Improving organs-at-risk sparing for choroidal melanoma patients: A CT-based two-beam strategy in ocular proton therapy with a dedicated eyeline
Bibliographic record
Abstract
Purpose To investigate the potential clinical benefit of a two-beam arrangement technique using three-dimensional (3D) imaging of uveal melanoma (UM) patients treated with proton therapy and a dedicated eyeline. Material/Methods Retrospective CT-based treatment plans of 39 UM patients performed using a single beam (SB) were compared to plans with two beams (TB) optimized for better trade-offs in organs-at-risk sparing. The RBE-weighted prescribed dose was 60 Gy ( D RBE, GTV = 60 Gy) in four fractions, assuming an RBE of 1.1. Dosimetric findings were analyzed for three patient groups based on tumor-optic nerve distance and UM staging (group GrA: ≤3 mm, T1 T2 UM; GrB: ≤3 mm, T3 UM; GrC: >3 mm, T1 T2 T3 UM). Finally, two schedules were compared on biologically effective dose (BED): both beams being delivered either the same day (TB), or on alternate days (TBalter). Results All strategies resulted in dosimetrically acceptable plans. A dose reduction to the anterior structures was achieved in 23/39 cases with the two-beam plans. D 25% was significantly lowered compared to SB plans by 12.4 and 15.4 Gy RBE-weighted median dose in GrA and GrB, respectively. D 2% was reduced by 18.6 and 6.0 Gy RBE-weighted median dose in GrA and GrB, respectively. A cost to the optic nerve was observed with a median difference up to 3.8 Gy RBE-weighted dose in GrB. BED differences were statistically significant for all considered parameters in favor of two beams delivered the same day. Conclusion A two-beam strategy appears beneficial for posterior tumors abutting the optic nerve. This strategy might have a positive impact on the risk of ocular complications.
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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 | 0.000 |
| 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) | 0.002 | 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".