TH‐C‐12A‐10: Surface Dose Enhancement Using Novel Hybrid Electron and Photon Low‐Z Therapy Beams: Monte Carlo Simulation
Bibliographic record
Abstract
Purpose: The introduction of the TrueBeam linac platform provides access to an in‐air target assembly making it possible to apply novel treatments using multiple target designs. One such novel treatment uses multiple low‐Z targets to enhance surface dose replacing the use of synthetic tissue equivalent material (bolus). This treatment technique will decrease the common dosimetric and set up errors prevalent in using physical treatment accessories like bolus. The groundwork for a novel treatment beam used to enhance surface dose to within 80‐100% of the dose at dmax by utilizing low‐Z (Carbon) targets of various percent CSDA range thickness operated at 2.5–4 MeV used in conjunction with a clinical 6 MV beam is presented herein. Methods: A standard Monte Carlo model of a Varian Clinac accelerator was developed to manufacturers specifications. Simulations were performed using Be, C, AL, and C, as potential low‐Z targets, placed in the secondary target position. The results determined C to be the target material of choice. Simulations of 15, 30 and 60% CSDA range C beams were propagated through slab phantoms. The resulting PDDs were weighted and combined with a standard 6 MV treatment beam. Versions of the experimental targets were installed into a 2100C Clinac and the models were validated. Results: Carbon was shown to be the low‐Z material of choice for this project. Using combinations of 15, 30, 60% CSDA beams operated at 2.5 and 4 MeV in combination with a standard 6 MV treatment beam the surface dose was shown to be enhanced to within 80–100% the dose at dmax. Conclusion: The modeled low‐Z beams were successfully validated using machined versions of the targets. Water phantom measurements and slab phantom simulations show excellent correlation. Patient simulations are now underway to compare the use of bolus with the proposed novel beams. NSERC
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".