Li Hailan, Wang Junbo, Ming Xin, Zhou Mingsha, Zhou Li
Department of Epidemiology, School of Public Health, Chongqing Medical University, Chongqing, China.
Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
Surg Open Sci. 2025 Jun 15;27:36-44. doi: 10.1016/j.sopen.2025.06.007. eCollection 2025 Sep.
With the development of conversion therapy, there has been a significant improvement in advanced stage hepatocellular carcinoma (HCC) patients' survival outcomes. Accurate prognostic assessment of patients with distant metastasis (DM) is therefore pivotal in improving quality of life, guiding treatment, and optimizing patient management.
This study extracted patients with distant metastatic HCC from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression were used to identify prognostic factors. Then, Cox regression, DeepSurv, Decision Tree, and Random Survival Forests models were used to predict overall survival. Model performance was evaluated by area under the curve (AUC), decision curve analysis, calibration curve, and Brier score. The visualization of Cox regression and machine learning algorithms utilized nomogram and Shapley additive explanations, respectively.
The study included 3051 HCC patients with DM. Factors such as tumor size, lung metastasis, N stage, ace, chemotherapy, radiotherapy, AFP, fibrosis, treatment interval, and number of metastases were independently associated with patient prognosis. Among all models, Cox regression and Random Survival Forest models showed stable performance, achieving AUCs of 0.746/0.760, 0.745/0.749, and 0.729/0.718 at 3, 6, and 12 months, respectively. Meanwhile, Cox regression showed the lowest Brier score (0.180 and 0.125) at 6 and 12 months.
Cox regression and Random Survival Forest models demonstrated robust prognostic performance for HCC, with Cox regression exhibiting superior temporal stability. The Cox-based nomogram provides an intuitive tool for rapid 3-, 6-, and 12-month survival stratification in metastatic HCC patients.
随着转化治疗的发展，晚期肝细胞癌（HCC）患者的生存结局有了显著改善。因此，准确评估远处转移（DM）患者的预后对于提高生活质量、指导治疗和优化患者管理至关重要。
本研究从监测、流行病学和最终结果数据库中提取远处转移性HCC患者。采用单因素和多因素Cox回归来确定预后因素。然后，使用Cox回归、DeepSurv、决策树和随机生存森林模型预测总生存期。通过曲线下面积（AUC）、决策曲线分析、校准曲线和Brier评分评估模型性能。Cox回归和机器学习算法的可视化分别采用列线图和Shapley相加解释法。
该研究纳入了3051例DM-HCC患者。肿瘤大小、肺转移、N分期、ace、化疗、放疗、甲胎蛋白、纤维化、治疗间隔和转移灶数量等因素与患者预后独立相关。在所有模型中，Cox回归和随机生存森林模型表现稳定，在3、6和12个月时的AUC分别为0.746/0.760、0.745/0.749和0.729/0.718。同时，Cox回归在6个月和12个月时的Brier评分最低（分别为0.180和0.125）。
Cox回归和随机生存森林模型对HCC显示出强大的预后性能，Cox回归表现出卓越的时间稳定性。基于Cox的列线图为转移性HCC患者快速进行3个月、6个月和12个月生存分层提供了直观工具。
