Monte Carlo investigation of collapsed versus rotated IMRT plan verification
Bibliographic record
Abstract
IMRT QA requires, among other tests, a time-consuming process of measuring the absorbed dose, at least to a point, in a high-dose, low-dose-gradient region. Some clinics use a technique of measuring this dose with all beams delivered at a single gantry angle (collapsed delivery), as opposed to the beams delivered at the planned gantry angle (rotated delivery). We examined, established, and optimized Monte Carlo simulations of the dosimetry for IMRT verification of treatment plans for these two different delivery modes (collapsed versus rotated). The results of the simulations were compared to the treatment planning system dose calculations for the two delivery modes, as well as to measurements taken. This was done in order to investigate the validity of the use of a collapsed delivery technique for IMRT QA. The BEAMnrc, DOSXYZnrc, and egs_chamber codes were utilized for the Monte Carlo simulations along with the MMCTP system. A number of different plan complexity metrics were also used in the analysis of the dose distributions in a bid to qualify why verification in a collapsed delivery may or may not be optimal for IMRT QA. Following the Alfonso et al. formalism, the kfclin,frefQclin,Q correction factor was calculated to correct the deviation of small fields from the reference conditions used for beam calibration. We report on the results obtained for a cohort of 20 patients. The plan complexity was investigated for each plan using the complexity metrics of homogeneity index, conformity index, modulation complexity score, and the fraction of beams from a particular plan that intersect the chamber when performing the QA. Rotated QA gives more consistent results than the collapsed QA technique. The kfclin,frefQclin,Qfactor deviates less from 1 for rotated QA than for collapsed QA. If the homogeneity index is less than 0.05 then the kfclin,frefQclin,Q factor does not deviate from unity by more than 1%. A value this low for the homogeneity index can only be obtained with the rotated QA technique.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".