Improving intensity‐modulated radiation therapy using the anatomic beam orientation optimization algorithm
Bibliographic record
Abstract
A novel, anatomic beam orientation optimization (A-BOO) algorithm is proposed to significantly improve conventional intensity-modulated radiation therapy (IMRT). The A-BOO algorithm vectorially analyses polygonal surface mesh data of contoured patient anatomy. Five optimal (5-opt) deliverable beam orientations are selected based on (1) tangential orientation bisecting the target and adjacent organ's-at-risk (OARs) to produce precipitous dose gradients between them and (2) parallel incidence with polygon features of the target volume to facilitate conformal coverage. The 5-opt plans were compared to standard five, seven, and nine 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 noncoplanar 5-opt plan reduced the right kidney V 20 Gy by 32.2%, 23.2%, and 20.6% compared to plans with five, seven, and nine equiangular-spaced beams. In the prostate case, the coplanar 5-opt plan produced similar rectal sparing as the 7-equi and 9-equi plans with a reduction of the V 75, V 70, V 65, and V 60 Gy of 2.4%, 5.3%, 7.0%, and 9.5% compared to the 5-equi plan. In the stage-III and IV oropharyngeal cases, the noncoplanar 5-opt plan substantially reduced the V 30 Gy and mean dose to the contralateral parotid compared to plans with five, seven, and nine equiangular-spaced beams: (stage-III) 7.1%, 5.2%, 6.8%, and 5.1, 3.5, 3.7 Gy and (stage-IV) 10.2%, 10.2%, 9.8% and 7.0, 7.1, 7.2 Gy. The geometry-based A-BOO algorithm has been demonstrated to be robust for application to a variety of IMRT treatment sites. Beam orientations producing significant improvements in OAR sparing over conventional IMRT can be automatically produced in minutes compared to hours with existing dose-based beam orientation optimization methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".