Static couch non-coplanar arc selection optimization for lung SBRT treatment planning
Bibliographic record
Abstract
Abstract Objective . Non-coplanar arc geometry optimizations that take advantage of beam’s eye view (BEV) geometric overlap information have been proven to reduce dose to healthy organs-at-risk (OARs). Recently, a metric called mean arc distance (MAD) has been developed that quantifies the arc geometry sampling of 4 π space. The purpose of this research is to combine improved BEV overlap information with MAD to generate static couch lung stereotactic body radiotherapy (SBRT) treatment plans deliverable on a C-arm linear accelerator. Approach . An algorithm utilizing the Moller–Trumbore ray-triangle intersection method was employed to compute a cost surrogate for dose to overlapping OARs using distances interpolated onto a PDD. Cost was combined with MAD for 100 000 random combinations of arc trajectories. A pathfinding algorithm for arc selection was created, balancing the contributions of MAD and 4 π cost for the final trajectory. This methodology was evaluated for 18 lung SBRT patients. Cases were also planned with arcs from a clinical treatment template protocol for dosimetric and plan quality comparison. Results were evaluated using dose constraints in the context of RTOG0915. Main results . Five of six OARs had maximum dose reductions when planned with the arc trajectory optimization algorithm. Significant maximum dose reductions were found for esophagus (7.41 ± 0.91 Gy, p = 0.00019), trachea (5.56 ± 1.55 Gy, p = 0.0025), spinal cord (2.87 ± 1.13 Gy, p = 0.039), large bronchus (3.47 ± 1.49 Gy, p = 0.0075), and aorta (3.13 ± 0.99 Gy, p = 0.012). Mean dose to contralateral lung was also significantly reduced (0.50 ± 0.06 Gy, p = 0.00019). There were two significant increases in OAR doses: mean dose to ipsilateral lung (0.40 ± 0.09, p = 0.00086) and V5 Gy to ipsilateral lung (1.95 ± 0.70%, p = 0.011). Paddick conformity index increased by 0.03 ± 0.02 ( p = 0.14), remaining below a limit of 1.2 for both techniques. Significance . Static couch non-coplanar optimization yielded maximum dose reductions to OARs while maintaining target conformity for lung SBRT.
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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) | 0.001 | 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 | 0.001 | 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".