ArticleInvestigative ophthalmology & visual science2017
PRiMeUM: A Model for Predicting Risk of Metastasis in Uveal Melanoma.
Article in Investigative ophthalmology & visual science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Imaging-based machine learning for the diagnosis and prognosis of uveal melanoma: a systematic review and meta analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Automatic classification of uveal melanoma response patterns following ruthenium-106 plaque brachytherapy using ultrasound images and deep convolutional neural network.Scientific reports · 2025Article
- Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging.Translational vision science & technology · 2025Article
- Clinical Applications of Artificial Intelligence in Uveal Melanoma.Anticancer research · 2025Review
- State-of-the-art in Metastatic Uveal Melanoma Treatment: A 2025 Update : How to treat Metastatic Uveal Melanoma in 2025.Current oncology reports · 2025Review
- Genetic landscape of uveal melanoma in Southeast Asia: high 1q gains and unique patterns of metastasis risk.Eye and vision (London, England) · 2025Article
- Clinical Value of MLPA for Prognostic Assessment of Chromosomal Rearrangements and DNA Methylation in Uveal Melanoma.Investigative ophthalmology & visual science · 2025Article
- Clinical Implications of Ultrasound-Based Morphology in Choroidal Melanoma.Ophthalmology. Retina · 2025Article
- Significance of Aneuploidy in Predicting Prognosis and Treatment Response of Uveal Melanoma.Current medicinal chemistry · 2025Article
- Uveal Melanoma: Comprehensive Review of Its Pathophysiology, Diagnosis, Treatment, and Future Perspectives.Biomedicines · 2024Review
- Article
- Identification of multiomics map and key biomarkers in uveal melanoma with chromosome 3 loss.Annals of medicine and surgery (2012) · 2024Article
- Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis.Journal of cancer research and clinical oncology · 2024Article
- Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.Investigative ophthalmology & visual science · 2023Review
- A prognostic classification system for uveal melanoma based on a combination of patient age and sex, the American Joint Committee on Cancer and the Cancer Genome Atlas models.Acta ophthalmologica · 2023Article
- Future perspectives of uveal melanoma blood based biomarkers.British journal of cancer · 2022Review
- Improved Uveal Melanoma Copy Number Subtypes Including an Ultra-High-Risk Group.Ophthalmology science · 2022Article
- SLC25A38 as a novel biomarker for metastasis and clinical outcome in uveal melanoma.Cell death & disease · 2022Article
- Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15-year follow-up study.Cancer communications (London, England) · 2022Article
- Long-term relative survival in uveal melanoma: a systematic review and meta-analysis.Communications medicine · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Purpose: To create an interactive web-based tool for the Prediction of Risk of Metastasis in Uveal Melanoma (PRiMeUM) that can provide a personalized risk estimate of developing metastases within 48 months of primary uveal melanoma (UM) treatment. The model utilizes routinely collected clinical and tumor characteristics on 1227 UM, with the option of including chromosome information when available. Methods: Using a cohort of 1227 UM cases, Cox proportional hazard modeling was used to assess significant predictors of metastasis including clinical and chromosomal characteristics. A multivariate model to predict risk of metastasis was evaluated using machine learning methods including logistic regression, decision trees, survival random forest, and survival-based regression models. Based on cross-validation results, a logistic regression classifier was developed to compute an individualized risk of metastasis based on clinical and chromosomal information. Results: The PRiMeUM model provides prognostic information for personalized risk of metastasis in UM. The accuracy of the risk prediction ranged between 80% (using chromosomal features only), 83% using clinical features only (age, sex, tumor location, and size), and 85% (clinical and chromosomal information). Kaplan-Meier analysis showed these risk scores to be highly predictive of metastasis (P < 0.0001). Conclusions: PRiMeUM provides a tool for predicting an individual's personal risk of metastasis based on their individual and tumor characteristics. It will aid physicians with decisions concerning frequency of systemic surveillance and can be used as a criterion for entering clinical trials for adjuvant therapies.
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