TH‐C‐BRB‐10: Clinical Implication of the New Dosimetry Formalism in IMRT Quality Assurance
Bibliographic record
Abstract
Purpose: To apply the new dosimetry formalism [Med. Phys. 35, 5179 (2008)] to clinical IMRT quality assurance (QA). Methods: 20 different linear accelerator (Varian Clinac 21 EX)‐based clinical IMRT fields were transferred to the CT images of a 30×30×17 cm3 Solid Water phantom to create IMRT QA fields. The phantom position was adjusted for each QA field to place the detector or chamber at the lowest dose gradient region in a virtual PTV. The reference doses in the IMRT QA and 10×10 cm2 fields were measured using a PTW micro liquid ion chamber (microLion). Based on the new dosimetry formalism, the clinical correction factor of each IMRT QA field was measured for a calibrated Exradin A12 Farmer‐type chamber in a fully‐rotated delivery and a delivery at a single gantry angle, a collapsed delivery. For each QA field, the measured dose with the correction factor was compared with a calculated dose using Analytical Anisotropic Algorithm (AAA) or Monte Carlo (MC) methods. Results: The clinical correction factor deviated from unity by up to 2.4% and 3.7% in the fully‐rotated and collapsed deliveries, respectively, depending on the dose homogeneity at the Exradin A12 collecting volume. In the fully‐rotated delivery, the measured dose with the correction factor is different from the calculated dose to within 5% and 3% for the AAA and MC, respectively. In the collapsed delivery, the discrepancy between the measured and AAA‐calculated doses was to within 8%, while it was improved to within 3.5% compared with the MC‐calculated dose. When applying the clinical correction factor, the decrease of the measured and calculated dose discrepancy is more significant for an IMRT QA field having higher dose heterogeneity. Conclusions: This work proves that the suggested dosimetry technique is effective to improve the dosimetric consistency of clinical IMRT QA.
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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 | 0.005 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".