Abstract
Introduction
Ocular melanoma is a type of malignancy affecting the eye. Symptoms can include dark spots near or around the iris or the mucous membrane of the eyes. Treatments include radiation, laser therapy, and enucleation or evisceration of the eye.
Method
A bibliometric analysis was conducted using the database Web of Science. VosViewer version 1.6.20 was utilized to import tab-delimited files and visualize the data from Web of Science.
Results
Data was collected from 1990 to 2024, with 2021 being the highest publication year (258). The U.S. (1,950), Germany (405), and England (205) released the greatest number of publications. From those countries, Thomas Jefferson University and Leiden University released the largest number of articles written about ocular melanoma.
Conclusion
Ocular melanoma is a rare type of cancer affecting people worldwide. As medicinal research advances over time, the ability to prevent and treat ocular melanoma steadily improves. Over the past years (1990–2024), there has been a general increase in the number of publications regarding ocular melanoma. There have been gaps in the research in South Asian and African demographics, showing that these communities have not brought enough attention to this disease.
Keywords: Uveal, Ocular, Melanoma, Ocular melanoma
Introduction
Melanocarcinoma, more commonly referred to as melanoma, is the abnormal growth of melanocytes. Ocular melanoma specifically arises within the eye, most commonly in the uvea, whereas melanoma occurring in the skin around the eye is a distinct entity known as cutaneous or periocular melanoma. Although ocular melanoma is the most common kind of cancer that can occur in the eyes, it is still an extremely rare condition. Roughly, there are 1500 new cases that are diagnosed annually. Ocular melanoma does not have a specific proclivity to a certain gender; however, it is more prominent in middle-aged Caucasians [1, 2].
Eyes have three main layers: the sclera (fibrous layer), the uvea (vascular layer), and the retina (inner layer) (Fig. 1).
Fig. 1.
Anatomy of the eye, Created in BioRender. Ganti, L. (2025) https://BioRender.com/d4ynla9
Ocular melanoma mainly occurs in the uvea, which can be detected on standard opthalmological examinations, including fundoscopy and imaging techniques such as ultrasound or optical coherence tomography (Fig. 2). The predisposing factors can include having light-colored eyes, hyperexposure to natural sunlight, the growth of nevi and moles on or around the eyes, as well as having inherited skin conditions [1].
Fig. 2.
Schematic representation of healthy eye vs. ocular melanoma. Created in BioRender. Ganti, L. (2025) https://BioRender.com/d4ynla9
Symptoms of ocular melanoma can be hard to detect as the malignant growth occurs in the uvea [3]. Over time, symptoms can include a dark spot or spots on the iris or conjunctiva (a mucus membrane that covers the whole of the outer eye), impaired vision, or even the sensation of flashing lights [4].
Treatments for ocular melanoma include radiation (blue light) therapy, laser therapy, plaque brachytherapy, proton beam therapy, enucleation, or emerging systemic therapies. Mohs micrographic surgery is performed for eyelid melanoma. These treatments are invasive; however, improvements such as Tebentafusp (Kimmtrak), Immune checkpoint inhibitors, T cell engagers (TCEs), and tumor-infiltrating lymphocytes are steadily being developed [3]. This bibliometric analysis explores the methods and improvements by which ocular melanoma is treated [5].
Methods
Bibliometric methods, including the Equator Network suggested BIBLIO checklist were used to examine the research landscape of ocular melanoma using the Web of Science (WOS) as the database and VOSviewer as the visualization software [6, 7]. The composition of this analysis was curated through the Web of Science database. The Web of Science is a highly distinguished archive containing vast volumes of publications and data that have been collected since 1990. In order to perform this research, the key words used were Melanoma and Ocular (“Melanoma” AND “Ocular”). The data used was collected from 1990 to 2024, excluding the year 2025 (the release date of this publication), since data is not yet complete, which yielded 4,678 journal articles. The desktop application VosViewer (1.60.20) was used to provide the graphs that visualize the given data by exporting tab-delimited files from Web of Science. Graphs were created to show the number of publications produced by organizations globally, publications produced by countries, the number of occurrences of keywords, and the most prevalent articles. To reduce visual congestion in graphs, only the top 50 organizations with the greatest number of publications were included in the graph, and the minimum number of documents published for countries was increased to 13. The minimum number for occurrences was increased to 150 occurrences to minimize clutter on the graph, and the minimum number of citations for each document was increased to 150 to allow for a cleaner visualization.
