Fuxing Hospital, The Eighth Clinical Medical College, Capital Medical University, Beijing, People's Republic of China.
Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Jiangxi Branch of the National Clinical Research Center for Ocular Disease, Nanchang, People's Republic of China.
Cancer Med. 2023 Oct;12(20):20482-20496. doi: 10.1002/cam4.6540. Epub 2023 Oct 5.
Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML).
We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM.
Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA.
We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.
眼部转移（OM）是原发性肝癌（PLC）罕见的转移部位。本研究旨在基于机器学习（ML）建立 PLC 患者 OM 的临床预测模型。
我们回顾性收集了 1540 例 PLC 患者的临床资料，并按 7：3 的比例将其分为训练集和内部测试集。PLC 患者分为 OM 组和非眼部转移（NOM）组，对两组进行单因素 logistic 回归分析。选择单因素 logistic 分析 p<0.05 的变量进行 ML 模型。我们构建了六个 ML 模型，通过 10 倍交叉验证进行内部验证。通过受试者工作特征曲线（ROC）评估每个 ML 模型的预测性能。我们还基于最佳性能的 ML 模型构建了一个网络计算器，以个性化 OM 的风险概率。
选择了六个变量用于 ML 模型。极端梯度提升（XGB）ML 模型具有最佳的鉴别诊断能力，曲线下面积（AUC）=0.993、准确率=0.992、灵敏度=0.998、特异性=0.984。基于这些结果，我们使用 XGB ML 模型构建了一个在线网络计算器，以帮助临床医生诊断和治疗 PLC 患者 OM 的风险概率。最后，使用 Shapley 可加性解释（SHAP）库获得 PLC 患者 OM 的六个最重要的风险因素：CA125、ALP、AFP、TG、CA199 和 CEA。
我们使用 XGB 模型建立了 PLC 患者 OM 的风险预测模型。该预测模型可以帮助识别出 OM 风险较高的 PLC 患者，提供早期和个性化的诊断和治疗，降低 OM 患者的不良预后，并提高 PLC 患者的生活质量。
