Zheng Miyun, Xu Maodong, You Mengxing, Huang Zhiqing
Department of Ophthalmology, The First Hospital of Putian City, Putian, China.
The School of Clinical Medicine, Fujian Medical University, Fuzhou, China.
Front Med (Lausanne). 2025 Jan 29;12:1494925. doi: 10.3389/fmed.2025.1494925. eCollection 2025.
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.
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.
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 < 0.05). The nomogram showed a concordance index of 0.712. The areas under the curve (AUC) for predicting 3-, 5-, and 10-year survival rates were 0.749, 0.734, and 0.730, respectively. Calibration plots for 3-, 5-, and 10-year survival were in close agreement with the ideal predictions, and DCA indicated a superior net benefit. The average AUC from 10-fold cross-validation was 0.725. The machine-learning model identified liver metastasis as the most significant predictor of survival, followed by age, radiotherapy, stage, and other factors that were incorporated into the nomogram. The machine-learning model achieved a predictive AUC score of 0.750.
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.
眼黑色素瘤（OM）是黑色素瘤的一种罕见但致命的亚型。本研究使用机器学习和内部验证技术开发了一种OM预后列线图，旨在改善预后预测和临床决策。
使用单变量和多变量COX比例风险回归模型确定独立的预后变量。然后将显著变量纳入列线图。使用受试者工作特征（ROC）曲线、校准图、决策曲线分析（DCA）和10倍交叉验证评估列线图的预测准确性。将列线图的性能与机器学习模型的性能进行比较。
包括年龄、性别、肿瘤部位、组织学亚型、分期、基底直径大小、肿瘤厚度、肝转移、首个恶性原发指标、婚姻状况和治疗方式（手术/放疗/化疗）在内的13个变量被确定为总生存期（OS）的独立预后因素，并被纳入列线图（均P<0.05）。列线图的一致性指数为0.712。预测3年、5年和10年生存率的曲线下面积（AUC）分别为0.749、0.734和0.730。3年、5年和10年生存率的校准图与理想预测结果高度一致，DCA显示净效益更佳。10倍交叉验证的平均AUC为0.725。机器学习模型确定肝转移是生存的最重要预测因素，其次是年龄、放疗、分期和其他纳入列线图的因素。机器学习模型的预测AUC评分为0.750。
开发了一个包含13个重要临床病理变量的可靠列线图。ROC曲线分析、校准图、DCA、10倍交叉验证和机器学习的联合使用证实了该列线图对OM患者生存结局具有强大的预测性能。
