Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Department of Ophthalmology, Xiang'an Hospital of Xiamen University, Xiamen, Fujian, China.
Cancer Sci. 2024 Sep;115(9):3107-3126. doi: 10.1111/cas.16276. Epub 2024 Jul 11.
Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.
葡萄膜黑色素瘤（UM）患者面临着远处转移的重大风险，这与预后不良密切相关。尽管如此，利用大数据预测 UM 远处转移的研究仍然很少。本研究利用机器学习方法对监测、流行病学和最终结果（SEER）数据库进行分析，以预测远处转移的风险概率。因此，SEER 数据库（2000-2020 年）中 UM 患者的信息根据是否发生远处转移分为 7:3 的训练集和内部测试集。单变量和多变量逻辑回归分析评估了远处转移的风险因素。六种机器学习方法在特征变量选择后构建了预测模型。通过模型评估，确定多层感知机（MLP）为最优模型。Shapley 加法解释（SHAP）对选定的模型进行了解释。一个基于网络的计算器为 UM 患者个性化计算了风险概率。结果表明，九个特征变量有助于机器学习模型。MLP 模型显示出较高的预测准确性（精度=0.788；ROC AUC=0.876；PR AUC=0.788）。分级重编码、年龄、原发部位、从诊断到治疗开始的时间以及恶性肿瘤总数被确定为远处转移的风险因素。诊断方法、侧别、城乡连续体代码和放射治疗重编码被确定为保护因素。开发的网络计算器利用 MLP 模型进行个性化风险评估。总之，MLP 机器学习模型是预测 UM 患者远处转移的最佳工具。该模型促进了个性化风险评估，有助于制定早期和针对性的治疗策略。
