Sci‐AM2 Sat ‐ 01: A novel geometric analysis for beam angle optimization in 3D‐CRT based on target conformity
Bibliographic record
Abstract
In radiotherapy, the primary goal is to deliver high dose to the Planning Target Volume (PTV) while sparing surrounding healthy tissue. For maximum radiation conformity to the PTV, one tries to achieve an extremely sharp dose gradient around the PTV. In this work, we have conducted a fundamental geometrical analysis of the PTV to explore the concept of geometrically inferring the preferred location of sharp dose gradients. To achieve this, the polygonal surface mesh data of the PTV was extracted from the Pinnacle3 treatment planning system and the surface normal of each polygon composing the PTV surface mesh was computed. Vectors describing the source/isocentre orientation of every possible couch and gantry combination were pre‐computed. If the polygon surface normal and the gantry‐couch vector were perpendicular, then the area of that polygon was cumulatively stored at the gantry‐couch combination. This beam orientation would then allow a beam to be aligned parallel to that particular surface of the PTV in order to produce a sharp dose gradient between the PTV and surrounding tissue. Optimal beam orientations were inferred from gantry‐couch combinations that were parallel to the largest cumulative area of PTV polygons. The algorithm was implemented on a cohort of prostate, lung, and head‐and‐neck patients. Optimal beam orientations for target conformity are presented for coplanar geometry, in order to simplify comparison to clinical beam configurations. This is the first work based on a geometric analysis of the PTV surface to demonstrate the 4‐field box configuration as an optimal orientation for prostate radiotherapy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".