Fluid-conducting extracellular matrix patterns known as vasculogenic mimicry (VM) have been associated with poor prognosis in uveal melanoma and other cancers.,We investigate the correlations between VM, presenting symptoms, mortality, and the area density of periodic acid-Schiff positive histological patterns (PAS density).,Sixty-nine patients that underwent enucleation for uveal melanoma between 2000 and 2007 were included.,Clinicopathological parameters presenting symptoms and outcomes were collected.,Histological tumor sections were evaluated for VM and PAS density was quantified with digital image analysis.,Thirty-four patients (49%) presented with blurred vision. 18 (26%) with a shadow in the visual field, 7 (10%) with photopsia and/or floaters, and 2 (3%) with metamorphopsia.,Nine patients (13%) had no symptoms at all.,Median follow-up was 16.7 years (SD 2.6).,A shadow in the visual field, but no other symptom, was positively correlated with the presence of VM (φ 0.70, p < 0.001) and greater PAS density (p < 0.001).,In multivariate regression, retinal detachment (RD), presence of VM, and PAS density ≥ median were independent predictors of a shadow, but not tumor distance to the macula, tumor apical thickness, tumor diameter, or ciliary body engagement.,The presence of VM was associated with significantly shorter cumulative disease-specific survival (Wilcoxon p = 0.04), but not PAS density ≥ median, presenting symptoms or RD (p > 0.28).,Tumors from uveal melanoma patients that report a visual field shadow are likely to display VM and greater PAS density, likely explaining the previously reported association between this symptom and poor prognosis.
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.,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.,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).,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.