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Abstract：
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.
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.
摘要：
背景：眼部转移（OM）是原发性肝癌（PLC）的罕见转移部位。目的建立基于机器学习(ML)的PLC患者OM临床预测模型。
方法：我们回顾性收集了1540名PLC患者的临床数据，并将其按7：3的比例分为训练集和内部测试集。将PLC患者分为OM组和非眼转移(NOM)组,两组间进行单因素logistic回归分析。对于ML模型选择具有单变量逻辑分析p<0.05的变量。我们构建了六个机器学习模型，通过10倍交叉验证进行了内部验证。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ML模型构建了一个网络计算器，以个性化OM的风险概率。
结果：为ML模型选择了六个变量。极端梯度增强(XGB)ML模型实现了最优鉴别诊断能力,曲线下面积（AUC）=0.993，准确性=0.992，敏感性=0.998，特异性=0.984。基于这些结果,使用XGBML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,Shapley加性解释（SHAP）库用于获得PLC患者OM的六个最重要的危险因素：CA125，ALP，法新社,TG,CA199和CEA。
结论：我们使用XGB模型建立了PLC患者OM的风险预测模型。预测模型可以帮助识别具有高OM风险的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，提高PLC患者的生活质量。
方法：我们回顾性收集了1540名PLC患者的临床数据，并将其按7：3的比例分为训练集和内部测试集。将PLC患者分为OM组和非眼转移(NOM)组,两组间进行单因素logistic回归分析。对于ML模型选择具有单变量逻辑分析p<0.05的变量。我们构建了六个机器学习模型，通过10倍交叉验证进行了内部验证。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ML模型构建了一个网络计算器，以个性化OM的风险概率。
结果：为ML模型选择了六个变量。极端梯度增强(XGB)ML模型实现了最优鉴别诊断能力,曲线下面积（AUC）=0.993，准确性=0.992，敏感性=0.998，特异性=0.984。基于这些结果,使用XGBML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,Shapley加性解释（SHAP）库用于获得PLC患者OM的六个最重要的危险因素：CA125，ALP，法新社,TG,CA199和CEA。
结论：我们使用XGB模型建立了PLC患者OM的风险预测模型。预测模型可以帮助识别具有高OM风险的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，提高PLC患者的生活质量。
