Background/Objectives: Although it comprises only 5% of all melanomas, uveal melanoma (UM) is the most commonly observed primary intraocular cancer. Methods: Poor patient survival persists in spite of innovative systemic therapies. In fact, approximately fifty percent of UM patients develop metastases from micro-metastases that remain undetected at the exact time of diagnosis. Results: The molecular understanding of UM is significantly enhanced by the recent identification of several mutations that are responsible for the metastasis, growth, and survival of UM. The crucial point is a more accurate genetic analysis for patient follow-up and metastatic risk prediction. Conclusions: This review provides a brief summary of the molecular features of UM that are recently discovered, as well as cytogenetic markers and biochemical pathways that are associated with the development of UM metastases.

Background/Objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare systems. Predicting injections needs at each monitoring session could optimize treatment outcomes and reduce unnecessary interventions. Methods: To achieve these aims, machine learning (ML) models were evaluated using different combinations of clinical variables, including retinal thickness and volume, best-corrected visual acuity, and features derived from macular optical coherence tomography (OCT). A “Leave Some Subjects Out” (LSSO) nested cross-validation approach ensured robust evaluation. Moreover, the SHapley Additive exPlanations (SHAP) analysis was employed to quantify the contribution of each feature to model predictions. Results: Results demonstrated that models incorporating both structural and functional features achieved high classification accuracy in predicting injection necessity (AUC = 0.747 ± 0.046, MCC = 0.541 ± 0.073). Moreover, the explainability analysis identified as key predictors both subretinal and intraretinal fluid, alongside central retinal thickness. Conclusions: These findings suggest that session-by-session prediction of injection needs in nAMD patients is feasible, even without processing the entire OCT image. The proposed ML framework has the potential to be integrated into routine clinical workflows, thereby optimizing nAMD therapeutic management.

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD.

Purpose: To report anatomical and functional outcomes of epiretinal proliferation (EP) embedding without internal limiting membrane peeling for lamellar macular holes (LMHs) with EP. Methods: This retrospective interventional case series involved patients with idiopathic LMHs and EP who underwent pars plana vitrectomy using the EP-embedding technique without internal limiting membrane peeling. The EP was peeled centripetally, left connected to the LMH edge, and embedded into the retinal cleavage plane. 20% sulfur hexafluoride gas tamponade was applied. After surgery, patients were recommended to avoid lying face-up. Results: Fifteen eyes of 15 patients were included, and the mean follow-up was 12 ± 8 months. Lamellar macular hole closure was achieved in 100% of cases. Best-corrected visual acuity and central retinal thickness showed significant postoperative improvement, from 0.31 ± 0.20 logMAR (20/40 Snellen) and 156.5 ± 40.7 µ m preoperatively to 0.10 ± 0.12 logMAR (20/25 Snellen) ( P = 0.002) and 221.5 ± 46.5 µ m ( P < 0.001), respectively. Ellipsoid zone (EZ) and external limiting membrane restoration rates were 50% and 100%, respectively. No cases of full-thickness macular hole formation, LMH recurrence, or major complications were observed. Conclusion: EP-embedding without internal limiting membrane peeling yields favorable anatomical and functional outcomes in LMHs with EP and supports the nontractional pathophysiology of the condition.

Background: The melanocortin-1 receptor (MC1R) gene is a major determinant of skin and hair color. Although several MC1R variants increase the risk of skin tumors regardless of pigmentation, their role as genetic modifiers in ocular diseases remains poorly understood. Methods: Here, we analyzed a cohort of 48 Caucasian individuals using a custom next-generation sequencing (NGS) panel targeted for oculocutaneous disorders. Results: MC1R variants were identified in two probands selected from this cohort, exhibiting distinctly divergent pathological phenotypes. The first patient, an adult male with a pheomelanin-related trait and a history of early-onset skin cancer without ocular abnormalities, carried compound heterozygous MC1R variants: the novel frameshift c.537dupC (p.Ile180Hisfs*59) and the known c.880G>C (p.Asp294His). Conversely, the second patient, a young boy with severe vision-threatening features—including congenital nystagmus, foveal hypoplasia, and an atypical visual-pathway misrouting pattern—carried a candidate digenic genotype combining the same MC1R c.880G>C variant with the albinism-related c.1209G>T (p.Arg403Ser) variant in the tyrosinase (TYR) gene. Conclusions: While MC1R variants have previously been proposed as phenotypic modifiers in albinism, primarily in combination with the oculocutaneous type II melanosomal transmembrane protein (OCA2) encoding gene, our findings support a potential role for MC1R as a candidate modifier interacting with TYR in neuroretinal development. Although functional validation is required to establish causality, incorporating MC1R screening in patients with unresolved foveal hypoplasia may help clarify complex oligogenic mechanisms and optimize phenotypic classification.