SU‐F‐T‐502: FFF Beams, Jaw‐Tracking and Treatment Techniques: Out of Field Dose Considerations for Pediatric Radiation Therapy Delivery
Bibliographic record
Abstract
Purpose: To compare the extended dose profile delivered by 3DCRT and VMAT techniques for flattened and flattening‐filter‐free(FFF) photon beams (6X, 6XFFF,10XFFF), with and without jaw‐tracking (JT) on Varian TrueBeam linac. The goal is to determine which treatment technique/modality will minimize the peripheral photon dose exposure (and ultimately minimize the risk of second malignant neoplasms (SMN)) in pediatric patients. Methods: 3DCRT, VMAT, and jaw tracking VMAT (JTVMAT) plans with 6X, 6XFFF and 10XFFF x‐ray beams were created on a 30×60×22.5cm solid water phantom with a 551 cc PTV. The 3DCRT plans consisted of a 4FLD arrangement. The optimization objectives for the single‐arc VMAT plans was V95%Rx=98% to PTV and minimize dose to a 5cm diameter organ at risk (OAR). The OAR to PTV distance varied from 0–30cm along the long axis at 7.5cm depth. The dose to the center of the OAR was measured using a 0.6cc ion chamber. Results: Relative to the 6X flattened beam, the 10XFFF photon beam had the lowest dose in the penumbra and peripheral region (>15 cm) region by up to 20% and 40%, respectively for all modalities (3DCRT, VMAT, JTVMAT). The 6XFFF beams only showed a dose reduction in the peripheral region (by up to 20%). JT did not significantly affect the peripheral dose for all modalities and energies. Conclusion: Treating pediatric patients with a 10XFFF beam is the most effective way to reduce photon scatter dose in both the penumbra and peripheral regions. However, the neutron dose contribution resulting from the 10MV beam still needs to be considered. For all modalities, 6XFFF was the next effective method to reduce peripheral photon doses. 3DCRT beams had the lowest peripheral doses for all energies compared to VMAT and JTVMAT, however previous publications have shown that this comes at the expense of PTV conformity and OAR sparing.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".