Investigation of two linear accelerator head designs for treating brain metastases with hypofractionated volumetric-modulated arc radiotherapy
Bibliographic record
Abstract
OBJECTIVE: To investigate brain radiation dose in complex cases arising from two stereotactic linear accelerator designs and to present a method for comparing brain dose to published data. METHODS: Two head designs were considered: Beam Modulator (BM) and Agility (AG). 12 patients treated on BM were replanned with AG. Planning objectives were: minimize brain dose and satisfy target coverage and organs at risk dose constraints. Each of the 36 targets was analyzed for conformality index (CI Paddick), gradient index (GI) and homogeneity index (HI). Total volume of tissue receiving 80% (V80) of the prescription dose down to 25% (V25) was evaluated. Similarly the volume of brain minus planning target volume receiving 80% (BMP80) down to 25% (BMP25) was evaluated. The mean brain dose and BMP dose were also evaluated. System differences were statistically evaluated using Wilcoxon signed-rank test. Power-law models for total volume (V) and brain minus planning target volumes (BMP) were generated based on BM data. RESULTS: The median CI Paddick was 0.74 and 0.76 for BM and AG, respectively (p = 0.04). The median GI was 5.5 and 6.1 (p < 0.01) and the median HI was 1.17 and 1.16 for BM and AG, respectively (p < 0.01). Neither V or BMP receiving doses of 80% down to 40% exhibited statistically significant difference between the two systems, whereas the volume of brain minus PTV receiving 25% (BMP25) was weakly different (p = 0.02). AG exhibited a lower mean BMP dose (4.1 Gy) than BM (4.6 Gy) (p < 0.01). Power-law models for V/BMP showed excellent (R(2) > 0.80) agreement for the dose levels studied and comparable results with published data. CONCLUSION: Treatment plans of equivalent quality were attained with AG compared with BM. ADVANCES IN KNOWLEDGE: The AG system involves a novel collimation design. The present article demonstrates equivalent or improved brain dose for complex, multitarget cases using AG vs an older stereotactic system.
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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 | 0.001 |
| 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) | 0.002 | 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".