Sci‐Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques ‐ 02: Feasibility of using multileaf collimation for stereotactic radiosurgery of arteriovenous malformation
Bibliographic record
Abstract
SRS using linac and cones offers steep dose fall‐off but a tradeoff exists between conformality and treatment time, which depends on the number of isocentres. Purpose of this study is to quantify planning metrics between cones‐ and MLC‐based SRS for arteriovenous malformation(AVM). Seven AVM cases treated with cones were re‐planned with MLC on Pinnacle treatment planning system. Planning target volume(PTV) was created with 1mm uniform margin to the AVM to account for MLC positional variation. Clinically‐planned prescription dose(15–25Gy) was used. Four plans were generated per case:non‐coplanar VMAT(ncV), single‐arc VMAT(saV), non‐coplanar IMRT(ncI), non‐coplanar conformal(ncC). Plans were compared for conformity(CI), heterogeneity(HI) and gradient(GI) indices and brain doses. Estimated treatment times and monitor units(MU) were compared. Cone‐based plans required 2–6 isocentres. Though CI‐RTOG was similar for plans(median=0.98), CI‐Paddick was most favourable for ncV(median=0.86) and worst for cones(0.54). HI for MLC plans(median=1.19–1.27) were lower than cone‐based plans(1.43). GI was similar for all plans. For 2/7 ncC had brainstem maximum dose>16.7Gy and therefore were clinically unacceptable. Brain V12Gy,V10Gy,V2Gy were lowest in the cones plan. ncV brain V12Gy,V10Gy,V2Gy were lowest of all MLC‐based plans studied. Treatment MUs were similar for MLC‐based plans and up to 70% lower than clinically delivered plans. ncV showed best conformality in this study. Of the MLC‐based plans, ncV also showed lowest normal tissue dose with reasonable treatment time.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".