Emerging Ocular Side Effects of Immune Checkpoint Inhibitors: A Comprehensive Review
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, offering significant improvements in patient survival across various malignancies. However, their use is associated with a broad spectrum of immune-related adverse events (irAEs), including those affecting the eye and its surrounding structures, collectively termed ocular irAEs (OirAEs). Although rare, OirAEs (e.g., keratitis, uveitis, retinal vasculitis, etc.) can significantly impact a patient's quality of life, leading to ocular complications if left untreated. This review provides a comprehensive overview of OirAEs associated with ICIs, including their clinical manifestations, underlying mechanisms, and current management strategies. We delve into the anterior and posterior segment adverse events, highlighting conditions such as dry eye, uveitis, and retinal disorders, as well as neuro-ophthalmic and orbital complications. Furthermore, we discuss the challenges in diagnosing and treating these conditions, particularly given the overlap with other autoimmune and paraneoplastic syndromes. Finally, we identify key knowledge gaps and suggest future research directions aimed at optimizing the management of OirAEs while maintaining the efficacy of cancer therapy. This review underscores the need for increased awareness among clinicians to prevent irreversible ocular damage and enhance patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".