Sci-Thur AM: Planning - 10: Improved dosimetric accuracy for patient specific quality assurance using a dual-detector measurement method for cyberknife output factors
Bibliographic record
Abstract
The measurement of output factors for small fields is challenging and can lead to large dose errors in patient treatments if corrections for detector size and scatter from high-Z material are not applied. Due to its high spatial resolution and near tissue equivalence, GAFCHROMIC® film potentially provides a correction free measure of output factors but it can be challenging to obtain high quality dosimetric results using this film. We propose minimizing errors in the clinical determination of small field output factors by employing diode measurements with Monte-Carlo generated corrections for small fields ≤10 mm diameter and using small volume ion chambers for apertures >10 mm diameter with independent validation using radiochromic film. We performed patient specific quality assurance (QA) measurements for 9 patients using GAFCHROMIC® film and an A16 small volume ion chamber in a head-shaped phantom, employing this hybrid dual detector method for relative output factor measurements within the Multiplan treatment planning system. Our results suggest that consistent output factors can be determined using this method with experimental verification using GAFCHROMIC® film dosimetry. For the patient specific QA using film, we achieve good dosimetric agreement (<2σ) of the measured and calculated average dose for pixels within the 80% isodose line. For patient specific QA using the micro-ion chamber, we get good agreement (<3%) for cone sizes greater than 5 mm. The differences observed for the 5 mm cone plans are consistent with a 1 mm radial setup uncertainty for patient positioning using the Cyberknife system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".