Che Feng, Gao Feifei, Li Qian, Ren Wei, Tang Hehan, Zaina Guli, Zhang Xin, Yao Shan, Zhang Ning, Zhu Shaocheng, Song Bin, Wei Yi
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Department of CT Imaging Research Center, GE Healthcare China, Beijing, China.
Int J Surg. 2025 Jul 1;111(7):4389-4399. doi: 10.1097/JS9.0000000000002547. Epub 2025 May 29.
OBJECTIVE: The aim of this study was to assess the potential role of fractal analysis derived from Gd-EOB-DTPA-enhanced MRI in predicting vessels that encapsulate tumor clusters (VETC) in patients with hepatocellular carcinoma (HCC). METHODS: This retrospective study included 505 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI before surgical resection at two medical centers (training set: 253 patients, internal test set: 108 patients, external test set: 144 patients). The fractal dimension (FD) and lacunarity were extracted from the hepatobiliary phase of the tumor using box-counting algorithms. Additionally, conventional imaging features were evaluated. Univariate and multivariate logistic regression analyses were conducted in the training set to identify independent predictors for VETC, and a nomogram was created to visualize the final predictive model. The performance of these models was tested in the internal and external test sets. Recurrence-free survival (RFS) and overall survival (OS) were analyzed using the Kaplan-Meier method along with the log-rank test. RESULTS: VETC-positive HCC exhibited higher FD and lacunarity than VETC-negative HCC ( P < 0 .001). The FD-lacunarity model achieved an area under receiver operating characteristics curve (AUC) of 0.78 (95% confidence interval [CI]: 0.70-0.87) in the internal test set and 0.79 (95%CI: 0.70-0.86) in the external test set. Multivariate logistic regression analysis identified serum alpha-fetoprotein, tumor size, intratumor artery, FD, and lacunarity as independent predictors for VETC, which were used for constructing the hybrid model. A clinical model was established using AFP, tumor size, and intratumor artery alone. The diagnostic performance of the hybrid model was significantly surpassed that of the clinical-radiological model when fractal parameters were incorporated, with AUCs increasing from 0.72 to 0.80 in the internal test set and from 0.65 to 0.84 in the external test set (all P < 0.05). Patients predicted by the hybrid model to have VETC-positive HCC exhibited significantly shorter RFS and OS compared to those predicted to have VETC-negative HCC ( P < 0.05). CONCLUSION: Fractal analysis based on Gd-EOB-DTPA-enhanced MRI enabled the quantitative characterization of VETC status by fractal dimension and lacunarity. The hybrid model may assist in estimating VETC and stratifying prognosis in patients with HCC.
目的：本研究旨在评估基于钆塞酸二钠增强磁共振成像（Gd-EOB-DTPA-enhanced MRI）的分形分析在预测肝细胞癌（HCC）患者中包裹肿瘤簇的血管（VETC）方面的潜在作用。 方法：这项回顾性研究纳入了505例在两个医学中心接受手术切除前进行Gd-EOB-DTPA增强MRI检查的HCC患者（训练集：253例患者，内部测试集：108例患者，外部测试集：144例患者）。使用盒计数算法从肿瘤的肝胆期提取分形维数（FD）和空隙率。此外，评估了传统的影像学特征。在训练集中进行单因素和多因素逻辑回归分析，以确定VETC的独立预测因素，并创建列线图以可视化最终预测模型。在内部和外部测试集中测试这些模型的性能。使用Kaplan-Meier方法和对数秩检验分析无复发生存期（RFS）和总生存期（OS）。 结果：VETC阳性的HCC比VETC阴性的HCC表现出更高的FD和空隙率（P < 0.001）。FD-空隙率模型在内部测试集中的受试者操作特征曲线下面积（AUC）为0.78（95%置信区间[CI]：0.70 - 0.87），在外部测试集中为0.79（95%CI：0.70 - 0.86）。多因素逻辑回归分析确定血清甲胎蛋白、肿瘤大小、瘤内动脉、FD和空隙率为VETC的独立预测因素，用于构建混合模型。仅使用AFP、肿瘤大小和瘤内动脉建立了临床模型。当纳入分形参数时，混合模型的诊断性能显著超过临床放射学模型，内部测试集中的AUC从0.72增加到0.80，外部测试集中从0.65增加到0.84（所有P < 0.05）。与预测为VETC阴性的HCC患者相比，混合模型预测为VETC阳性的HCC患者的RFS和OS显著缩短（P < 0.05）。 结论：基于Gd-EOB-DTPA增强MRI的分形分析能够通过分形维数和空隙率对VETC状态进行定量表征。混合模型可能有助于估计HCC患者的VETC并对预后进行分层。
Medicine (Baltimore). 2025-6-27
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