Apparatus dependence of normal brain tissue dose in stereotactic radiosurgery for multiple brain metastases
Bibliographic record
Abstract
OBJECT: Technical improvements in commercially available radiosurgery platforms have made it practical to treat a large number of intracranial targets. The goal of this study was to investigate whether the dose to normal brain when planning radiosurgery to multiple targets is apparatus dependent. METHODS: The authors selected a single case involving a patient with 12 metastatic lesions widely distributed throughout the brain as visualized on contrast-enhanced CT. Target volumes and critical normal structures were delineated with Leksell Gamma Knife Perfexion software. The imaging studies including the delineated contours were digitally exported into the CyberKnife and Novalis multileaf collimator-based planning systems for treatment planning using identical target dose goals and dose-volume constraints. Subsets of target combinations (3, 6, 9, or 12 targets) were planned separately to investigate the relationship of number of targets and radiosurgery platform to the dose to normal brain. RESULTS: Despite similar target dose coverage and dose to normal structures, the dose to normal brain was strongly apparatus dependent. A nonlinear increase in dose to normal brain volumes with increasing number of targets was also noted. CONCLUSIONS: The dose delivered to normal brain is strongly dependent on the radiosurgery platform. How general this conclusion is and whether apparatus-dependent differences are related to differences in hardware design or differences in dose-planning algorithms deserve further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".