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PRiMeUM: A Model for Predicting Risk of Metastasis in Uveal Melanoma
Jorge Vaquero-Garcia1, Emilie Lalonde1, Kathryn G Ewens1
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States.
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.
Insights
A new tool, Prediction of Risk of Metastasis in Uveal Melanoma (PRiMeUM), estimates metastasis risk in uveal melanoma patients. This personalized approach aids treatment decisions and clinical trial eligibility.
Area of Science:
- Ophthalmology
- Oncology
- Medical Informatics
Background:
- Uveal melanoma (UM) is the most common primary intraocular malignancy.
- Accurate prediction of metastasis is crucial for patient management and prognosis.
- Current prognostic models may not fully integrate all relevant clinical and genetic factors.
Purpose of the Study:
- To develop an interactive web-based tool, PRiMeUM, for personalized risk estimation of metastasis in UM patients.
- To provide a risk assessment within 48 months of primary UM treatment.
- To integrate routinely collected clinical and tumor characteristics, with optional chromosomal data.
Main Methods:
- A cohort of 1227 UM cases was analyzed using Cox proportional hazard modeling.
- Machine learning methods, including logistic regression and survival random forest, were employed to build a multivariate prediction model.
- A logistic regression classifier was developed based on cross-validation for individualized risk computation.
Main Results:
- The PRiMeUM model demonstrated strong predictive accuracy, achieving 85% with combined clinical and chromosomal data.
- Risk prediction accuracy was 83% using clinical features alone and 80% with chromosomal features only.
- Kaplan-Meier analysis confirmed the high predictability of metastasis risk scores (P < 0.0001).
Conclusions:
- PRiMeUM offers a valuable tool for predicting individual metastasis risk in UM based on patient and tumor characteristics.
- The tool can assist physicians in tailoring systemic surveillance frequency.
- PRiMeUM can serve as a criterion for patient selection in clinical trials for adjuvant therapies.
