SU‐GG‐T‐200: Comparison of a Novel Transmission Detector to a Standard Measurement Technique for Patient IMRT Quality Assurance
Bibliographic record
Abstract
Purpose: A new measurement‐based, pre‐treatment IMRT composite quality assurance (QA) procedure was compared to a standard QA method. The standard QA method consisted of delivering the treatment to a cuboidal IMRT phantom containing dose measuring media. This standard measurement employed Gaf‐chromic film sheets for relative dosimetry and an ion chamber for absolute dosimetry. The new method examined here used a novel transmission detector measurement (IBA Dosimetry) designed for in vivo patient dosimetry. However, before in vivo application, we compare to our standard QA method, which is the focus of this work. Method and Materials: Treatment plans for ten previously‐treated, head and neck IMRT patients were used. All treatment plans were QA'd with the standard method at the treatment beam angles. Measurements with the transmission detector (an array of 1600 IC's) were also performed at the treatment beam angles. The COMPASS software (IBA Dosimetry) converts these 2D‐array measurements to incident fluence, and then forward calculates dose in a patient model (ie. CT data). In this work the patient model was a cuboidal shaped solid‐water IMRT phantom (MedTec). Absolute doses were compared using isocentre location for the COMPASS/planning system, and a low dose gradient point location for the IC/planning system comparison (near isocentre, but not necessarily due to alignment needs during measurement). Results: On average, the standard QA method results for absolute dosimetry were different by 1.4±1.8% compared to the planning system (measured above planning system). On average the transmission detector based QA method results for absolute dosimetry were different by 0.3±1.6% compared to the planning system (measured above planning system). Conclusion: QA results using the COMPASS software with the transmission detector were comparable to the standard QA method. Conflict of Interest: CancerCare Manitoba has a collaborative research agreement with IBA Dosimetry, although there is not direct financial support.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".