MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
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2025
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| author | Herold, Alexander Sobotka, Daniel Beer, Lucian Bastati, Nina Poetter-Lang, Sarah Weber, Michael Reiberger, Thomas Mandorfer, Mattias Semmler, Georg Simbrunner, Benedikt Wichtmann, Barbara D. Ba-Ssalamah, Sami A. Trauner, Michael Ba-Ssalamah, Ahmed Langs, Georg |
| author_facet | Herold, Alexander Sobotka, Daniel Beer, Lucian Bastati, Nina Poetter-Lang, Sarah Weber, Michael Reiberger, Thomas Mandorfer, Mattias Semmler, Georg Simbrunner, Benedikt Wichtmann, Barbara D. Ba-Ssalamah, Sami A. Trauner, Michael Ba-Ssalamah, Ahmed Langs, Georg |
| contents | Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension.
Methods: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and model for end-stage liver disease-sodium (MELD-Na) score, and fibrosis/portal hypertension (Fibrosis-4 [FIB-4] score, liver stiffness measurement [LSM], hepatic venous pressure gradient [HVPG], platelet count [PLT], and spleen volume).
Results: We included 197 subjects, aged 54.9 $\pm$ 13.8 years (mean $\pm$ standard deviation), 111 males (56.3\%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3; 1.0) ($p \leq 0.001$). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) ($p \leq 0.001$), but showed no difference between CLD groups ($p = 0.999$). HVVR significantly correlated indirectly with FIB-4, ALBI, MELD-Na, LSM, and spleen volume ($ρ$ ranging from -0.27 to -0.40), and directly with PLT ($ρ= 0.36$). TVVR and PVVR showed similar but weaker correlations.
Conclusions: Deep learning-based hepatic vessel volumetry demonstrated differences between healthy liver and chronic liver disease stages and shows correlations with established markers of disease severity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach Herold, Alexander Sobotka, Daniel Beer, Lucian Bastati, Nina Poetter-Lang, Sarah Weber, Michael Reiberger, Thomas Mandorfer, Mattias Semmler, Georg Simbrunner, Benedikt Wichtmann, Barbara D. Ba-Ssalamah, Sami A. Trauner, Michael Ba-Ssalamah, Ahmed Langs, Georg Medical Physics Artificial Intelligence Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. Methods: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and model for end-stage liver disease-sodium (MELD-Na) score, and fibrosis/portal hypertension (Fibrosis-4 [FIB-4] score, liver stiffness measurement [LSM], hepatic venous pressure gradient [HVPG], platelet count [PLT], and spleen volume). Results: We included 197 subjects, aged 54.9 $\pm$ 13.8 years (mean $\pm$ standard deviation), 111 males (56.3\%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3; 1.0) ($p \leq 0.001$). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) ($p \leq 0.001$), but showed no difference between CLD groups ($p = 0.999$). HVVR significantly correlated indirectly with FIB-4, ALBI, MELD-Na, LSM, and spleen volume ($ρ$ ranging from -0.27 to -0.40), and directly with PLT ($ρ= 0.36$). TVVR and PVVR showed similar but weaker correlations. Conclusions: Deep learning-based hepatic vessel volumetry demonstrated differences between healthy liver and chronic liver disease stages and shows correlations with established markers of disease severity. |
| title | MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach |
| topic | Medical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2510.08039 |