Serial and parallel organ‐at‐risk‐specific noncoplanar arc optimization for small versus large target volumes in liver SBRT
Bibliographic record
Abstract
Noncoplanar arc optimization has been shown to reduce OAR doses in SRS/SRT and has the potential to reduce doses to OARs in SBRT. Extracranial targets have additional considerations, including large OARs and, in the case of the liver, volume constraints on the healthy liver. Considering pathlengths through OARs that encompass target volumes may lead to specific dose reductions as in the encompassing healthy liver tissue. These optimizations must also leverage delivery efficiency and trajectory sampling to ensure ease of clinical translation. The purpose of this research is to generate optimized static-couch arcs that separately consider serial and parallel OARs and arc delivery efficiency, with a trajectory sampling metric, towards the aim of reducing dose to OARs and the surrounding healthy liver tissue. Separate BEV cost maps were created for parallel, and serial OARs by means of a fast ray-triangle intersection algorithm. An additional BEV cost map was created for the liver which, by definition, encompasses the liver tumors. The individual costs of these maps were summed and combined with the sampling metric for 100 000 random combinations of arc trajectories. A search algorithm was applied to find an arc trajectory solution that satisfied BEV cost and sampling optimization, while also ensuring an efficient delivery was possible with a low number of arcs. This method of arc selection was evaluated for 16 liver SBRT patients characterized by small and large target volumes. Comparisons were made with a clinical arc template of coplanar arcs. Dosimetric plan quality was evaluated using published guidelines and metrics from RTOG1112. Four of five plan quality metrics for the liver were significantly reduced when planned with optimized noncoplanar arcs. Median (range) reductions of the volumes receiving 10, 18, and 21 Gy were found of 140.4 (295.8) cc (p = 0.001), 28.2 (230.6) cc (p = 0.002) and 18.5 (155.5) cc (p = 0.04). A significant increase in median (range) dose to the right kidney of 0.2 ± 0.9 Gy (p = 0.03) was also found using optimized noncoplanar arcs, which was below the tolerance of 10 Gy for all cases. The average number of arcs chosen was 4 ± 1. Optimizing serial and parallel OARs separately during static couch noncoplanar arc selection significantly reduced the dose to the liver during SBRT using a moderate number of arcs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".