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MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
Alexander Herold1, Daniel Sobotka2, Lucian Beer1
1Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Insights
Deep learning analysis of liver MRI reveals reduced hepatic vessel volumes in chronic liver disease (CLD) patients compared to healthy controls. These volumetric changes correlate with key biomarkers of liver dysfunction and fibrosis.
Area of Science:
- Radiology and Imaging
- Artificial Intelligence in Medicine
- Hepatology
Background:
- Chronic liver disease (CLD) encompasses a spectrum of conditions affecting liver health.
- Accurate staging and monitoring of CLD progression are crucial for patient management.
- Non-invasive biomarkers for assessing liver (dys)function and fibrosis are highly sought after.
Purpose of the Study:
- To quantify hepatic vessel volumes in different stages of CLD and in healthy controls using deep learning-based MRI.
- To evaluate the correlation between hepatic vessel volumes and established biomarkers of liver function, fibrosis, and portal hypertension.
Main Methods:
- Retrospective analysis of 3-T MRI scans from healthy controls and patients with non-advanced and advanced CLD (ACLD).
- Utilized a 3D U-Net deep learning model for hepatic vessel segmentation.
- Quantified total vessel volume ratio (TVVR), hepatic vessel volume ratio (HVVR), and portal vein-to-volume ratio (PVVR).
- Correlated volumetric data with albumin-bilirubin (ALBI) score, MELD-Na score, FIB-4 score, liver stiffness measurement (LSM), hepatic venous pressure gradient (HVPG), platelet count (PLT), and spleen volume.
Main Results:
- Included 197 participants (35 controls, 44 non-ACLD, 118 ACLD).
- TVVR and HVVR were significantly higher in controls, intermediate in non-ACLD, and lowest in ACLD patients (p ≤ 0.001).
- PVVR was reduced in both non-ACLD and ACLD groups compared to controls (p ≤ 0.001), with no difference between CLD groups.
- HVVR showed significant inverse correlations with FIB-4, ALBI, MELD-Na, LSM, and spleen volume, and a direct correlation with PLT.
Conclusions:
- Deep learning-based hepatic vessel volumetry effectively differentiates between healthy liver and various stages of CLD.
- Quantified hepatic vascular changes, particularly reduced HVVR, correlate with established markers of liver dysfunction, fibrosis, and portal hypertension.
- Hepatic vessel volumetry shows potential as a non-invasive imaging biomarker for assessing CLD severity.
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 ± 13.8 years (mean ± 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 ≤ 0.001). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) (p ≤ 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.
Conclusion:
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.
Relevance Statement:
Hepatic vessel volumetry demonstrates differences between healthy liver and chronic liver disease stages, potentially serving as a non-invasive imaging biomarker.
Key Points:
Deep learning-based vessel analysis can provide automated quantification of hepatic vascular changes across healthy liver and chronic liver disease stages. Automated quantification of hepatic vasculature shows significantly reduced hepatic vascular volume in advanced chronic liver disease compared to non-advanced disease and healthy liver. Decreased hepatic vascular volume, particularly in the hepatic venous system, correlates with markers of liver dysfunction, fibrosis, and portal hypertension.
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