Integration of Magnetoencephalography-Generated Functional Brain Maps into Dose Planning during Arteriovenous Malformation Radiosurgery
Bibliographic record
Abstract
BACKGROUND: Magnetoencephalography (MEG) can delineate critical regions of the cortex and facilitate conformal stereotactic radiosurgery (SRS) dose planning. Despite the substantial role of Gamma Knife® SRS in arteriovenous malformation (AVM) management, MEG-generated maps of critical regions have never been utilized to improve dose planning. PURPOSE: To assess the value of integrating functional brain mapping using MEG with dose planning during treatment of brain AVMs with SRS. METHODS: This case series encompassed 5 patients with motor region AVMs. Noninvasive eloquent cortex mapping was achieved using a whole-head 306-channel Neuromag® Vectorview MEG System 5-10 days before SRS. On the day of SRS, the functional brain maps were integrated onto the intraoperative dose planning magnetic resonance imaging for Leksell GammaPlan® version 10. The median AVM volume treated was 12.7 cm(3), and 18 Gy was the median margin dose. RESULTS: Functional image integration of MEG improved the recognition of critical brain structures adjacent to the AVM. This facilitated anatomical planning designed to reduce the dose to adjacent critical structures while maintaining a therapeutic dose to the AVM target. The 5 patients had no adverse radiation effects during the follow-up. CONCLUSION: Coregistration of MEG data improves the accuracy and dose sparing needed for optimal planning during Gamma Knife SRS.
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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 | 0.003 |
| 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) | 0.000 | 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".