Sci‐Fri AM: YIS‐07: A new paradigm for improving IMRT: Selection of beam orientations by optimizing beam intersection volume
Bibliographic record
Abstract
A beam orientation optimization (BOO) algorithm based on optimizing beam intersection volume (BIV) components within an Organ-at-Risk (OAR) is proposed to improve conventional intensity-modulated radiation therapy (IMRT). A simulated annealing algorithm was employed to search for the optimal set of five beam orientations (5-opt) which simultaneously minimize the BIV components within an OAR. The 5-opt plans were compared to standard 5, 7, and 9 equiangular-spaced beam plans (5-equi, 7-equi, 9-equi) for: (1) gastric (2) Radiation Therapy Oncology Group (RTOG) P-0126 prostate and (3) RTOG H-0022 oropharyngeal (Stage-III, IV) cancer patients. In the gastric case, the coplanar 5-opt plan reduced the right kidney V 20 Gy by 41.1%, 32.1%, and 29.5% compared to the 5-equi, 7-equi, and 9-equi plans. In the prostate case, the coplanar 5-opt plan improved rectal sparing over all standard plans with a reduction of the V 75 Gy, V 70 Gy, V 65 Gy, and V 60 Gy of 3.9%, 6.2%, 8.1%, and 10.6% compared to the 5-equi plan. In both oropharyngeal cases, the non-coplanar 5-opt plan substantially reduced the V 30 Gy and mean dose to the contralateral parotid compared to the 5-equi, 7-equi, and 9-equi plans: (Stage-III) 8.9%, 7.0%, 8.6% and 4.1 Gy, 2.5 Gy, 2.7 Gy (Stage-IV) 11.2%, 11.2%, 10.8% and 7.8 Gy, 7.9 Gy, 8.0 Gy. In conclusion, the method of optimizing BIV to produce substantial improvements in OAR sparing over conventional IMRT has been demonstrated to be robust for application to a variety of IMRT treatment sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".