Monte Carlo calculation of dose distributions in oligometastatic patients planned for spine stereotactic ablative radiotherapy
Bibliographic record
Abstract
Dosimetric consequences of plans optimized using the analytical anisotropic algorithm (AAA) implemented in the Varian Eclipse treatment planning system for spine stereotactic body radiotherapy were evaluated by re-calculating with BEAMnrc/DOSXYZnrc Monte Carlo. Six patients with spinal vertebral metastases were planned using volumetric modulated arc therapy. The planning goal was to cover at least 80% of the planning target volume with a prescribed dose of 35 Gy in five fractions. Tissue heterogeneity-corrected AAA dose distributions for the planning target volume and spinal canal planning organ-at-risk volume were compared against those obtained from Monte Carlo. The results showed that the AAA overestimated planning target volume coverage with the prescribed dose by up to 13.5% (mean 8.3% +/- 3.2%) when compared to Monte Carlo simulations. Maximum dose to spinal canal planning organ-at-risk volume calculated with Monte Carlo was consistently smaller than calculated with the treatment planning system and remained under spinal cord dose tolerance. Differences in dose distribution appear to be related to the dosimetric effects of accounting for body composition in Monte Carlo simulations. In contrast, the treatment planning system assumes that all tissues are water-equivalent in their composition and only differ in their electron density.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".