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肝切除术后肝细胞癌复发预测列线图

Nomogram for prediction of hepatocellular carcinoma recurrence after liver resection.

作者信息

Zhang Zhi-Jun, Wei Ba-Jin, Liu Zhi-Kun, Xuan Ze-Feng, Zhou Lin, Zheng Shu-Sen

机构信息

Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; NHC Key Laboratory of Combined Multi-organ Transplantation, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.

NHC Key Laboratory of Combined Multi-organ Transplantation, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; Division of Breast Surgery, Department of Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.

出版信息

Hepatobiliary Pancreat Dis Int. 2025 Jun;24(3):269-276. doi: 10.1016/j.hbpd.2024.09.006. Epub 2024 Sep 19.

DOI:10.1016/j.hbpd.2024.09.006

PMID:39332935

Abstract

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignancy with high mortality. Liver resection (LR) is a curative treatment for early-stage HCC, but the prognosis of HCC patients after LR is unsatisfactory because of tumor recurrence. Prognostic prediction models with great performance are urgently needed. The present study aimed to establish a novel prognostic nomogram to predict tumor recurrence in HCC patients after LR.

METHODS

We retrospectively analyzed 726 HCC patients who underwent LR between October 2011 and December 2016. Patients were randomly divided into the training cohort (n = 508) and the testing cohort (n = 218). The protein expression of 14 biomarkers in tumor tissues was assessed by immunohistochemistry. The nomogram predicting recurrence-free survival (RFS) was established by a multivariate Cox regression analysis model and was evaluated by calibration curves, Kaplan-Meier survival curves, time-dependent areas under the receiver operating characteristic (ROC) curves (AUCs), and decision curve analyses in both the training and testing cohorts.

RESULTS

Alpha-fetoprotein [hazard ratio (HR) = 1.013, P = 0.002], portal vein tumor thrombosis (HR = 1.833, P < 0.001), ascites (HR = 2.024, P = 0.014), tumor diameter (HR = 1.075, P < 0.001), E-cadherin (HR = 0.859, P = 0.011), EMA (HR = 1.196, P = 0.022), and PCNA (HR = 1.174, P = 0.031) immunohistochemistry scores were found to be independent factors for RFS. The 1-year and 3-year AUCs of the nomogram for RFS were 0.813 and 0.739, respectively. The patients were divided into the high-risk group and the low-risk group by median value which was generated from the nomogram, and Kaplan-Meier analysis revealed that the high-risk group had a shorter RFS than the low-risk group in both the training (P < 0.001) and testing cohorts (P < 0.001).

CONCLUSIONS

Our newly developed nomogram integrated clinicopathological data and key gene expression data, and was verified to have high accuracy in predicting the RFS of HCC patients after LR. This model could be used for early identification of patients at high-risk of postoperative recurrence.

摘要

背景

肝细胞癌（HCC）是一种常见的恶性肿瘤，死亡率很高。肝切除术（LR）是早期HCC的一种根治性治疗方法，但由于肿瘤复发，LR术后HCC患者的预后并不理想。迫切需要性能优异的预后预测模型。本研究旨在建立一种新型预后列线图，以预测LR术后HCC患者的肿瘤复发情况。

方法

我们回顾性分析了2011年10月至2016年12月期间接受LR的726例HCC患者。患者被随机分为训练队列（n = 508）和测试队列（n = 218）。通过免疫组织化学评估肿瘤组织中14种生物标志物的蛋白表达。通过多变量Cox回归分析模型建立预测无复发生存期（RFS）的列线图，并在训练队列和测试队列中通过校准曲线、Kaplan-Meier生存曲线、受试者操作特征（ROC）曲线下的时间依赖性面积（AUC）以及决策曲线分析进行评估。

结果

甲胎蛋白[风险比（HR）= 1.013，P = 0.002]、门静脉肿瘤血栓形成（HR = 1.833，P < 0.001）、腹水（HR = 2.024，P = 0.014）、肿瘤直径（HR = 1.075，P < 0.001）、E-钙黏蛋白（HR = 0.859，P = 0.011）、EMA（HR = 1.196，P = 0.022）和PCNA（HR = 1.174，P = 0.031）免疫组织化学评分被发现是RFS的独立因素。RFS列线图的1年和3年AUC分别为0.813和0.739。根据列线图产生的中位数将患者分为高风险组和低风险组，Kaplan-Meier分析显示，在训练队列（P < 0.001）和测试队列（P < 0.001）中，高风险组的RFS均短于低风险组。