Optimization of photon beam energy in aperture‐based inverse planning
Bibliographic record
Abstract
Optimal choice of beam energy in radiation therapy is easy in many well-documented cases, but less obvious in some others. Low-energy beams may provide better conformity around the target than their high-energy counterparts due to reduced lateral scatter, but they also contribute to overdosage of peripheral normal tissue. Beam energy was added as an optimization parameter in an automatic aperture-based inverse planning system. We have investigated two sites (prostate and lung), representative of deep-seated and moderately deep-seated tumors. For each case and different numbers of beam incidences, four plans were optimized: 6 MV, 23 MV, and mixed energy plans with one or two energies per incidence. Each plan was scored with a dose-volume cost function. Cost function values, number of segments, monitor units, dose-volume parameters and isodose distributions were compared. For the prostate and lung cases, energy mixing improved plans in terms of cost function values, with a more important reduction for a small number of beam incidences. Use of high energy allows better peripheral tissue sparing, while keeping similar target coverage and sensitive structures avoidance. Low energy contribution to monitor units usually increased with the number of beam incidences. Thus, for deep-seated and moderately deep-seated tumors, energy optimization can produce interesting plans with less peripheral dose and monitor units than for low energy alone.
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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 | 0.001 |
| 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".