A Novel and Easy-to-Promote Prognostic Model for Patients With Uveal Melanoma

PurposeTo establish an easy and widely applicable prognostic prediction model for uveal melanoma (UM) based on a Chinese population. Patients and MethodsA total of 295 consecutive cases treated at the Eye & ENT Hospital of Fudan University were included as the primary cohort, and 256 cases were included in the validation cohorts from two external Caucasian databases. Clinicopathological data were collected retrospectively, and nomogram models were formulated based on multivariable analysis. The concordance index (C-index), AUC (area under the Receiver Operating Characteristic, ROC curve), and Brier score were calculated and compared. ResultsBased on the training cohort, a nomogram model was established with five relevant variables: age, tumor size, ciliary body involvement, non-spindle cell type and extra-scleral extension. The C-index was 0.737, the 3- and 5-year AUCs were 0.767 and 0.742, and the Brier scores for 3- and 5-year survival were 0.082 and 0.129, respectively, which showed superior prediction compared to that of the Tumor, Node and Metastasis staging system. The model also displayed good discrimination and calibration in the external validation cohorts. By risk stratification, patients could be divided into low- and high-risk groups, and the overall survival curves displayed significant differences in the training and validation cohorts. ConclusionOur nomogram model was simple and accurate at predicting the overall survival of patients with UM. It was established based on Asian patients and proved suitable for Caucasian patients; thus, it has a wide range of potential applications, especially for patients living in less medically developed countries and regions.

Automated summary

A simple and widely applicable prognostic prediction model for uveal melanoma (UM) in a Chinese population was established. The model, based on five relevant variables, showed superior prediction compared to traditional staging systems. The model also demonstrated good accuracy and stability in external validation cohorts.