Dosimetric analysis of beam number variations in IMRT for head-and-neck, breast, and pelvic cancers using Halcyon
Bibliographic record
Abstract
This study investigated the dosimetric effects of varying beam numbers (9, 11, and 13) in intensity-modulated radiation therapy (IMRT) using the Halcyon linear accelerator at ShingMark University Hospital. IMRT plans for 27 patients with head-and-neck, breast, and pelvic cancers were evaluated according to the RTOG 0615 protocol, focusing on Planning Target Volume (PTV) dose coverage, uniformity, maximum and minimum doses, and Organ-at-Risk (OAR) dose constraints. For breast cancer, the 11-beam plan improved PTV coverage (V50Gy: 97.9 ± 0.5 %) and reduced maximum dose compared to 9- and 13-beam plans, while the 13-beam plan lowered heart mean dose by up to 10.5 % in left breast cases. In pelvic cases, the 13-beam plan enhanced OAR sparing, reducing bowel V35Gy by 10.5 % compared to the 9-beam plan, with minimal PTV coverage differences (<0.2 %). For head-and-neck cancer, the 13-beam plan improved PTV D95 % and spared critical structures like the brainstem , though it increased doses to the chiasm and cochlea. Treatment time and Monitor Units rose with more beams, particularly for head-and-neck and pelvic cases, but remained minimal for breast cases. These findings suggest tailoring beam numbers to cancer type and clinical priorities, balancing dosimetric benefits with treatment efficiency.
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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 | 0.002 |
| 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) | 0.001 | 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".