Results
There has been a wide array of universities and organizations responsible for publications on uveal Melanoma (Fig. 3). Based on the data presented in the graphs, Thomas Jefferson University released the highest number of publications, with 176 articles, which account for 3.76% of the overall publications. The organization with the second most reports came from Leiden University, with 125 released articles, which made up for 2.67% of the overall search. Following that, Emory University released 104 papers, which is 2.22% of the total publications. The University of Miami (88 publications, 1.88%) and the Royal Liverpool (79 publications, 1.69%) also had a significant number of articles and research that was performed on ocular melanoma. The remainder of the universities and organizations had a relatively equal spread in terms of publications.
Fig. 3.
Visualization of publications from organizations
The top countries to release publications were the United States, England, and Germany. The U.S. has released 1,950 articles, which is a large 41.75% of the overall research about ocular melanoma. Germany released the second most articles with 405 articles, 8.66%. England released the third greatest number of articles with 205 articles, which was a close 4.38%. The United States is a highly industrialized country, which accounts for its large percentage of research and publications (Fig. 4).
Fig. 4.
Visualization of publications from countries
In all the articles, the keywords that were mentioned the most were Uveal Melanoma (N = 1422), Melanoma (N = 840), Choroidal Melanoma (N = 826), and ocular melanoma (N = 817). Melanoma was often combined with other words, creating a large section. Outlier words included those regarding different therapies, mutations, and tumors (Fig. 5).
Fig. 5.
Visualization of occurrences of keywords
The article with the most citations pertaining to ocular melanoma was “The National Cancer Database Report on Cutaneous and Noncutaneous Melanoma” (1,247 citations) by Chang et al., published in 2000 [8] (Fig. 6). However, this reference is a general report encompassing all melanoma forms, including cutaneous and noncutaneous forms, and does not specifically focus on ocular melanoma. The high citation count is therefore likely attributable to its broad coverage of melanoma rather than its direct contribution to ocular melanoma research.
Fig. 6.
Publications from citations of authors
The article with the second most citations was “Frequent Somatic Mutations of GNAQ in Uveal Melanoma and Blue Naevi” (1,205 citations) by Catherine D. Van Raamsdonk et al., published in 2008 [9]. The next article with the most citations was “Very Long-Term Prognosis of Patients with Malignant Uveal Melanoma” (775 citations) by Kujala et al., published in 2003 [10]. The article with the fourth most citations regarding ocular melanoma was “Uveal Melanoma”, by Jager et al., published in 2020 (513 citations) [2] (Table 1). Figure 6 depicts the collaborations between authors.
Table 1.
Data table for the most cited articles
Research on ocular melanoma (with the exception of a few years, i.e., 2006, 2015, etc.) shows a generally increasing trend (Fig. 7). 2025 (124) has a lower number due to the fact that research for this year is still in progress. This shows that there is a growth in ocular melanoma research.
Fig. 7.
Publications per year from 1997–2025
The tree graph in Fig. 8 was generated from Web of Science to visualize the different areas of interest in which papers regarding the treatments of ocular melanoma were published. The sum of all the numbers in this graph will not add up to the total publications searched (4,678) because only the ten highest categories were included. Based on the data in this graph, the majority of the publications were categorized in the Ophthalmology category, with 1,951 publications. This was followed by Oncology and Radiology Nuclear Medicine Medical Imaging, with 1,099 and 524 publications, respectively.
Fig. 8.
Tree graph of publications in top 10 web of science categories
Discussion
The grouping shown on the graphs represents the relationships between the U.S. and European countries that have contributed the most to the publication of articles regarding ocular melanoma. As illustrated in the graphs, American organizations collaborated with each other, as did European universities and organizations. Notably, there are few Asian, African, and South American regions within the research pool.
The United States conducted a cross-sectional study that included US citizens diagnosed with ocular melanoma. The survey was carried out by the Ocular Melanoma Foundation [11]. The research addressed essential questions regarding how patients were diagnosed. The results indicated that one hundred and twelve (62.6%) of 179 patients were diagnosed by an ophthalmologist and 55 (30.7%) by an optometrist, and 12 (6.7%) reported being diagnosed by another provider.
In addition to the US, many European countries have made advancements in ocular melanoma research. The British Journal of Ophthalmology sought to explore the complexity of ocular melanoma. They targeted different subjects in order to unveil the layers of complexity within the disease, based on the significant underreporting or underdiagnosis of ocular melanoma for England and Wales in those over the age of 65 years [12].
New directions are emerging that may reshape both clinical practice and research priorities in ocular melanoma. One such area is the concept of watchful waiting for small choroidal lesions. Historically, early intervention was favored to minimize metastatic risk, but recent longitudinal studies indicate that closely monitoring small, indeterminate lesions, particularly those without high-risk features, may not worsen prognosis. This more selective approach could reduce overtreatment, limit unnecessary procedures, and focus interventions on cases with demonstrable progression [13–16].
