基于深度学习的慢性肝病肝血管-体积比MRI量化
MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
AI总结：
本研究利用深度学习MRI量化慢性肝病肝血管体积比，发现其随疾病进展降低，并与纤维化和肝功能标志物相关，可作为疾病严重程度的影像学指标。
摘要：
背景：我们旨在利用基于深度学习的磁共振成像（MRI）分析，量化慢性肝病各阶段及健康对照者的肝血管体积，并评估其与肝脏（功能）障碍及纤维化/门静脉高压生物标志物的相关性。方法：我们回顾性评估了健康对照者、非晚期及晚期慢性肝病（ACLD）患者，使用3D U-Net模型对钆塞酸增强3-T MRI门静脉期图像进行肝血管分割。比较各组间的总血管-体积比（TVVR）、肝血管-体积比（HVVR）和肝内门静脉-体积比（PVVR），并分析与白蛋白-胆红素（ALBI）评分、终末期肝病模型-钠（MELD-Na）评分、纤维化/门静脉高压指标（Fibrosis-4 [FIB-4]评分、肝脏硬度测量[LSM]、肝静脉压力梯度[HVPG]、血小板计数[PLT]和脾脏体积）的相关性。结果：我们纳入了197名受试者，年龄54.9±13.8岁（平均值±标准差），其中男性111名（56.3%）：35名健康对照者、44名非ACLD患者和118名ACLD患者。TVVR和HVVR在对照组中最高（3.9；2.1），在非ACLD组中居中（2.8；1.7），在ACLD患者中最低（2.3；1.0）（p≤0.001）。PVVR在非ACLD和ACLD患者中（均为1.2）较对照组（1.7）降低（p≤0.001），但在慢性肝病（CLD）各组间无差异（p=0.999）。HVVR与FIB-4、ALBI、MELD-Na、LSM和脾脏体积显著间接相关（ρ范围-0.27至-0.40），与PLT直接相关（ρ=0.36）。TVVR和PVVR显示出相似但较弱的相关性。结论：基于深度学习的肝血管体积测量显示了健康肝脏与慢性肝病各阶段之间的差异，并与已确立的疾病严重程度标志物相关。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
