用深度学习分析MRI，量化肝脏血管体积变化，评估肝病严重程度。
MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
- 基于3D U-Net模型分割肝脏血管，计算血管体积比。
- 健康人血管体积比最高，肝硬化患者最低，差异显著（p≤0.001）。
- 结果与肝功能、纤维化和门脉高压指标相关，适合临床评估肝病进展。
本研究回顾性分析了197名受试者（平均年龄54.9±13.8岁，男性111人），包括35名健康对照、44名非进展期慢性肝病（non-ACLD）患者和118名进展期慢性肝病（ACLD）患者。采用3D U-Net模型对钆塞酸增强的3-T MRI门静脉期图像进行肝脏血管分割，计算总血管体积比（TVVR）、肝内血管体积比（HVVR）和肝内门静脉体积比（PVVR）。结果显示，健康对照组的TVVR（3.9）和HVVR（2.1）均高于non-ACLD组（2.8；1.7）和ACLD组（2.3；1.0），三组间差异显著（p≤0.001）。PVVR在non-ACLD和ACLD组中均为1.2，显著低于对照组的1.7（p≤0.001），但两组间无差异（p=0.999）。HVVR与FIB-4、ALBI、MELD-Na、LSM、脾脏体积呈负相关（ρ范围-0.27至-0.40），与血小板计数呈正相关（ρ=0.36），而TVVR和PVVR的相关性较弱。结论：基于深度学习的肝脏血管体积测量可有效区分健康肝脏与不同阶段慢性肝病，并与现有肝病严重程度标志物显著相关。
原文摘要 · Abstract (English)
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.
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