1×1-20×20 cm∧2 to within 1.5% (depth-dose), 2.0% (in-field profiles), and 2.5% (out-of-field profiles). Reconstructed doses in the test plans were mostly within 2% of those in the TPS. DVHs compared to <1.2%. Reconstructed doses were overlaid on CT data and contoured structures, to enable a clinically useful understanding of discrepancies as compared to the TPS plan. Research partially sponsored by 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.014 | 0.006 |
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".