ArticleEuropean radiology2026
3D fractal dimension analysis of CT imaging for microvascular invasion prediction in hepatocellular carcinoma.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A CECT-Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual-Validation Study.Cancer science · 2026Article
- Macroscopic fractal dynamics characterize the "physical-metabolic" dual barriers and systemic immune exhaustion associated with primary resistance to immunotherapy in liver metastases.Frontiers in immunology · 2026Article
- Novel Unsupervised Machine Learning Model Using Multi-Parametric Radiomics for Prognostic Stratification of Bifocal Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
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10 authors.
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Abstract
objectivesThis 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
methodsThis 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.
resultsPatients 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).
conclusionThe 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. KEY POINTS: 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.
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