Dosimetric considerations for validation of a sequential IMRT process with a commercial treatment planning system
Bibliographic record
Abstract
Commercial multileaf collimator (MLC) systems can employ leaves with rounded ends. Treatment planning beam modelling should consider the effects of transmission through rounded leaf ends to provide accurate dosimetry for IMRT treatments delivered with segmented MLC. We determined that an MLC leaf gap reduction of 1.4 mm is required to obtain an agreement between calculated and measured profile 50% dose points. A head and neck dosimetry phantom, supplied by the Radiological Physics Center (RPC), was planned and irradiated as a necessary credentialing requirement for the RTOG H-0022 protocol. The agreement between the RPC TLD measurements and treatment planning calculations was within experimental error for the primary and secondary planning target volumes (PTVs); however, the calculated mean dose for the critical structure was approximately 9% lower than the RPC TLD measurements. RPC radiochromic film profile measurements also indicated significant discrepancies (>5%) with calculated values especially in the high dose gradient region in the vicinity of the critical structure. These results substantiate our own in-house phantom measurements, performed with the same IMRT fields as for the RPC phantom experiment, using Kodak EDR2 film to measure absolute dose. Our results indicate a maximum underestimate of calculated dose of 12% with no leaf gap reduction. The discrepancy between measured and calculated phantom values is reduced to +/- 5% when a leaf gap reduction of 1.4 mm is used. A further improvement in the accuracy of dose calculation is not possible without a more accurate modelling of the leaf end transmission by the planning system. In the absence of published dosimetric criteria for IMRT our results stress the need for stringent in-house dosimetric QA and validation for IMRT treatments. We found the dosimetric validation service provided by the RPC to be a valuable component of our IMRT validation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".