PDF(Pubmed)
Abstract：
UNASSIGNED: Identifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy.
UNASSIGNED: We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy.
UNASSIGNED: A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model.
UNASSIGNED: MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.
UNASSIGNED: We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy.
UNASSIGNED: A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model.
UNASSIGNED: MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.
摘要：
■确定肝细胞癌（HCC）患者肝切除术后复发的高风险，有助于及时实施介入治疗。本研究旨在开发一种机器学习（ML）模型来预测肝癌患者肝切除术后的复发风险。
■我们回顾性收集了2013年4月至2017年10月在中山大学附属第三医院接受根治性肝切除术的315例HCC患者，并以7:3的比例随机分为训练集和验证集。根据HCC患者术后复发情况,将患者分为复发组和未复发组,并对两组进行单因素和多因素logistic回归。我们应用了六种机器学习算法来构建预测模型，并通过10倍交叉验证进行了内部验证。Shapley加性解释(SHAP)方法用于解释机器学习模型。我们还建立了一个基于最佳机器学习模型的网络计算器，以个性化评估肝癌患者肝切除术后的复发风险。
■机器学习模型中包含了总共13个变量。多层感知器（MLP）机器学习模型在测试集（AUC=0.680）中具有最佳预测值。SHAP方法显示γ-谷氨酰转肽酶(GGT)，纤维蛋白原，中性粒细胞,谷草转氨酶（AST）和总胆红素（TB）是肝癌患者肝切除术后复发风险的前5个重要因素。此外,我们通过分析两名患者进一步证明了模型的可靠性.最后,我们成功构建了基于MLP机器学习模型的在线网络预测计算器。
■MLP是预测肝癌患者肝切除术后复发风险的最佳机器学习模型。该预测模型可以帮助识别肝切除术后高复发风险的HCC患者，以提供早期和个性化的治疗。
■我们回顾性收集了2013年4月至2017年10月在中山大学附属第三医院接受根治性肝切除术的315例HCC患者，并以7:3的比例随机分为训练集和验证集。根据HCC患者术后复发情况,将患者分为复发组和未复发组,并对两组进行单因素和多因素logistic回归。我们应用了六种机器学习算法来构建预测模型，并通过10倍交叉验证进行了内部验证。Shapley加性解释(SHAP)方法用于解释机器学习模型。我们还建立了一个基于最佳机器学习模型的网络计算器，以个性化评估肝癌患者肝切除术后的复发风险。
■机器学习模型中包含了总共13个变量。多层感知器（MLP）机器学习模型在测试集（AUC=0.680）中具有最佳预测值。SHAP方法显示γ-谷氨酰转肽酶(GGT)，纤维蛋白原，中性粒细胞,谷草转氨酶（AST）和总胆红素（TB）是肝癌患者肝切除术后复发风险的前5个重要因素。此外,我们通过分析两名患者进一步证明了模型的可靠性.最后,我们成功构建了基于MLP机器学习模型的在线网络预测计算器。
■MLP是预测肝癌患者肝切除术后复发风险的最佳机器学习模型。该预测模型可以帮助识别肝切除术后高复发风险的HCC患者，以提供早期和个性化的治疗。
