Machine learning quantification of hepatobiliary lesion-to-liver contrast enhancement ratio in benign hepatocellular tumors: a feasibility study - 29/09/26
, Salim Jazzar a, Sonia Colombo d, Giuseppina Pierro d, Silvia Gonella d, Deborah Fazzini e, Marco Ali e, Fabio Tedoldi d, Giovanni Valbusa d, Frédéric Pigneur a, Sébastien Mulé a, b, c, Alain Luciani a, b, c
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Highlights |
• | All different automatic region of interest were effective for differentiating focal nodular hyperplasia and hepatocellular adenoma. |
• | Intraclass correlation coefficients were high to very high for all the calculated lesion-to-liver contrast enhancement ratios. |
• | Results of our study do not support a superiority for the 3D evaluation. |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Focal nodular hyperplasia, Adenoma, Liver neoplasms, Organic anion transporters, Contrast media
Plan
Vol 17
Article 100066- mars 2026 Retour au numéro
Article précédent
- Machine learning quantification of hepatobiliary lesion-to-liver contrast enhancement ratio in benign hepatocellular tumors: a feasibility study
- Édouard Reizine, Salim Jazzar, Sonia Colombo, Giuseppina Pierro, Silvia Gonella, Deborah Fazzini, Marco Ali, Fabio Tedoldi, Giovanni Valbusa, Frédéric Pigneur, Sébastien Mulé, Alain Luciani
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