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Jorge Vaquero-Garcia, Emilie Lalonde, Kathryn G. Ewens, Jessica Ebrahimzadeh, Jennifer Richard-Yutz, Carol L. Shields, Alejandro Barrera, Christopher J. Green, Yoseph Barash, Arupa Ganguly; PRiMeUM: A Model for Predicting Risk of Metastasis in Uveal Melanoma. Invest. Ophthalmol. Vis. Sci. 2017;58(10):4096-4105. doi: https://doi.org/10.1167/iovs.17-22255.
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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.
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