Analysis of treatment parameters for conformal shaped field stereotactic irradiation: comparison with non-coplanar arcs
Bibliographic record
Abstract
The change in configuration from circular convergent arcs to shaped static fields for stereotactic radiosurgery raises questions regarding comparability of dose distributions between the techniques. This study aims to quantify the optimization of planning parameters to achieve dose distributions minimizing dose to healthy tissue. Dose volume histograms were calculated and averaged from several patient treatments to measure dose homogeneity and healthy tissue irradiation inherent in variable PTV margins, the effect of increasing numbers of static shaped fields, the dose fall-off outside the PTV and of field placement. Our results show that adding a 2 mm margin around the target volume when defining field shapes maximizes dose coverage and homogeneity without substantially increasing the volume of healthy tissue irradiated to high dose levels. We demonstrate that 5-6 static fields may be optimal for typical lesions and that placement of these fields may not always play a major role in healthy tissue sparing. This work illustrates a systematic approach to conformal static field treatment plan optimization which relies on the prior determination of parameters such as optimum margin width to account for field penumbra. Complex irregularly shaped lesions still require careful patient-specific assessment of healthy tissue irradiation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".