Another developing field examines the link between melatonin, circadian rhythms, and tumor biology. Disruptions in circadian regulation have been implicated in cancer biology, and melatonin has shown anti-proliferative and antioxidant effects in experimental models of melanoma. These findings suggest that circadian biology could influence tumor behavior and therapeutic response, raising the possibility of chronotherapy, in which treatments are timed to maximize effectiveness based on biological rhythms [17].
Advances in gene therapy and immunotherapy are also changing the therapeutic landscape. Molecular studies have identified recurrent mutations in GNAQ and GNA11 that drive tumorigenesis, prompting targeted drug development to inhibit downstream signaling pathways [18]. In parallel, immune checkpoint inhibitors—already transformative in cutaneous melanoma—are being tested for ocular melanoma, with early studies exploring their potential to enhance the body’s own immune response against tumor cells [19].
In diagnostics, artificial intelligence (AI) is being explored for its potential to improve early detection and monitoring. AI-powered algorithms trained on fundus photography, optical coherence tomography (OCT), and ultrasound imaging can enhance lesion detection, improve segmentation accuracy, and reduce observer variability [20]. Such tools could standardize diagnostic quality across institutions and improve access to expert-level assessment in underserved regions [20, 21].
Large patient registries are playing an increasingly important role in advancing the field. Databases such as the Ocular Melanoma Foundation’s patient survey and international multicenter registries collect longitudinal data on thousands of patients, allowing for robust analysis of treatment outcomes, survival patterns, and adverse events [11]. These datasets enable the identification of rare trends, facilitate international collaboration, and provide a foundation for designing large-scale clinical trials [22].
Taken together, these developments signal a shift toward more personalized, data-driven care in ocular melanoma. Whether through tailored surveillance strategies, integrating circadian biology into treatment planning, applying molecularly targeted or immune-based therapies, deploying AI-enhanced diagnostics, or harnessing the power of collaborative registries, the field is rapidly evolving. As these research avenues mature, they are poised to complement and enhance the established diagnostic and therapeutic approaches, leading to improved patient outcomes and a deeper understanding of this complex disease.
Limitations
The studies used to obtain information were exclusively from the Web of Science. Due to these limitations, data from outside resources is not present. Additionally, A study showed that Web of Science may not receive data, files, and articles as rapidly as other databases such as PubMed [23]. Another constraint faced was that the search was hindered by language bias. The data files obtained contained only articles that were written in English; thus, a number of publications and data were not included in this analysis. Additionally, this analysis only included dates of publication from dates 1990 to 2024. To broaden the scope of the research, dates before 1990 and for the first half of 2025 could have been included. Lastly, the search strategy could have been modified to include more or fewer keywords. Fewer keywords would allow for a broader search, yielding more results. More keywords, however, would make for an extensively specific search, both of which would improve the overall analysis.
Conclusion
Ocular melanoma is an ophthalmologic cancer that affects people worldwide. As medical research advances over time, more publications result. This bibliometric review highlights geographic regions with the highest research output and emerging treatment themes, including watchful waiting versus prompt intervention.
Authors’ contributions
ML and EN drafted the initial manuscript. VR and LG edited and critically revised the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for this study.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Consent for publication
Not applicable.
Competing interests
Dr. Ganti has an editorial role at Springer.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Chattopadhyay C, Kim DW, Gombos DS, Oba J, Qin Y, Williams MD, Esmaeli B, Grimm EA, Wargo JA, Woodman SE, Patel SP. Uveal melanoma: from diagnosis to treatment and the science in between. Cancer. 2016;122(15):2299–312. 10.1002/cncr.29727. Epub 2016 Mar 15. PMID: 26991400; PMCID: PMC5567680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jager MJ, Shields CL, Cebulla CM, Abdel-Rahman MH, Grossniklaus HE, Stern MH, Carvajal RD, Belfort RN, Jia R, Shields JA, Damato BE. Uveal melanoma. Nat Rev Dis Primers. 2020;6(1):24. 10.1038/s41572-020-0158-0. Erratum in: Nat Rev Dis Primers. 2022;8(1):4. 10.1038/s41572-022-00339-9. PMID: 32273508. [DOI] [PubMed]
- 3.Jovanovic P, Mihajlovic M, Djordjevic-Jocic J, Vlajkovic S, Cekic S, Stefanovic V. Ocular melanoma: an overview of the current status. Int J Clin Exp Pathol. 2013;6(7):1230–44. PMID: 23826405; PMCID: PMC3693189. [PMC free article] [PubMed] [Google Scholar]
- 4.Begaj T, Capone A Jr. Ocular melanocytosis with uveal melanoma. Ophthalmology. 2024;131(1):65. 10.1016/j.ophtha.2023.04.017. Epub 2023 May 22. PMID: 37212765. [DOI] [PubMed] [Google Scholar]
- 5.Leyvraz S, Keilholz U. Ocular melanoma: what’s new? Curr Opin Oncol. 2012;24(2):162-9. 10.1097/CCO.0b013e32834ff069. PMID: 22234256. [DOI] [PubMed] [Google Scholar]
- 6.Montazeri A, Mohammadi S, Hesari M, Ghaemi P, Riazi M, Sheikhi-Mobarakeh H. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Syst Rev. 2023;12(1):239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ganti L, Persaud NA, Stead TS. Bibliometric analysis methods for the medical literature. Academic medicine & surgery. Published Online January. 2025;30. 10.62186/001c.129134.
