Wu Jixuan, Zhang Chun, Zhang Youjia, He Rui, Wang Qin, Zhang Lei, Hu Jing, Wan Runlan
School of Pharmacy, Southwest Medical University, Luzhou, Sichuan 646000, China; Key Laboratory of Luzhou City for Aging Medicine, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
School of Public Health, Southwest Medical University, Luzhou 646000, China.
Cancer Epidemiol. 2025 Feb;94:102729. doi: 10.1016/j.canep.2024.102729. Epub 2024 Dec 15.
Distant metastasis in hepatocellular carcinoma (HCC) is an important indicator of poor patient prognosis. Identifying patients who are at high risk of metastasis early on is essential for creating personalized treatment plans, yet currently, there is a scarcity of effective predictive tools.
To investigate the effects of different factors on distant metastasis in HCC patients and to establish a clinical prediction model for predicting distant metastasis in HCC patients.
Our study retrospectively examined 22,318 patients diagnosed with confirmed HCC from the SEER database. Prognostic factors for developing distant metastases in HCC patients were identified by univariate and multivariate logistic regression analyses. Utilizing data from a multivariate logistic regression analysis, we created a nomogram. Its predictive precision was evaluated by analyzing the calibration curve, the area under the curve (AUC) of the receiver operating characteristic curve, decision curve assessment (DCA), and Kaplan-Meier (KM) curve analysis of overall survival. Finally,the nomogram was visualized with an online calculator.
We identified six independent prognostic factors: ethnicity, marital status, tumor size, survival time, surgery, and radiotherapy. The nomogram constructed from these six factors showed good calibration, discrimination, and clinical application value after calibration curve analysis, receiver operating characteristic curve analysis and DCA curve analysis. Besides, KaplanMeier survival curves also demonstrated that this nomogram had predictive accuracy.
In this research, a nomogram model was created to accurately predict distant metastasis risk in patients with HCC. This study provides guidance for optimizing individual therapies and making better clinical decisions.
肝细胞癌（HCC）的远处转移是患者预后不良的重要指标。尽早识别出具有高转移风险的患者对于制定个性化治疗方案至关重要，但目前缺乏有效的预测工具。
探讨不同因素对HCC患者远处转移的影响，并建立预测HCC患者远处转移的临床预测模型。
我们的研究回顾性分析了来自监测、流行病学和最终结果（SEER）数据库中确诊为HCC的22318例患者。通过单因素和多因素逻辑回归分析确定HCC患者发生远处转移的预后因素。利用多因素逻辑回归分析的数据，我们创建了一个列线图。通过分析校准曲线、受试者工作特征曲线下面积（AUC）、决策曲线评估（DCA）以及总生存的Kaplan-Meier（KM）曲线分析来评估其预测精度。最后，使用在线计算器将列线图可视化。
我们确定了六个独立的预后因素：种族、婚姻状况、肿瘤大小、生存时间、手术和放疗。经校准曲线分析、受试者工作特征曲线分析和DCA曲线分析后，由这六个因素构建的列线图显示出良好的校准、区分度和临床应用价值。此外，Kaplan-Meier生存曲线也表明该列线图具有预测准确性。
在本研究中，创建了一个列线图模型以准确预测HCC患者的远处转移风险。本研究为优化个体化治疗和做出更好的临床决策提供了指导。
