SU‐FF‐T‐135: Complex IMRT Plan Verification Using a Commercial MU Calculation Package
Bibliographic record
Abstract
Purpose: The general availability of IMRT treatments is constrained by the need to perform dosimetry measurements as part of the patient plan verification. While secondary IMRT MU calculators have been successfully used to validate some plans for a restricted number of sites, these calculators have suffered from unacceptably large uncertainties when applied to sites confined to the head and neck region. A strategy to reduce these calculational uncertainties has been developed which permits a greater use of IMRT MU calculators and reduces the need for dosimetric measurements, enabling a larger patient population to receive the benefits offered by IMRT treatments. Method and Materials: Segmental IMRT head and neck treatments were developed using the Pinnacle 6.2b inverse planning module. The IMRT treatment plans where then calculated on a CT image set of PMH IMRT phantom, and validation points corresponding to key target and avoidance regions where identified. The dosimetric data corresponding to these points was exported to RadCalc, a commercial IMRT MU calculator, and the calculations were compared to Pinnacle calculations and in‐phantom measurements. Results: A total of 10 clinical patient cases, each containing 7 to 9 gantry angles, were assessed. Agreement was assessed on basis of total dose delivered to the point of calculation. The measured and RadCalc calculated doses were found to agree within 3% at the high dose point and 5% at the low dose point in all cases, while the Pinnacle calculated dose was found to agree with the measured dose within 2.5% at the high dose point, with deviations as large as 13.5% observed at the low dose point. Conclusion: In‐phantom IMRT verification calculations of the total dose yields similar results as in‐phantom measurements. Consequently, a secondary MU calculator can be used to verify IMRT treatment plans and reduce the frequency of validation measurements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 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".