Shot sequencing based on biological equivalent dose considerations for multiple isocenter Gamma Knife radiosurgery
Bibliographic record
Abstract
Rapid delivery of multiple shots or isocenters is one of the hallmarks of Gamma Knife radiosurgery. In this study, we investigated whether the temporal order of shots delivered with Gamma Knife Perfexion would significantly influence the biological equivalent dose for complex multi-isocenter treatments. Twenty single-target cases were selected for analysis. For each case, 3D dose matrices of individual shots were extracted and single-fraction equivalent uniform dose (sEUD) values were determined for all possible shot delivery sequences, corresponding to different patterns of temporal dose delivery within the target. We found significant variations in the sEUD values among these sequences exceeding 15% for certain cases. However, the sequences for the actual treatment delivery were found to agree (<3%) and to correlate (R² = 0.98) excellently with the sequences yielding the maximum sEUD values for all studied cases. This result is applicable for both fast and slow growing tumors with α/β values of 2 to 20 according to the linear-quadratic model. In conclusion, despite large potential variations in different shot sequences for multi-isocenter Gamma Knife treatments, current clinical delivery sequences exhibited consistent biological target dosing that approached that maximally achievable for all studied cases.
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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 | 0.004 |
| 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) | 0.001 | 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".