Che Feng, Li Qian, Ren Wei, Tang Hehan, Zaina Guli, 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.
Eur Radiol. 2025 Aug 7. doi: 10.1007/s00330-025-11878-6.
This study aimed to assess the potential role of 3-dimensional (3D) fractal dimension (FD) derived from contrast-enhanced CT images in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).
This retrospective study included 655 patients with surgically confirmed HCC from two medical centers (training set: 406 patients; internal test set: 170 patients; external test set: 79 patients). Box-counting algorithms were used to compute 3D FD values from portal venous phase images. Univariable and multivariable logistic regression analyses identified independent predictors. The model's area under the curve (AUC) was calculated. Recurrence-free survival (RFS) and overall survival (OS) were evaluated using the Kaplan-Meier method.
Patients with MVI-positive HCC demonstrated significantly higher FD values compared to those with MVI-negative HCC (p < 0.01). The FD achieved AUCs of 0.786 (95% CI: 0.713-0.849) in the internal test set and 0.776 (95% CI: 0.669-0.874) in the external test set. A combined model incorporating alpha-fetoprotein, tumor size, tumor number, and FD showed superior diagnostic performance for MVI prediction compared to the clinical model, with AUCs of 0.795 (95% CI: 0.720-0.860) vs 0.752 (95% CI: 0.670-0.825) in the internal test set, and 0.826 (95% CI: 0.721-0.915) vs 0.739 (95% CI: 0.613-0.849) in the external test set. Patients stratified as high-risk MVI exhibited significantly worse RFS and OS outcomes compared to low-risk MVI patients (p < 0.05).
The 3D FD values differed significantly between MVI-positive and MVI-negative HCC patients. Integrating FD into the clinical model enhanced MVI prediction accuracy and may help identify patients at high risk.
Question The predictive value of three-dimensional (3D) fractal dimension (FD) derived from contrast-enhanced CT images for identifying MVI-positive HCC remains unclear. Findings Quantitative indicators derived from fractal analysis were able to predict MVI. The developed model demonstrated improved performance when incorporating fractal dimension. Clinical relevance Fractal analysis based on contrast-enhanced CT is a feasible approach for evaluating MVI and provides additional clinical value for prognostic assessment. It may serve as a reference for preoperative MVI estimation and assist clinicians in executing more tailored therapies.
本研究旨在评估从增强CT图像得出的三维（3D）分形维数（FD）在预测肝细胞癌（HCC）患者微血管侵犯（MVI）中的潜在作用。
这项回顾性研究纳入了来自两个医疗中心的655例经手术确诊的HCC患者（训练集：406例患者；内部测试集：170例患者；外部测试集：79例患者）。采用盒计数算法从门静脉期图像计算3D FD值。单变量和多变量逻辑回归分析确定独立预测因素。计算模型的曲线下面积（AUC）。采用Kaplan-Meier法评估无复发生存期（RFS）和总生存期（OS）。
与MVI阴性的HCC患者相比，MVI阳性的HCC患者FD值显著更高（p < 0.01）。FD在内部测试集中的AUC为0.786（95%CI：0.713 - 0.849），在外部测试集中为0.776（95%CI：0.669 - 0.874）。与临床模型相比，结合甲胎蛋白、肿瘤大小、肿瘤数量和FD的联合模型在预测MVI方面显示出更好的诊断性能，在内部测试集中的AUC分别为0.795（95%CI：0.720 - 0.860）和0.752（95%CI：0.670 - 0.825），在外部测试集中分别为0.826（95%CI：0.721 - 0.915）和0.739（95%CI：0.613 - 0.849）。与低风险MVI患者相比，被分层为高风险MVI的患者RFS和OS结局显著更差（p < 0.05）。
MVI阳性和MVI阴性的HCC患者之间3D FD值存在显著差异。将FD纳入临床模型可提高MVI预测准确性，并可能有助于识别高危患者。
问题 从增强CT图像得出的三维（3D）分形维数（FD）对识别MVI阳性HCC的预测价值尚不清楚。发现 分形分析得出的定量指标能够预测MVI。所开发的模型在纳入分形维数时表现出更好的性能。临床意义 基于增强CT的分形分析是评估MVI的可行方法，为预后评估提供了额外的临床价值。它可作为术前MVI评估的参考，并协助临床医生实施更具针对性的治疗。
