Overlap‐guided fixed‐patient support positioning optimization for cranial <scp>SRT</scp>
Bibliographic record
Abstract
PURPOSE: To investigate potential dosimetric improvements through the optimization of fixed-couch rotational position in cranial cancer stereotactic treatments. METHODS: Using previously delivered cranial stereotactic radiotherapy plans treated at the Nova Scotia Health Authority (NSHA), we have redesigned the treatment arrangement to find the optimal couch rotation positions based on the reduction of overlap between organs-at-risk of exposure (OARs) and target volume (PTV). Maintaining the gantry arrangements from the delivered treatment, the couch positions were determined based on a cost function analysis of accumulation of overlap score from an equation developed by Yang et al. and refined by MacDonald et al. The algorithm incorporates factors for radiation dose sensitivities of each OAR, depth of both OARs and target (PTV) volumes, and orthogonality of the 3D vector between OAR and PTV in the case of proximal OAR position. RESULTS: The plan evaluation was conducted on 16 acoustic neuroma patients treated with stereotactic radiotherapy plans at the NSHA. Maximum and mean doses to the OARs were reduced by approximately 14.30% ± 2.86% and 19.25% ± 2.10%, respectively, with application of this optimization technique as compared to the delivered treatment plans. In addition, PTV conformity and homogeneity were improved with application of this optimization technique. CONCLUSION: This variation of the existing delivery techniques with guidance from a PTV-OAR overlap cost function analysis technique can yield significant dosimetric improvements with no increase to delivery or planning time.
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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 | 0.001 | 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".