Proton Therapy in Uveal Melanoma
Bibliographic record
Abstract
Background/Objectives: Uveal melanoma is the most common primary intraocular malignancy in adults. Treatment options for localized, early-stage disease include enucleation, brachytherapy, and proton beam therapy. This review aims to evaluate the role of proton therapy in the definitive management of uveal melanoma, focusing on its physics, radiobiology, treatment techniques, and associated outcomes. Methods: This narrative review synthesizes current literature on proton therapy for uveal melanoma, emphasizing case selection, treatment efficacy, and side effects. Results: Proton therapy offers significant advantages for thicker uveal melanomas (over 8 mm) due to its unique physical properties, including a rapid dose fall-off that protects critical structures like the retina and optic nerve. Proton therapy may have benefits in tumor control for ocular melanomas given its increased relative biological effectiveness relative to photon therapy for these typically more radioresistant melanomas. Proton therapy may also hold special value for uveal melanomas in close proximity to the optic nerve, as patients are at high risk of visual toxicities with brachytherapy. The review discusses the efficacy of proton therapy across small, medium, and large tumors, along with strategies for improving patient survival through combined systemic therapy. Additionally, the potential of ocular reirradiation with proton therapy is addressed. Conclusions: Proton therapy is an effective treatment for uveal melanoma. It offers advantages over brachytherapy for large tumors, tumors that are close to the optic nerve or insertion of extra-ocular muscles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".