{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T03:21:39Z","timestamp":1790824899642,"version":"4.1.0"},"reference-count":71,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"crossref","award":["P30EY001792"],"award-info":[{"award-number":["P30EY001792"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"crossref","award":["K12EY021475"],"award-info":[{"award-number":["K12EY021475"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"crossref"}]},{"award":["P30EY001792"],"award-info":[{"award-number":["P30EY001792"]}],"id":[{"id":"https:\/\/ror.org\/03wkg3b53","id-type":"ROR","asserted-by":"publisher"}]},{"award":["K12EY021475"],"award-info":[{"award-number":["K12EY021475"]}],"id":[{"id":"https:\/\/ror.org\/03wkg3b53","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/04drjs621","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100002224","name":"Melanoma Research Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100002224","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/00wyk1t96","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018133","name":"VitreoRetinal Surgery Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018133","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100017982","name":"Illinois Society for the Prevention of Blindness","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017982","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/0112vdv20","id-type":"ROR","asserted-by":"publisher"}]},{"name":"University of Illinois Chicago Cancer Center"},{"id":[{"id":"https:\/\/ror.org\/02mpq6x41","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/cancers18091357","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T12:29:11Z","timestamp":1777033751000},"page":"1357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Uveal Melanoma Ground Truth Labeling in Machine Learning"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7682-3313","authenticated-orcid":false,"given":"Emily","family":"Kao","sequence":"first","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9694-594X","authenticated-orcid":false,"given":"Sanjay","family":"Ganesh","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William F.","family":"Chadwick","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0254-6284","authenticated-orcid":false,"given":"Reem","family":"Alahmadi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0356-3242","authenticated-orcid":false,"given":"Xincheng","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL 60607, USA"},{"name":"Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-0164","authenticated-orcid":false,"given":"Michael J.","family":"Heiferman","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.aopr.2024.10.001","article-title":"Global Incidence and Prevalence in Uveal Melanoma","volume":"4","author":"Hou","year":"2024","journal-title":"Adv. Ophthalmol. Pract. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s43856-022-00082-y","article-title":"Long-Term Relative Survival in Uveal Melanoma: A Systematic Review and Meta-Analysis","volume":"2","author":"Herrspiegel","year":"2022","journal-title":"Commun. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.4103\/0301-4738.154367","article-title":"Uveal Melanoma: Estimating Prognosis","volume":"63","author":"Kaliki","year":"2015","journal-title":"Indian J. Ophthalmol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1002\/cncr.29727","article-title":"Uveal Melanoma: From Diagnosis to Treatment and the Science in Between","volume":"122","author":"Chattopadhyay","year":"2016","journal-title":"Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hanratty, K., Finegan, G., Rochfort, K.D., and Kennedy, S. (2025). Current Treatment of Uveal Melanoma. Cancers, 17.","DOI":"10.3390\/cancers17091403"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1097\/00006982-200509000-00013","article-title":"Pseudomelanomas of the Posterior Uveal Tract: The 2006 Taylor R. Smith Lecture","volume":"25","author":"Shields","year":"2005","journal-title":"Retina"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e188","DOI":"10.7861\/fhj.2021-0095","article-title":"Artificial Intelligence in Healthcare: Transforming the Practice of Medicine","volume":"8","author":"Bajwa","year":"2021","journal-title":"Future Healthc. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7\u201312 June 2015, IEEE.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100148","DOI":"10.1016\/j.cmpbup.2024.100148","article-title":"Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions","volume":"5","author":"Khalifa","year":"2024","journal-title":"Comput. Methods Programs Biomed. Update"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100584","DOI":"10.1016\/j.xops.2024.100584","article-title":"Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning","volume":"5","author":"Tailor","year":"2025","journal-title":"Ophthalmol. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, T.Y.A., Chen, H., Koseoglu, N.D., Kolchinski, A., Unberath, M., and Correa, Z.M. (2025). Direct Prediction of 48 Month Survival Status in Patients with Uveal Melanoma Using Deep Learning and Digital Cytopathology Images. Cancers, 17.","DOI":"10.3390\/cancers17020230"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Iddir, S.P., Love, J., Ma, J.S., Bryan, J.M., Ganesh, S., Heiferman, M.J., and Yi, D. (2023). Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning. Res. Sq., rs.3.rs-3778562.","DOI":"10.21203\/rs.3.rs-3778562\/v1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8528","DOI":"10.1038\/s41598-026-37070-4","article-title":"Interpretation Drift in Explainable AI under Label Noise","volume":"16","author":"Raikovskaia","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.2147\/OPTH.S472064","article-title":"Geographic Patterns of Ocular Oncologist Supply and Patient Demand for Uveal Melanoma Treatment in the United States: A Supply and Demand Analysis","volume":"18","author":"Lieu","year":"2024","journal-title":"Clin. Ophthalmol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1167\/tvst.14.10.35","article-title":"Uveal Pigmented Lesion Classification, Detection, and Segmentation: A Comparative Analysis of Machine Learning Tasks","volume":"14","author":"Tasso","year":"2025","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1038\/s41433-024-03148-4","article-title":"Colour Fusion Effect on Deep Learning Classification of Uveal Melanoma","volume":"38","author":"Dadzie","year":"2024","journal-title":"Eye"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Krig, S. (2014). Ground Truth Data, Content, Metrics, and Analysis. Computer Vision Metrics: Survey, Taxonomy, and Analysis, Apress.","DOI":"10.1007\/978-1-4302-5930-5"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e784","DOI":"10.1016\/j.jcjo.2024.04.003","article-title":"Automated Segmentation for Early Detection of Uveal Melanoma","volume":"59","author":"Ma","year":"2024","journal-title":"Can. J. Ophthalmol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1038\/s41586-024-07894-z","article-title":"A Pathology Foundation Model for Cancer Diagnosis and Prognosis Prediction","volume":"634","author":"Wang","year":"2024","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e338","DOI":"10.1016\/S2589-7500(23)00070-5","article-title":"Clinical Ground Truth in Machine Learning for Early Sepsis Diagnosis","volume":"5","author":"Lindner","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2856","DOI":"10.1007\/s11606-024-08844-1","article-title":"Pyrite Standards: Medical Uncertainty, Ground Truth, and AI Model Evaluation in Historical Perspective","volume":"39","author":"Lea","year":"2024","journal-title":"J. Gen. Intern. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20539517251352823","DOI":"10.1177\/20539517251352823","article-title":"Ground-Truth Is Law: The Invisible Conceptual Work behind AI","volume":"12","year":"2025","journal-title":"Big Data Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e693","DOI":"10.1016\/S2589-7500(21)00216-8","article-title":"Evaluation of Artificial Intelligence on a Reference Standard Based on Subjective Interpretation","volume":"3","author":"Chen","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1097\/XCS.0000000000001044","article-title":"In Search of the Truth: Choice of Ground Truth for Predictive Modeling of Trauma Team Activation in Pediatric Trauma","volume":"239","author":"Chacon","year":"2024","journal-title":"J. Am. Coll. Surg."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4669","DOI":"10.21873\/anticanres.17817","article-title":"Clinical Applications of Artificial Intelligence in Uveal Melanoma","volume":"45","author":"Chadwick","year":"2025","journal-title":"Anticancer Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4274\/tjo.37431","article-title":"Uveal Melanoma: Current Trends in Diagnosis and Management","volume":"46","author":"Tarlan","year":"2016","journal-title":"Turk. J. Ophthalmol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Collaborative Ocular Melanoma Study Group (1990). Accuracy of Diagnosis of Choroidal Melanomas in the Collaborative Ocular Melanoma Study: COMS Report No. 1. Arch. Ophthalmol., 108, 1268\u20131273.","DOI":"10.1001\/archopht.1990.01070110084030"},{"key":"ref_29","unstructured":"(2026, February 19). Updated AJCC Classification for Posterior Uveal Melanoma. Available online: https:\/\/retinatoday.com\/articles\/2018-may-june\/updated-ajcc-classification-for-posterior-uveal-melanoma."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.1167\/iovs.07-1603","article-title":"Size Overlap between Benign Melanocytic Choroidal Nevi and Choroidal Malignant Melanomas","volume":"49","author":"Augsburger","year":"2008","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1136\/bjo.74.3.183","article-title":"Ultrasonographic Measurement of Uveal Melanoma Thickness: Interobserver Variability","volume":"74","author":"Char","year":"1990","journal-title":"Br. J. Ophthalmol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1159\/000516536","article-title":"What\u2019s in a Name? Large Choroidal Nevus, Small Choroidal Melanoma, or Indeterminate Melanocytic Tumor","volume":"7","author":"Singh","year":"2021","journal-title":"Ocul. Oncol. Pathol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Collaborative Ocular Melanoma Study Group (1998). Histopathologic Characteristics of Uveal Melanomas in Eyes Enucleated from the Collaborative Ocular Melanoma Study COMS Report No. 6. Am. J. Ophthalmol., 125, 745\u2013766.","DOI":"10.1016\/S0002-9394(98)00040-3"},{"key":"ref_34","first-page":"6541","article-title":"Clinical, Histopathological and Cytogenetic Prognosticators