Wang Miaomiao, Wang Yinzhong, Cao Liang, Wang Qian, Shen Ya, Yan Ruifeng, Lei Junqiang
The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China.
Department of Radiology, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China.
J Gastrointest Surg. 2025 Nov;29(11):102181. doi: 10.1016/j.gassur.2025.102181. Epub 2025 Aug 11.
This study aimed to assess the relationship of the Liver Imaging Reporting and Data System (LI-RADS) major feature with vessels encapsulating tumor clusters (VETC) pattern and recurrence in hepatocellular carcinoma (HCC).
Patients with HCC who underwent hepatectomy between January 2016 and August 2024 were included and underwent enhanced magnetic resonance imaging or computed tomography. The study assessed LI-RADS major features and clinicopathological characteristics. Logistic regression was used to screen variables associated with the VETC pattern and construct predictive models. Diagnostic performance was assessed by the area under the curve (AUC). Risk factors associated with recurrence-free survival (RFS) were identified by Cox regression, and recurrence curves were plotted by the Kaplan-Meier method.
A total of 290 patients with HCC were included. Multivariate analysis revealed that maximal diameter (odds ratio [OR], 1.15; 95% CI, 1.04-1.28; P =.007), nonperipheral washout (OR, 2.22; 95% CI, 1.14-4.33; P =.02), and enhancing capsule (OR, 1.84; 95% CI, 1.08-3.13; P =.02) were associated with VETC-positive HCC, based on which the LI-RADS model was constructed, with sensitivity, specificity, and AUC of 82.2%, 46.6%, and 0.67 (0.62-0.73), respectively. In contrast, the inclusion of clinical characteristics (aspartate transaminase and hepatitis C) constituted a hybrid model with sensitivity, specificity, and AUC of 74.1%, 62.1%, and 0.72 (0.66-0.77), respectively. Cox regression demonstrated that TNM stage (hazard ratio [HR], 2.09; 95% CI, 1.07-4.09; P =.03) and VETC pattern (HR, 2.45; 95% CI, 1.39-4.32; P =.002) were risk factors for RFS, and the VETC pattern had a higher risk of recurrence.
A model based on major features of LI-RADS may provide some support for the initial clinical assessment of VETC-positive HCC, and VETC patterns are associated with an increased risk of postoperative recurrence of HCC. However, this needs to be supported by more studies.
本研究旨在评估肝脏影像报告和数据系统（LI-RADS）主要特征与肿瘤簇包绕血管（VETC）模式及肝细胞癌（HCC）复发之间的关系。
纳入2016年1月至2024年8月期间接受肝切除术的HCC患者，并对其进行增强磁共振成像或计算机断层扫描。该研究评估了LI-RADS主要特征和临床病理特征。采用逻辑回归筛选与VETC模式相关的变量并构建预测模型。通过曲线下面积（AUC）评估诊断性能。通过Cox回归确定无复发生存期（RFS）的危险因素，并采用Kaplan-Meier法绘制复发曲线。
共纳入290例HCC患者。多变量分析显示，最大直径（比值比[OR]，1.15；95%置信区间[CI]，1.04 - 1.28；P = 0.007）、非周边洗脱（OR，2.22；95% CI，1.14 - 4.33；P = 0.02）和强化包膜（OR，1.84；95% CI，1.08 - 3.13；P = 0.02）与VETC阳性HCC相关，基于此构建了LI-RADS模型，其敏感性、特异性和AUC分别为82.2%、46.6%和0.67（0.62 - 0.73）。相比之下，纳入临床特征（天冬氨酸转氨酶和丙型肝炎）构成了一个混合模型，其敏感性、特异性和AUC分别为74.1%、62.1%和0.72（0.66 - 0.77）。Cox回归表明，TNM分期（风险比[HR]，2.09；95% CI，1.07 - 4.09；P = 0.03）和VETC模式（HR，2.45；95% CI，1.39 - 4.32；P = 0.002）是RFS的危险因素，且VETC模式具有更高的复发风险。
基于LI-RADS主要特征的模型可能为VETC阳性HCC的初始临床评估提供一些支持，且VETC模式与HCC术后复发风险增加相关。然而，这需要更多研究的支持。
