Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma.

Since molecular assays are not accessible to all uveal melanoma patients, we aim to identify cost-effective prognostic tool in risk stratification using machine learning models based on routine histologic and clinical variables. We identified important prognostic parameters in a discovery cohort of 164 enucleated primary uveal melanomas from 164 patients without prior therapies. We then validated the prognostic prediction of top important parameters identified in the discovery cohort using 80 uveal melanomas from the Tumor Cancer Genome Atlas database with available gene expression prognostic signature (GEPS). The performance of three different survival analysis models (Cox proportional hazards (CPH), random survival forest (RSF), and survival gradient boosting (SGB)) was compared against GEPS using receiver operating curves (ROC). In all three selection methods, BAP1 status, nucleoli size, age, mitotic rate per 1 mm Our study shows that routine histologic and clinical variables are adequate for patient risk stratification in comparison with not readily accessible GEPS.

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Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.