in Uveal Melanoma\u2014A Comprehensive Review","volume":"37","author":"Berus","year":"2017","journal-title":"Anticancer Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110","DOI":"10.4103\/0301-4738.154373","article-title":"Using Risk Factors for Detection and Prognostication of Uveal Melanoma","volume":"63","author":"Rishi","year":"2015","journal-title":"Indian J. Ophthalmol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/eye.1992.3","article-title":"The Uses and Limitations of Intraocular Biopsy","volume":"6","author":"Foulds","year":"1992","journal-title":"Eye"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lou, H., Yue, H., Qian, J., Bi, Y., Lin, X., Chen, H., Xu, B., Ma, R., Xue, K., and Guo, J. (2025). Insights into Shape of Uveal Melanoma: A Comprehensive Evaluation of Clinical Features, Pathological Features, and Prognosis Analysis. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1687291"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3319","DOI":"10.1200\/JCO.24.00447","article-title":"15-Gene Expression Profile and PRAME as Integrated Prognostic Test for Uveal Melanoma: First Report of Collaborative Ocular Oncology Group Study No. 2 (COOG2.1)","volume":"42","author":"Harbour","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1159\/000508382","article-title":"Gene Expression Profiling in Uveal Melanoma: Five-Year Prospective Outcomes and Meta-Analysis","volume":"6","author":"Aaberg","year":"2020","journal-title":"Ocul. Oncol. Pathol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1016\/j.ophtha.2012.02.017","article-title":"Collaborative Ocular Oncology Group Report Number 1: Prospective Validation of a Multi-Gene Prognostic Assay in Uveal Melanoma","volume":"119","author":"Onken","year":"2012","journal-title":"Ophthalmology"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Scott, J.F., and Gerstenblith, M.R. (2018). Genetics of Uveal Melanoma. Noncutaneous Melanoma, Codon Publications.","DOI":"10.15586\/codon.noncutaneousmelanoma.2018"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.ophtha.2023.01.020","article-title":"Validation of the Prognostic Usefulness of the Gene Expression Profiling Test in Patients with Uveal Melanoma","volume":"130","author":"Miguez","year":"2023","journal-title":"Ophthalmology"},{"key":"ref_43","unstructured":"(2026, February 19). Enabling Personalized Medicine in the Management of Uveal Melanoma. Available online: https:\/\/retinatoday.com\/articles\/2013-nov-dec\/enabling-personalized-medicine-in-the-management-of-uveal-melanoma."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cross, J.L., Choma, M.A., and Onofrey, J.A. (2024). Bias in Medical AI: Implications for Clinical Decision-Making. PLoS Digit. Health, 3.","DOI":"10.1371\/journal.pdig.0000651"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13000-017-0650-3","article-title":"Gene Expression Profiling in Uveal Melanoma: Technical Reliability and Correlation of Molecular Class with Pathologic Characteristics","volume":"12","author":"Plasseraud","year":"2017","journal-title":"Diagn. Pathol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1038\/s41433-022-02143-x","article-title":"Can the MOLES Acronym and Scoring System Improve the Management of Patients with Melanocytic Choroidal Tumours?","volume":"37","author":"Damato","year":"2023","journal-title":"Eye"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Roelofs, K.A., O\u2019Day, R., Harby, L.A., Arora, A.K., Cohen, V.M.L., Sagoo, M.S., and Damato, B. (2020). The MOLES System for Planning Management of Melanocytic Choroidal Tumors: Is It Safe?. Cancers, 12.","DOI":"10.3390\/cancers12051311"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"28534","DOI":"10.1038\/s41598-024-78692-w","article-title":"Performance of the MOLES and TFSOM-DIM Scores in Classifying Choroidal Nevi and Melanoma","volume":"14","author":"Jahnke","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1007\/s11548-025-03457-3","article-title":"Streamlining the Annotation Process by Radiologists of Volumetric Medical Images with Few-Shot Learning","volume":"20","author":"Ryabtsev","year":"2025","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s10792-025-03624-0","article-title":"Long-Term Outcome after Treatment of Large Uveal Melanoma","volume":"45","author":"Jabbarli","year":"2025","journal-title":"Int. Ophthalmol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.breast.2012.09.009","article-title":"Costs of Different Follow-up Strategies in Early Breast Cancer: A Review of the Literature","volume":"21","author":"Scholten","year":"2012","journal-title":"Breast"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4218","DOI":"10.1158\/1078-0432.CCR-05-2821","article-title":"Circulating Tumor Cells at Each Follow-up Time Point during Therapy of Metastatic Breast Cancer Patients Predict Progression-Free and Overall Survival","volume":"12","author":"Hayes","year":"2006","journal-title":"Clin. Cancer Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1056\/NEJMc2104626","article-title":"The Clinician and Dataset Shift in Artificial Intelligence","volume":"385","author":"Finlayson","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1038\/s41433-026-04264-z","article-title":"Validation of MOLES Score for Small Choroidal Melanomas: Impact of Assuming Enlargement for Telemedicine in Sizable Lesions","volume":"40","author":"Gallo","year":"2026","journal-title":"Eye"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1504\/IJBET.2012.045355","article-title":"Enhancing Survival Prognostication in Patients with Choroidal Melanoma by Integrating Pathologic, Clinical and Genetic Predictors of Metastasis","volume":"8","author":"Eleuteri","year":"2012","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1007\/s00262-021-03090-4","article-title":"Clinical Determinants of Long-Term Survival in Metastatic Uveal Melanoma","volume":"71","author":"Koch","year":"2022","journal-title":"Cancer Immunol. Immunother."