ArticleResearch in diagnostic and interventional imaging2026
Machine learning quantification of hepatobiliary lesion-to-liver contrast enhancement ratio in benign hepatocellular tumors: a feasibility study.
Article in Research in diagnostic and interventional imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To evaluate the feasibility of a machine learning derived semi-automatic quantification of the lesion-to-liver contrast enhancement ratio following hepatobiliary contrast agent injection for the characterization of benign hepatocellular tumors. Materials and methods: Precontrast, arterial, portal venous and hepatobiliary phases of liver magnetic resonance images were coregistered, then the liver was automatically segmented by a pretrained U-Net network. Each lesion was then manually segmented on the arterial phase, while different regions of interest were automatically placed in the liver parenchyma (ring shape, circular). Two-dimensional and three-dimensional lesion-to-liver contrast enhancement ratios were automatically calculated using each different regions of interest and the whole liver volume. All the calculated lesion-to-liver contrast enhancement ratio were compared to a manual lesion-to-liver contrast enhancement ratio and correlated to organic-anion-transporting polypeptide expression. Moreover, the ability of each calculated lesion-to-liver contrast enhancement ratio in classifying the two lesions was investigated. Results: A total of 23 hepatocellular benign tumors were analyzed including nine focal nodular hyperplasias (39 %) and 14 hepatocellular adenomas (61 %). The correlation between each calculated lesion-to-liver contrast enhancement ratio and the manual value was strong (Pearson coefficient ranging between 0.69 and 0.87). Regarding correlation with organic-anion-transporting polypeptide expression, the highest correlation values was obtained with two-dimensional ring region of interest lesion-to-liver contrast enhancement ratio (Pearson coefficient = 0.56). All lesion-to-liver contrast enhancement ratio values were effective in classifying between focal nodular hyperplasia and hepatocellular adenoma, with the best results in terms of inverse Fisher discriminant ratio obtained with ring-shaped region of interest (0.34 2D, 0.28 3D) and uniform liver region of interest (0.28 2D, 0.37 3D). Conclusion: Semi-automatic quantification of the hepatobiliary lesion-to-liver contrast enhancement ratio both using a two- or a three-dimensional approach is a promising tool for benign hepatocellular characterization.
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