Po‐Poster ‐ 11: Interface dosimetry around air cavities for cobalt‐60, 6MV and 15MV beams using EGSnrc/DOSXYZnrc Monte‐Carlo simulation
Bibliographic record
Abstract
Underdosing of the treatment target in radiation therapy can occur when tumors are situated near natural air cavities due to charged particle disequilibrium around air‐tissue interfaces. These effects increase with energy and decrease with radiation field size. Significant underdosing may occur with small field sizes, which may be critical for IMRT and tomotherapy techniques, which employ radiation beamlets of varying intensities to deliver conformal radiation therapy. Cobalt‐60 based tomotherapy has been suggested as a clinically and commercially viable alternative to 6MV linac based approaches. The work presented here is undertaken to explore potential reduction in interface underdosing with Co‐60 based tomotherapy due to lower beam energy. The Monte‐Carlo simulations using EGSnrc/DOSXYZnrc code were performed for single, parallel opposed and four beams of Co‐60 γ‐rays, and 6MV and 15MV x‐rays. The radiation fields were varied in size from small beamlets of 2×2cm2 to large fields up to 9×9cm2. The doses were scored near the air‐water interfaces for the air cavity of sizes 1×1×1cm3, 3×3×3cm3 and 5×5×5cm3 centered within a 20×20×20cm3 water phantom in 2×2×2mm3 voxels and compared with doses in homogeneous water phantom. In all situations, no significant interface underdosing is observed for 1×1×1cm3 cavity or where radiation beam is comparable or larger than the cavity size. The interface underdosing decreases with an increase in the numbers and size of the radiation beams, and increases with energy. Significant interface underdosing is observed for small beams. Co‐60 simulations show significantly less underdosing near the interfaces than that with 6MV and 15MV beams.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".