PDF(Pubmed)
Abstract：
BACKGROUND: At present, liver transplantation (LT) is one of the best treatments for hepatocellular carcinoma (HCC). Accurately predicting the survival status after LT can significantly improve the survival rate after LT, and ensure the best way to make rational use of liver organs.
OBJECTIVE: To develop a model for predicting prognosis after LT in patients with HCC.
METHODS: Clinical data and follow-up information of 160 patients with HCC who underwent LT were collected and evaluated. The expression levels of alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, Golgi protein 73, cytokeratin-18 epitopes M30 and M65 were measured using a fully automated chemiluminescence analyzer. The best cutoff value of biomarkers was determined using the Youden index. Cox regression analysis was used to identify the independent risk factors. A forest model was constructed using the random forest method. We evaluated the accuracy of the nomogram using the area under the curve, using the calibration curve to assess consistency. A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomograms.
RESULTS: The total tumor diameter (TTD), vascular invasion (VI), AFP, and cytokeratin-18 epitopes M30 (CK18-M30) were identified as important risk factors for outcome after LT. The nomogram had a higher predictive accuracy than the Milan, University of California, San Francisco, and Hangzhou criteria. The calibration curve analyses indicated a good fit. The survival and recurrence-free survival (RFS) of high-risk groups were significantly lower than those of low- and middle-risk groups (P < 0.001). The DCA shows that the model has better clinical practicability.
CONCLUSIONS: The study developed a predictive nomogram based on TTD, VI, AFP, and CK18-M30 that could accurately predict overall survival and RFS after LT. It can screen for patients with better postoperative prognosis, and improve long-term survival for LT patients.
OBJECTIVE: To develop a model for predicting prognosis after LT in patients with HCC.
METHODS: Clinical data and follow-up information of 160 patients with HCC who underwent LT were collected and evaluated. The expression levels of alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, Golgi protein 73, cytokeratin-18 epitopes M30 and M65 were measured using a fully automated chemiluminescence analyzer. The best cutoff value of biomarkers was determined using the Youden index. Cox regression analysis was used to identify the independent risk factors. A forest model was constructed using the random forest method. We evaluated the accuracy of the nomogram using the area under the curve, using the calibration curve to assess consistency. A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomograms.
RESULTS: The total tumor diameter (TTD), vascular invasion (VI), AFP, and cytokeratin-18 epitopes M30 (CK18-M30) were identified as important risk factors for outcome after LT. The nomogram had a higher predictive accuracy than the Milan, University of California, San Francisco, and Hangzhou criteria. The calibration curve analyses indicated a good fit. The survival and recurrence-free survival (RFS) of high-risk groups were significantly lower than those of low- and middle-risk groups (P < 0.001). The DCA shows that the model has better clinical practicability.
CONCLUSIONS: The study developed a predictive nomogram based on TTD, VI, AFP, and CK18-M30 that could accurately predict overall survival and RFS after LT. It can screen for patients with better postoperative prognosis, and improve long-term survival for LT patients.
摘要：
背景：目前，肝移植（LT）是肝细胞癌（HCC）的最佳治疗方法之一。准确预测LT术后生存状态可显著提高LT术后生存率,并确保合理利用肝脏器官的最佳方法。
目的：建立预测肝癌患者肝移植后预后的模型。
方法：收集并评估了160例接受LT的HCC患者的临床数据和随访信息。甲胎蛋白（AFP）的表达水平，des-γ-羧基凝血酶原，使用全自动化学发光分析仪测量高尔基体蛋白73，细胞角蛋白18表位M30和M65。使用Youden指数确定生物标志物的最佳截止值。采用Cox回归分析确定独立危险因素。使用随机森林方法构建了森林模型。我们使用曲线下的面积来评估列线图的准确性，使用校准曲线评估一致性。使用决策曲线分析(DCA)来评价列线图的临床效用。
结果：肿瘤总直径（TTD），血管浸润（VI），法新社,和细胞角蛋白18表位M30（CK18-M30）被确定为LT后结局的重要危险因素。列线图比米兰有更高的预测准确性，加州大学,旧金山,和杭州标准。校准曲线分析表明良好的拟合。高危组患者的生存率和无复发生存率(RFS)明显低于低危组和中危组(P<0.001)。DCA表明该模型具有较好的临床实用性。
结论：该研究开发了基于TTD的预测列线图，VI,法新社,CK18-M30可以准确预测LT术后总生存率和RFS。它可以筛选术后预后较好的患者，并提高LT患者的长期生存率。
目的：建立预测肝癌患者肝移植后预后的模型。
方法：收集并评估了160例接受LT的HCC患者的临床数据和随访信息。甲胎蛋白（AFP）的表达水平，des-γ-羧基凝血酶原，使用全自动化学发光分析仪测量高尔基体蛋白73，细胞角蛋白18表位M30和M65。使用Youden指数确定生物标志物的最佳截止值。采用Cox回归分析确定独立危险因素。使用随机森林方法构建了森林模型。我们使用曲线下的面积来评估列线图的准确性，使用校准曲线评估一致性。使用决策曲线分析(DCA)来评价列线图的临床效用。
结果：肿瘤总直径（TTD），血管浸润（VI），法新社,和细胞角蛋白18表位M30（CK18-M30）被确定为LT后结局的重要危险因素。列线图比米兰有更高的预测准确性，加州大学,旧金山,和杭州标准。校准曲线分析表明良好的拟合。高危组患者的生存率和无复发生存率(RFS)明显低于低危组和中危组(P<0.001)。DCA表明该模型具有较好的临床实用性。
结论：该研究开发了基于TTD的预测列线图，VI,法新社,CK18-M30可以准确预测LT术后总生存率和RFS。它可以筛选术后预后较好的患者，并提高LT患者的长期生存率。
