Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000-2021).

Introduction: Ocular melanoma (OM) is a rare but lethal subtype of melanoma. This study develops a prognostic nomogram for OM using machine learning and internal validation techniques, aiming to improve prognosis prediction and clinical decision-making. Methods: Independent prognostic variables were identified using univariate and multivariate COX proportional hazard regression models. Significant variables were then incorporated into the nomogram. The predictive accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and 10-fold cross-validation. The performance of the nomogram was compared with that of a machine learning model. Results: Thirteen variables, including age, sex, tumor site, histologic subtype, stage, basal diameter size, tumor thickness, liver metastasis, first malignant primary indicator, marital status, and treatment modalities (surgery/radiotherapy/chemotherapy) were identified as independent prognostic factors for overall survival (OS) and were included in the nomogram (all Conclusions: A robust nomogram incorporating 13 significant clinicopathological variables was developed. The combined use of ROC curve analysis, calibration plots, DCA, 10-fold cross-validation, and machine learning confirmed the strong predictive performance of the nomogram for survival outcomes in patients with OM.