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肝细胞癌肝切除术后晚期复发个体化预测模型(POLAR-HCC)的开发与验证:一项多中心研究

Development and Validation of an Individualized Prediction Model for Postoperative Late Recurrence After Hepatectomy for Hepatocellular Carcinoma (POLAR-HCC): A Multicenter Study.

作者信息

Xu Xin-Fei, Wu Han, Gu Li-Hui, Zhao Yu-Ze, Zhou Ya-Hao, Chen Ting-Hao, Guo Hong-Wei, Chen Zhong, Lin Kong-Ying, Gu Wei-Min, Wang Zi-Xuan, Wang Hong, Wang Xian-Ming, Diao Yong-Kang, Li Chao, Yao Lan-Qing, Wang Ming-Da, Pawlik Timothy M, Shen Feng, Yang Tian

机构信息

Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China.

Department of Hepatobiliary Surgery, Pu'er People's Hospital, Yunnan, China.

出版信息

Ann Surg Oncol. 2025 Sep 10. doi: 10.1245/s10434-025-18213-9.

Abstract

BACKGROUND

Postoperative late recurrence (POLAR) after 2 years from the date of surgical resection of hepatocellular carcinoma (HCC) represents a unique surveillance and management challenge. Despite identified risk factors, individualized prediction tools to guide personalized surveillance strategies for recurrence remain scarce. The current study sought to develop a predictive model for late recurrence among patients undergoing HCC resection.

METHODS

This multicenter study analyzed HCC patients who underwent resection across 10 Chinese hepatobiliary centers and remained recurrence-free at 2 years after hepatectomy. Patients were randomly assigned to development and validation cohorts (2:1 ratio). Independent predictors identified through multivariate Cox regression analysis were integrated into a nomogram and web-based calculator.

RESULTS

Among 849 recurrence-free patients at 2 years after hepatectomy for HCC, seven independent predictors of POLAR were identified: male (hazard ratio [HR] 1.37, p = 0.04), cirrhosis (HR 1.42, p = 0.008), multiple tumors (HR 1.56, p = 0.006), satellite nodules (HR 1.59, p = 0.004), large tumor size (HR 1.49, p = 0.009), macrovascular invasion (HR 4.63, p < 0.001), and microvascular invasion (HR 1.69, p = 0.001). The POLAR-HCC nomogram-based calculator demonstrated robust performance in both the development (area under the curve [AUC] 0.660) and validation (AUC 0.626) cohorts. Using the optimal cut-off value of 1.93, patients were accurately stratified into high- and low-risk groups with different risks of POLAR (p < 0.001).

CONCLUSIONS

The POLAR-HCC online calculator enables risk stratification for POLAR after HCC resection. By integrating tumor characteristics and host factors, this prediction tool identified high-risk patients who may benefit from intensified recurrence surveillance, potentially improving long-term survival through earlier detection of POLAR. The model represents an important step toward personalized surveillance strategies among patients undergoing HCC resection.

摘要

背景

肝细胞癌(HCC)手术切除术后2年出现的术后晚期复发(POLAR)是一个独特的监测和管理挑战。尽管已确定了风险因素,但用于指导个性化复发监测策略的个体化预测工具仍然稀缺。本研究旨在开发一种预测HCC切除术后患者晚期复发的模型。

方法

这项多中心研究分析了在中国10个肝胆中心接受手术切除且肝切除术后2年无复发的HCC患者。患者被随机分配到开发队列和验证队列(比例为2:1)。通过多变量Cox回归分析确定的独立预测因素被纳入到一个列线图和基于网络的计算器中。

结果

在849例HCC肝切除术后2年无复发的患者中,确定了7个POLAR的独立预测因素:男性(风险比[HR]1.37,p = 0.04)、肝硬化(HR 1.42,p = 0.008)、多发肿瘤(HR 1.56,p = 0.006)、卫星结节(HR 1.59,p = 0.004)、肿瘤体积大(HR 1.49,p = 0.009)、大血管侵犯(HR 4.63,p < 0.001)和微血管侵犯(HR 1.69,p = 0.001)。基于POLAR-HCC列线图的计算器在开发队列(曲线下面积[AUC]0.660)和验证队列(AUC 0.626)中均表现出良好的性能。使用最佳截断值1.93,患者被准确分层为具有不同POLAR风险的高风险组和低风险组(p < 0.001)。

结论

POLAR-HCC在线计算器能够对HCC切除术后的POLAR进行风险分层。通过整合肿瘤特征和宿主因素,该预测工具识别出可能从强化复发监测中获益的高风险患者,有可能通过早期检测POLAR提高长期生存率。该模型代表了HCC切除术后患者个性化监测策略迈出的重要一步。

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