Impact of Multileaf Collimator Width and Normal Tissue Objective on Radiation Dose Distribution in Stereotactic Radiosurgery Using HyperArc for Single Brain Lesions
Bibliographic record
Abstract
This study retrospectively investigated the impact of stereotactic radiosurgery (SRS) normal tissue objective (NTO) and multileaf collimator (MLC) width on radiation dose distribution in patients with brain metastasis treated using HyperArc. In total, 21 patients who underwent SRS using the HyperArc of the TrueBeam linear accelerator from November 2022 to June 2024 were included. All patients received radiotherapy with HASH planned with SRS NTO and HD MLC. HyperArc(HAAH) combined with the auto NTO and HD MLC and HyperArc(HAAM) with auto NTO and millennium MLC were generated and compared. Monitor units (MU), conformity index (CI), radical dose homogeneity index (rDHI), moderate DHI (mDHI), and gradient index (GI) were evaluated as target factors, and V2(Gy), V10(Gy), V12(Gy), V18(Gy), V10(cc), and V12(cc) were evaluated as normal brain factors. Dosimetric comparisons were performed between HASH, HAAH, and HAAM and between target and normal brain tissues. Between HASH and HAAH, average MU was 7206 and 5798, respectively; the difference was significant (p < 0.001). The MU of HAAM was 5835. Among HASH, HAAH, and HAAM, CI and mDHI were not significantly different, but there were significant differences in rDHI, GI, and normal brain tissues. When treating a single lesion using HyperArc, SRS NTO influences MU and GI, and the MLC width influences rDHI and GI. In HyperArc for single metastatic brain lesions, SRS NTO and MLC width have a significant effect on the radiation dose delivered to the target and normal brain tissues.
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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 | 0.001 |
| 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) | 0.001 | 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".