{"ID":2858066,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.08039","arxiv_id":"2510.08039","title":"MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach","abstract":"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.","short_abstract":"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 hea...","url_abs":"https://arxiv.org/abs/2510.08039","url_pdf":"https://arxiv.org/pdf/2510.08039v1","authors":"[\"Alexander Herold\",\"Daniel Sobotka\",\"Lucian Beer\",\"Nina Bastati\",\"Sarah Poetter-Lang\",\"Michael Weber\",\"Thomas Reiberger\",\"Mattias Mandorfer\",\"Georg Semmler\",\"Benedikt Simbrunner\",\"Barbara D. Wichtmann\",\"Sami A. Ba-Ssalamah\",\"Michael Trauner\",\"Ahmed Ba-Ssalamah\",\"Georg Langs\"]","published":"2025-10-09T10:23:16Z","proceeding":"physics.med-ph","tasks":"[\"physics.med-ph\",\"cs.AI\"]","methods":"[]","has_code":false}