- 8.Chang AE, Karnell LH, Menck HR. The National cancer data base report on cutaneous and noncutaneous melanoma. Cancer. 1998;83:1664–78. 10.1002/(SICI)1097-0142(19981015)83:8%3C1664::AID-CNCR23%3E3.0.CO;2-G. [DOI] [PubMed] [Google Scholar]
- 9.Van Raamsdonk C, Bezrookove V, Green G, et al. Frequent somatic mutations of GNAQ in uveal melanoma and blue Naevi. Nature. 2009;457:599–602. 10.1038/nature07586. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kujala E, Mäkitie T, Kivelä T. Very long-term prognosis of patients with malignant uveal melanoma. Invest Ophthalmol Vis Sci. 2003;44(11):4651–9. 10.1167/iovs.03-0538. [DOI] [PubMed] [Google Scholar]
- 11.Afshar AR, Deiner M, Allen G, Damato BE. The patient’s experience of ocular melanoma in the US: A survey of the ocular melanoma foundation. Ocul Oncol Pathol. 2018;4(5):280–90. 10.1159/000485189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Foss AJ, Cree IA, Dolin PJ, Hungerford JL. Modelling uveal melanoma. Br J Ophthalmol. 1999;83(5):588–94. 10.1136/bjo.83.5.588. PMID: 10216060; PMCID: PMC1723024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Singh AD, Turell ME, Topham AK. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology. 2011;118(9):1881–5. 10.1016/j.ophtha.2011.01.040. Epub 2011 Jun 24. PMID: 21704381. [DOI] [PubMed] [Google Scholar]
- 14.Grin-Jorgensen C, Berke A, Grin M. Ocular melanoma. Dermatol Clin. 1992;10(4):663–8. https://pubmed.ncbi.nlm.nih.gov/1395150/. [PubMed] [Google Scholar]
- 15.Carvajal RD, Sacco JJ, Jager MJ, et al. Advances in the clinical management of uveal melanoma. Nat Rev Clin Oncol. 2023;20:99–115. 10.1038/s41571-022-00714-1. [DOI] [PubMed] [Google Scholar]
- 16.Gerba-Górecka K, Romanowska-Dixon B, Karska-Basta I, Cieplińska-Kechner E, Nowak MS. Clinical characteristics and management of ocular metastases. Cancers (Basel). 2025;17(6):1041. 10.3390/cancers17061041. PMID: 40149375; PMCID: PMC11940828. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Masri S, Sassone-Corsi P. The emerging link between cancer, metabolism, and circadian rhythms. Nat Med. 2018;24(12):1795–803. 10.1038/s41591-018-0271-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Silva-Rodríguez P, Fernández-Díaz D, Bande M, Pardo M, Loidi L, Blanco-Teijeiro MJ. GNAQ and GNA11 genes: A comprehensive review on Oncogenesis, prognosis and therapeutic opportunities in uveal melanoma. Cancers (Basel). 2022;14(13):3066. 10.3390/cancers14133066. Published 2022 Jun 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Shoushtari AN, Carvajal RD. GNAQ and GNA11 mutations in uveal melanoma. Melanoma Res. 2014;24(6):525–34. 10.1097/CMR.0000000000000121. [DOI] [PubMed] [Google Scholar]
- 20.Dahrouj M, Miller JB. Artificial intelligence (AI) and retinal optical coherence tomography (OCT). Semin Ophthalmol. 2021;36(4):341–5. 10.1080/08820538.2021.1901123. [DOI] [PubMed] [Google Scholar]
- 21.Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2022;19(2):132–46. 10.1038/s41571-021-00560-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther. 2022;7(1):156. 10.1038/s41392-022-00994-0. Published 2022 May 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, web of Science, and Google scholar: strengths and weaknesses. FASEB J. 2008;22:338–42. 10.1096/fj.07-9492LSF. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.









