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Abstract：
OBJECTIVE: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC.
METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model.
RESULTS: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM.
CONCLUSIONS: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.
METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model.
RESULTS: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM.
CONCLUSIONS: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.
摘要：
目的：预测肝内胆管癌(ICC)的淋巴结转移(LNM)对治疗方案和预后至关重要。我们旨在开发和验证基于机器学习（ML）的ICC患者LNM预测模型。
方法：共纳入2007年1月至2019年1月的345例临床病理特征证实为ICC的患者。通过最小绝对收缩和选择算子（LASSO）和逻辑分析确定LNM的预测因子。选定的变量用于通过六种ML算法开发LNM的预测模型，包括Logistic回归(LR)，梯度增压机（GBM），极端梯度提升(XGB)，随机森林(RF),决策树(DT)，多层感知器（MLP）。我们应用了10倍交叉验证作为内部验证，并计算了接收器工作特征（ROC）曲线下面积的平均值，以测量所有模型的性能。应用特征选择方法来识别每个模型中预测因子的重要性。热图用于研究特征的相关性。最后,我们使用性能最佳的模型建立了一个网络计算器。
结果：在多变量逻辑回归分析中，因素包括酒精性肝病（ALD），吸烟,边界,直径,和白细胞（WBC）被确定为ICC患者LNM的独立预测因子。在内部验证中，6个模型的AUC平均值为0.820~0.908.XGB模型被确定为最佳模型，平均AUC为0.908。最后,我们通过XGB模型建立了一个网络计算器，这对临床医生计算LNM的可能性很有用。
结论：提出的基于ML的预测模型在预测ICC患者的LNM方面具有良好的性能。XGB表现最好。基于ML算法的网络计算器有望帮助临床医生预测LNM并制定个性化医疗计划。
方法：共纳入2007年1月至2019年1月的345例临床病理特征证实为ICC的患者。通过最小绝对收缩和选择算子（LASSO）和逻辑分析确定LNM的预测因子。选定的变量用于通过六种ML算法开发LNM的预测模型，包括Logistic回归(LR)，梯度增压机（GBM），极端梯度提升(XGB)，随机森林(RF),决策树(DT)，多层感知器（MLP）。我们应用了10倍交叉验证作为内部验证，并计算了接收器工作特征（ROC）曲线下面积的平均值，以测量所有模型的性能。应用特征选择方法来识别每个模型中预测因子的重要性。热图用于研究特征的相关性。最后,我们使用性能最佳的模型建立了一个网络计算器。
结果：在多变量逻辑回归分析中，因素包括酒精性肝病（ALD），吸烟,边界,直径,和白细胞（WBC）被确定为ICC患者LNM的独立预测因子。在内部验证中，6个模型的AUC平均值为0.820~0.908.XGB模型被确定为最佳模型，平均AUC为0.908。最后,我们通过XGB模型建立了一个网络计算器，这对临床医生计算LNM的可能性很有用。
结论：提出的基于ML的预测模型在预测ICC患者的LNM方面具有良好的性能。XGB表现最好。基于ML算法的网络计算器有望帮助临床医生预测LNM并制定个性化医疗计划。
