Differentiation of hepatocellular carcinoma from hepatic hemangioma using diffusion-derived vessel density map-based radiomics features.

Objective: To evaluate the diagnostic performance of radiomics features extracted from diffusion-derived Vessel Density (DDVD) in differentiating hepatocellular carcinoma (HCC) from hepatic hemangioma (HG). Methods: This retrospective study enrolled 232 patients (104 with pathologically confirmed HCCs and 128 with clinically diagnosed HGs). The cohort was randomly divided into training and testing sets (7:3 ratio). We generated DDVD maps (subtraction maps of b0-b50 voxel-by-voxel). Features were extracted from b0, b50, b800, ADC, and DDVD maps, respectively. Feature selection was sequentially performed for each type of image using Mann-Whitney U test, Pearson correlation ( Results: From a total of 1,197 features initially extracted, 10 most informative features from each image type were retained. The DDVD-based model demonstrated comparable performance to b0, b50, and ADC models, achieving AUC values of 0.926 (95% CI: 0.914 - 0.929) in the validation cohort and 0.977 (95% CI: 0.948 - 1.000) in the independent test cohort. Comparative analysis revealed that the b0, b50, ADC, and DDVD models significantly outperformed the b800 model in the test cohort (all Conclusions: DDVD-based radiomics demonstrates an effective approach for differentiating HCC from HG by quantifying spatial heterogeneity in microvascular characteristics.