用于预测老年肝细胞癌患者早期复发的CECT放射学和肝纤维化标志物
CECT Radiomics and Liver Fibrosis Markers for Predicting Early Recurrence in Older Patients with Hepatocellular Carcinoma.
摘要(中文译文)
肝细胞癌（HCC）在老年患者中术后早期复发风险较高，传统预测方法受限于侵入性和主观性。影像组学研究缺乏针对老年人群的专门设计及对肝纤维化因素的充分整合，限制了其适用性。因此，本研究旨在开发并验证一种整合增强计算机断层扫描（CECT）影像组学与肝纤维化指标的无创模型，用于预测老年HCC患者术后早期复发，并识别关键危险因素。共回顾性纳入169例接受根治性肝切除术的老年HCC患者（≥60岁），随机分为训练集（n = 131）和验证集（n = 38）。影像组学特征提取自术前门静脉期CECT扫描图像。采用最小绝对收缩和选择算子算法筛选最优特征并构建影像组学评分（Rad-score）。采用多因素logistic回归构建影像-影像组学（RR）列线图，以识别早期复发的独立预测因素。采用曲线下面积（AUC）、校准曲线、Hosmer-Lemeshow拟合优度检验及决策曲线分析（DCA）评估模型性能。63例患者（37.3%）发生术后早期复发。肿瘤直径、总胆红素、白蛋白、IV型胶原及Rad-score（由5个最优影像组学特征得出）被确定为独立预测因素。RR列线图模型在训练集和验证集中预测早期复发的AUC分别为0.871和0.722。校准曲线及Hosmer-Lemeshow检验显示模型校准良好（P > 0.05）；DCA显示在多个阈值概率下，其净临床获益高于“全部治疗”或“不治疗”策略。整合RR列线图在预测老年HCC患者早期复发方面显示出良好的区分能力和校准能力。该列线图或可作为无创的术前风险分层工具，但在临床推广应用前仍需前瞻性多中心验证。
Abstract
Hepatocellular carcinoma (HCC) carries a high risk of early postoperative recurrence in older patients, and traditional prediction methods are limited by invasiveness and subjectivity. Radiomics studies lack older adult-specific designs and adequate integration of liver fibrosis factors, limiting applicability. Accordingly, we aimed to develop and validate a non-invasive model integrating contrast-enhanced computed tomography (CECT) radiomics and liver fibrosis indicators for predicting early postoperative recurrence in older patients with HCC and identify key risk factors. In total, 169 older patients with HCC (≥60 years) who underwent radical hepatectomy were retrospectively enrolled and randomly assigned to training (n = 131) and validation (n = 38) sets. Radiomics features were derived from preoperative CECT scans acquired during the portal venous phase. The radiomics score (Rad-score) was constructed using the least absolute shrinkage and selection operator algorithm for optimal feature selection. A radiologic‒radiomics (RR) nomogram was developed using multivariable logistic regression to identify independent predictors of early recurrence. Model performance was assessed using the area under the curve *AUC), calibration plots, the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA). Sixty-three patients (37.3%) experienced early postoperative recurrence. Tumor diameter, total bilirubin, albumin, type IV collagen, and Rad-score (derived from five optimal radiomics features) were identified as independent predictors. The RR nomogram model achieved AUCs of 0.871 and 0.722 in the training and validation sets, respectively, for predicting early recurrence. Calibration curves and Hosmer-Lemeshow test demonstrated good model calibration (P > 0.05); DCA showed a higher net clinical benefit than "treat-all" or "treat-none" strategies across multiple threshold probabilities. The integrated RR nomogram shows promising discriminatory and calibration ability for predicting early recurrence in older patients with HCC. While this nomogram may serve as a non-invasive preoperative risk-stratification tool, prospective multicenter validation is warranted before clinical adoption.
如何引用
AMA
Fang Liu, Zhi Zou, Liuchang Zheng. CECT Radiomics and Liver Fibrosis Markers for Predicting Early Recurrence in Older Patients with Hepatocellular Carcinoma.. Journal of hepatocellular carcinoma. 2026; doi:10.2147/JHC.S608338.
APA
Fang Liu, Zhi Zou, Liuchang Zheng (2026). CECT Radiomics and Liver Fibrosis Markers for Predicting Early Recurrence in Older Patients with Hepatocellular Carcinoma.. Journal of hepatocellular carcinoma. https://doi.org/10.2147/JHC.S608338
GB/T 7714
Fang Liu, Zhi Zou, Liuchang Zheng. CECT Radiomics and Liver Fibrosis Markers for Predicting Early Recurrence in Older Patients with Hepatocellular Carcinoma.[J]. Journal of hepatocellular carcinoma, 2026 doi:10.2147/JHC.S608338.
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