预测接受TACE联合靶向免疫治疗的中晚期肝细胞癌的肿瘤进展
Prediction of tumor progression in intermediate and advanced hepatocellular carcinoma undergoing TACE combined with targeted immunotherapy.
摘要(中文译文)
经导管动脉化疗栓塞（TACE）联合靶向免疫治疗已成为中晚期肝细胞癌（HCC）的标准一线治疗策略，但部分患者仍无法从该治疗中获益。本研究旨在基于HCC患者队列构建预测模型，以指导个体化治疗决策。回顾性纳入2019年1月1日至2024年3月31日期间接受TACE联合靶向免疫治疗的243例中晚期HCC患者。通过Cox回归分析和最小绝对收缩和选择算子（LASSO）回归筛选最佳预后因素。基于筛选出的危险因素构建预测6个月、12个月和24个月影像学无进展生存概率的列线图模型，并通过校准曲线、决策曲线分析和限制性立方样条分析评估模型性能。多因素Cox分析显示，总胆红素、D-二聚体（DD）和门静脉癌栓（PVTT）是影响患者肿瘤进展的独立危险因素。LASSO回归筛选出10个关键预后因素：天冬氨酸氨基转移酶、总胆红素、白蛋白（ALB）、白细胞计数、DD、甲胎蛋白、CD4+ T细胞计数、PVTT、肿瘤数目（≥3）和淋巴结侵犯。与Cox回归模型相比，LASSO回归模型在筛选预后因素方面表现出显著优势。因此，选择LASSO回归识别的危险因素构建列线图预测模型进行后续分析。该模型在训练组和验证组中均显示出良好的区分能力（Log rank P<0.05）、校准能力和临床策略的净获益。限制性立方样条分析发现，ALB水平和DD与患者预后呈显著非线性相关。本研究构建了中晚期HCC患者肿瘤进展的多维度预测模型，有助于筛选最佳获益人群并优化治疗策略。
Abstract
Transcatheter arterial chemoembolization (TACE) combined with targeted immunotherapy have become the standard first-line treatment strategy for intermediate and advanced hepatocellular carcinoma (HCC), but some patients still cannot benefit from this treatment. This study aimed to construct a prediction model based on a cohort of HCC patients to guide individualized treatment decisions. A total of 243 intermediate and advanced HCC patients who received TACE combined with targeted immunotherapy from January 1, 2019 to March 31, 2024 were retrospectively enrolled. The optimal prognostic factors were screened by Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression. A nomogram model for predicting the probability of radiologic progression-free survival at 6-, 12-, and 24-month was constructed based on the screened risk factors, and the model performance was evaluated by calibration curve, decision curve analysis, and restricted cubic spline analysis. Multivariate COX analysis showed that total bilirubin, D-dimer (DD) and portal vein tumor thrombosis (PVTT) were independent risk factors affecting the tumor progression of patients. LASSO regression screened out 10 key prognostic factors: aspartate aminotransferase, total bilirubin, albumin (ALB), white blood cell count, DD, alpha-fetoprotein, CD4+ T cell count, PVTT, tumor number (≥3) and lymph node invasion. Compared with the Cox regression model, significant advantages in screening prognostic factors were demonstrated by the LASSO regression model. Therefore, the risk factors identified by LASSO regression were chosen to construct a nomogram prediction model for subsequent analysis. The model showed good discrimination ability (Log rank P<0.05), calibration ability and net benefit of clinical strategy in both the training and validation group. Restricted cubic spline analysis found that ALB level and DD were significantly nonlinearly correlated with patient prognosis. This study constructed a multidimensional prediction model for tumor progression in patients with intermediate and advanced HCC. It was helpful to screen the best benefit population and optimize the treatment strategy.
如何引用
AMA
Lina Sun, Yanqiu Li, Qiang Zhao, Ke Shi, Xue Yang, Qun Zhang, et al. Prediction of tumor progression in intermediate and advanced hepatocellular carcinoma undergoing TACE combined with targeted immunotherapy.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1678771.
APA
Lina Sun, Yanqiu Li, Qiang Zhao, Ke Shi, Xue Yang, Qun Zhang, et al (2026). Prediction of tumor progression in intermediate and advanced hepatocellular carcinoma undergoing TACE combined with targeted immunotherapy.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1678771
GB/T 7714
Lina Sun, Yanqiu Li, Qiang Zhao, Ke Shi, Xue Yang, Qun Zhang, et al. Prediction of tumor progression in intermediate and advanced hepatocellular carcinoma undergoing TACE combined with targeted immunotherapy.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1678771.
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