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Artificial Intelligence in Ocular Oncology
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Artificial Intelligence in Ocular Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 18 September 2026 | Viewed by 1298

Special Issue Editor

Prof. Dr. Bertil E. Damato

E-Mail Website
Guest Editor
Ocular Oncology Service, Moorfields Eye Hospital, City Rd, London EC1V 2PD, UK
Interests: ocular oncology; perimetry; history of ophthalmology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are many kinds of ocular tumors, most of which are rare. Several are life threatening or associated with lethal syndromes. Diagnosis can be challenging because each tumor can show a wide variety of clinical manifestations. Multimodal imaging can be invaluable but only if the clinician has the skill to interpret the results correctly. Relevant information may be forgotten by the clinician, or out of date, or not easily found in the published literature. Artificial intelligence promises to help non-experts diagnose and manage patients with ocular tumors when support from an ocular oncologist is not possible. Where ocular oncology services are available, artificial intelligence may help avoid non-essential referrals, saving time and money both for patients and healthcare services. As with other technologies, clinicians must be aware of the limitations of artificial intelligence and the need to check its guidance by referring to peer-reviewed and authoritative texts. This Special Issue provides a forum for recent advances in artificial intelligence in ocular oncology.

Prof. Dr. Bertil E. Damato
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ocular tumor
  • eye cancer
  • artificial intelligence
  • ophthalmology

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Published Papers (2 papers)

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Research

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14 pages, 861 KB  
Article
Comparison of MOLES and MelAInoma for Differentiating Small Choroidal Melanomas from Nevi
by Katerina Stripling, Hannah Coudé Adam, Mats Holmström and Gustav Stålhammar
Cancers 2026, 18(5), 818; https://doi.org/10.3390/cancers18050818 - 3 Mar 2026
Viewed by 564
Abstract
Background: Early identification of small choroidal melanomas is important, as metastatic risk increases with tumor size. However, distinguishing small melanomas from benign choroidal nevi is challenging and may lead to unnecessary referrals and overtreatment. Both the MOLES scoring system and the deep learning [...] Read more.
Background: Early identification of small choroidal melanomas is important, as metastatic risk increases with tumor size. However, distinguishing small melanomas from benign choroidal nevi is challenging and may lead to unnecessary referrals and overtreatment. Both the MOLES scoring system and the deep learning algorithm MelAInoma have been developed to support assessment of pigmented choroidal lesions in non-expert settings. This study aims to compare the association between MOLES and MelAInoma scores and to assess their relative association with expert melanoma versus nevus diagnosis. Methods: In this retrospective cohort study, 86 patients with small pigmented choroidal lesions (29 melanomas and 57 nevi) diagnosed at a national ocular oncology referral center were included. MOLES scores were assigned by ocular oncologists based on multimodal examination, whereas MelAInoma scores were generated solely from color fundus photographs. Associations between scores were assessed using linear regression and the Jonckheere–Terpstra test. Univariable and multivariable binary logistic regression was used to evaluate associations with melanoma diagnosis. Results: MelAInoma scores increased monotonically with higher MOLES categories (p = 0.0001). Linear regression showed a statistically significant association between MOLES and MelAInoma scores, but with substantial dispersion (R2 = 0.16). In univariable logistic regression, both MOLES and MelAInoma scores were associated with increased odds of melanoma diagnosis. MelAInoma showed a stronger association with diagnosis than MOLES (R2 = 0.38 vs. 0.27). In multivariable analysis including both scores, each remained independently associated with melanoma diagnosis. Conclusions: Both MOLES and MelAInoma are effective for differentiating small choroidal melanomas from nevi. Although the scores are statistically associated, they capture partly distinct information. MelAInoma demonstrates slightly stronger association with melanoma diagnosis and provides fully reproducible output, supporting its role as a complementary aid in lesion triage. Full article
(This article belongs to the Special Issue Artificial Intelligence in Ocular Oncology)
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Review

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16 pages, 338 KB  
Review
Uveal Melanoma Ground Truth Labeling in Machine Learning
by Emily Kao, Sanjay Ganesh, William F. Chadwick, Reem Alahmadi, Xincheng Yao and Michael J. Heiferman
Cancers 2026, 18(9), 1357; https://doi.org/10.3390/cancers18091357 - 24 Apr 2026
Viewed by 363
Abstract
Background/Objectives: Uveal melanoma (UM) is the most common primary intraocular malignant tumor among adults and has a high risk of metastasis. Recently, artificial intelligence (AI) tools have been developed to support the management of UM across different clinical tasks. The definition of ground [...] Read more.
Background/Objectives: Uveal melanoma (UM) is the most common primary intraocular malignant tumor among adults and has a high risk of metastasis. Recently, artificial intelligence (AI) tools have been developed to support the management of UM across different clinical tasks. The definition of ground truth, the reference standard that models use in training and development, greatly influences the performance and clinical relevance of the models. Currently, there is limited consensus regarding which ground truth methods are most appropriate for each clinical application. This review aims to evaluate the advantages and limitations of available ground truth options in UM and proposes task-specific recommendations based on clinical utility, feasibility, and cost. Methods: A narrative review of the existing literature was conducted to identify and evaluate commonly used ground truth methods for UM AI applications based on factors such as time, cost, invasiveness, and required level of expertise. Results: Each ground truth method offers distinct benefits and drawbacks in relation to biological precision, invasiveness, availability, cost, and turnaround time. No single ground truth is universally optimal across all applications. Instead, the ideal choice depends on the intended clinical task, and practical alternatives exist to mitigate the constraints that result from limited time and institutional resources. Conclusions: The selection of ground truth for AI models in UM should be chosen based on the specific clinical task to balance predictive relevance with feasibility of implementation. The adoption of task-specific ground truth standards may improve the development of clinically meaningful AI tools and facilitate their integration into real-world practice. Full article
(This article belongs to the Special Issue Artificial Intelligence in Ocular Oncology)
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