MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach

2025-10-09

AI Summary

Chronic liver disease (CLD) progression involves complex microvascular remodeling, yet noninvasive, quantitative imaging biomarkers reflecting vascular alterations across disease stages remain lacking. Method: We applied a 3D U-Net model to automatically segment hepatic vasculature from portal-venous-phase gadoxetic acid–enhanced 3-T MRI in 197 subjects, enabling precise quantification of hepatic vein volume ratio (HVVR), portal vein volume ratio (PVVR), and total vessel volume ratio (TVVR). Contribution/Results: HVVR exhibited a significant, stage-dependent decline across CLD progression and demonstrated strong correlations with established clinical markers—including FIB-4, ALBI, MELD-Na, liver stiffness measurement (LSM), splenic volume, and platelet count (all *P* < 0.001). HVVR effectively discriminated healthy controls from patients across all CLD stages (AUC = 0.89). In contrast, PVVR and TVVR showed weaker or nonsignificant associations. This study establishes HVVR as a novel, noninvasive, MRI-derived imaging biomarker for CLD, offering a robust, quantitative tool for risk stratification and longitudinal disease monitoring.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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.

Problem

• Quantifying hepatic vessel volumes across chronic liver disease stages
• Assessing correlations between vessel ratios and liver dysfunction biomarkers
• Differentiating healthy and diseased livers using deep learning MRI analysis

Innovation

• Deep learning 3D U-Net segments hepatic vessels
• Quantifies vessel-to-volume ratios from MRI scans
• Correlates volumetric measurements with clinical biomarkers