Uveal Melanoma: Comprehensive Review of Its Pathophysiology, Diagnosis, Treatment, and Future Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Uveal melanoma (UM) is the most common intraocular malignancy in adults. Recent advances highlight the role of tumor-derived extracellular vesicles (TEV) and circulating hybrid cells (CHC) in UM tumorigenesis. Bridged with liquid biopsies, a novel technology that has shown incredible performance in detecting cancer cells or products derived from tumors in bodily fluids, it can significantly impact disease management and outcome. The aim of this comprehensive literature review is to provide a summary of current knowledge and ongoing advances in posterior UM pathophysiology, diagnosis, and treatment. The first section of the manuscript discusses the complex and intricate role of TEVs and CHCs. The second part of this review delves into the epidemiology, etiology and risk factors, clinical presentation, and prognosis of UM. Third, current diagnostic methods, ensued by novel diagnostic tools for the early detection of UM, such as liquid biopsies and artificial intelligence-based technologies, are of paramount importance in this review. The fundamental principles, limits, and challenges associated with these diagnostic tools, as well as their potential as a tracker for disease progression, are discussed. Finally, a summary of current treatment modalities is provided, followed by an overview of ongoing preclinical and clinical research studies to provide further insights on potential biomolecular pathway alterations and therapeutic targets for the management of UM. This review is thus an important resource for all healthcare professionals, clinicians, and researchers working in the field of ocular oncology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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) | 0.000 | 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 it