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Dadzie, A.K., Iddir, S.P., Ganesh, S., Ebrahimi, B., Rahimi, M., Abtahi, M., Son, T., Heiferman, M.J., and Yao, X. (2025). Artificial Intelligence in the Diagnosis of Uveal Melanoma: Advances and Applications. Exp. Biol. Med., 250.","DOI":"10.3389\/ebm.2025.10444"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ajo.2014.10.026","article-title":"Frequency and Implications of Discordant Gene Expression Profile Class in Posterior Uveal Melanomas Sampled by Fine Needle Aspiration Biopsy","volume":"159","author":"Augsburger","year":"2015","journal-title":"Am. J. Ophthalmol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zako, C., Nisanova, A., Weinberg, V., Scholey, J., Swason, C., Afshar, A.R., Quivey, J., Daftari, I.K., Tsai, T., and Park, S.S. (2025). Long-Term Clinical Outcomes for Adolescent and Young-Adult Uveal Melanoma Patients Treated with Dedicated Particle-Beam Radiation. Cancers, 17.","DOI":"10.3390\/cancers17122042"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cennamo, G., Montorio, D., D\u2019 Andrea, L., Farella, A., Matano, E., Giuliano, M., Liuzzi, R., Breve, M.A., De Placido, S., and Cennamo, G. (2022). Long-Term Outcomes in Uveal Melanoma After Ruthenium-106 Brachytherapy. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.754108"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Damato, B., Eleuteri, A., Taktak, A., Hussain, R., Fili, M., St\u00e5lhammar, G., Heimann, H., and Coupland, S.E. (2024). Deferral of Treatment for Small Choroidal Melanoma and the Risk of Metastasis: An Investigation Using the Liverpool Uveal Melanoma Prognosticator Online (LUMPO). Cancers, 16.","DOI":"10.3390\/cancers16081607"},{"key":"ref_62","first-page":"120","article-title":"NCCN Guidelines Insights: Uveal Melanoma, Version 1.2019","volume":"18","author":"Rao","year":"2020","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1177\/17407745251358235","article-title":"Automated Data Collection from an Electronic Medical Record for a Prospective Real-World Study in Patients with Retinal Disease (VOYAGER)","volume":"22","author":"Bailey","year":"2025","journal-title":"Clin. Trials"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"104496","DOI":"10.1016\/j.esmoop.2025.104496","article-title":"How We Treat Patients with Metastatic Uveal Melanoma","volume":"10","author":"Saldanha","year":"2025","journal-title":"ESMO Open"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TRPMS.2021.3107454","article-title":"Artificial Intelligence in Radiation Therapy","volume":"Volume 6","author":"Fu","year":"2022","journal-title":"IEEE Transactions on Radiation and Plasma Medical Sciences"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.jaad.2023.08.105","article-title":"Development and Validation of Time-to-Event Models to Predict Metastatic Recurrence of Localized Cutaneous Melanoma","volume":"90","author":"Wan","year":"2024","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1097\/EDE.0000000000000564","article-title":"Biases in Randomized Trials: A Conversation between Trialists and Epidemiologists","volume":"28","author":"Mansournia","year":"2017","journal-title":"Epidemiology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"MMT62","DOI":"10.2217\/mmt-2022-0003","article-title":"Uveal Melanoma Patient Attitudes Towards Prognostic Testing Using Gene Expression Profiling","volume":"9","author":"Williams","year":"2022","journal-title":"Melanoma Manag."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wan, Q., Hou, C., Wei, R., Zhang, M., Yan, N., Ma, K., and Deng, Y. (2025). Histopathological Images-Based Deep-Learning for Risk Stratification of Uveal Melanoma. Chin. Med. J.","DOI":"10.1097\/CM9.0000000000003739"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1097\/PPO.0b013e31824bd256","article-title":"Metastatic Uveal Melanoma: Biology and Emerging Treatments","volume":"18","author":"Woodman","year":"2012","journal-title":"Cancer J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4597","DOI":"10.2147\/OPTH.S561118","article-title":"Effect of Social Determinants of Health and Geography on Uveal Melanoma","volume":"19","author":"Sureshkumar","year":"2025","journal-title":"Clin. Ophthalmol."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/9\/1357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T04:12:27Z","timestamp":1777176747000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/9\/1357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":71,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["cancers18091357"],"URL":"https:\/\/doi.org\/10.3390\/cancers18091357","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]}}}