3D fractal dimension analysis of CT imaging for microvascular invasion prediction in hepatocellular carcinoma

作者：Feng Che, Qian Li, Wei Ren, Hehan Tang, Guli Zaina, Shan Yao, Ning Zhang, Shaocheng Zhu, Bin Song, Yi Wei · 发表于：European Radiology · 年份：2025 · DOI：10.1007/s00330-025-11878-6 · 被引用次数：4 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging

OBJECTIVES: 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). MATERIALS AND METHODS: 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. RESULTS: 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...