Poster - Thur Eve - 64: Preliminary investigation of arc configurations for optimal sparing of normal tissue in hypofractionated stereotactic radiotherapy (HF-SRT) of multiple brain metastases using a 5mm interdigitating micro-multileaf collimator
Bibliographic record
Abstract
Purpose: To evaluate normal tissue sparing in intra-cranial HF-SRT, comparing various arc configurations with the Synergy Beam Modulator (SynBM) and Agility linacs, the latter incorporating leaf interdigitation and backup jaws. Methods: Five patients with multiple brain metastases (BMs), (5 BMs (n=2), 3 BMs (n=3)) treated with HF-SRT using 25 Gy (n=2) or 30 Gy (n=3) in 5 fractions, were investigated. Clinical treatment plans used the SynBM. Each patient was retrospectively re-planned on Agility, employing three planning strategies: (A) one isocenter and dedicated arc for each BM; (B) a single isocenter, centrally placed with respect to BMs; (C) the isocenter and arc configuration used in the SynBM plan, where closely spaced (<5cm) BMs used a dedicated isocenter and arcs. Agility plans were normalized for PTV coverage and heterogeneity. Results and Conclusion: Strategy A obtained the greatest improvements over the SynBM plan, where the maximum OAR dose, and mean dose to normal brain (averaged for all patients) were reduced by 55cGy and 25cGy, respectively. Strategy B was limited by having a single isocenter, hence less jaw shielding and increased MLC leakage. The maximum OAR dose was reduced by 13cGy, however mean dose to normal brain increased by 84cGy. Strategy C reduced the maximum OAR dose and mean dose to normal brain by 32cGy and 9cGy, respectively. The results from this study indicate that, for intra-cranial HF-SRT of multiple BMs, Agility plans are equal or better than SynBM plans. Further planning is needed to investigate dose sparing using Strategy A and the SynBM.
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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 | 0.000 | 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) | 0.002 | 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".