Wu Chih-Horng, Yen Kuang-Chen, Wang Li-Ying, Hsieh Ping-Lun, Wu Wei-Kai, Lee Pei-Lin, Liu Chun-Jen
Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan.
Gut Liver. 2025 Jul 15;19(4):617-626. doi: 10.5009/gnl240408. Epub 2025 Apr 1.
BACKGROUND/AIMS: Magnetic resonance imaging (MRI) with a proton density fat fraction (PDFF) sequence is the most accurate, noninvasive method for assessing hepatic steatosis. However, manual measurement on the PDFF map is time-consuming. This study aimed to validate automated whole-liver fat quantification for assessing hepatic steatosis with MRI-PDFF.
In this prospective study, 80 patients were enrolled from August 2020 to January 2023. Baseline MRI-PDFF and magnetic resonance spectroscopy (MRS) data were collected. The analysis of MRI-PDFF included values from automated whole-liver segmentation (autoPDFF) and the average value from measurements taken from eight segments (avePDFF). Twenty patients with ≥10% autoPDFF values who received 24 weeks of exercise training were also collected for the chronologic evaluation. The correlation and concordance coefficients (r and ρ) among the values and differences were calculated.
There were strong correlations between autoPDFF versus avePDFF, autoPDFF versus MRS, and avePDFF versus MRS (r=0.963, r=0.955, and r=0.977, all p<0.001). The autoPDFF values were also highly concordant with the avePDFF and MRS values (ρ=0.941 and ρ=0.942). The autoPDFF, avePDFF, and MRS values consistently decreased after 24 weeks of exercise. The change in autoPDFF was also highly correlated with the changes in avePDFF and MRS (r=0.961 and r=0.870, all p<0.001).
Automated whole-liver fat quantification might be feasible for clinical trials and practice, yielding values with high correlations and concordance with the time-consuming manual measurements from the PDFF map and the values from the highly complex processing of MRS (ClinicalTrials.gov identifier: NCT04463667).
背景/目的：采用质子密度脂肪分数（PDFF）序列的磁共振成像（MRI）是评估肝脂肪变性最准确的非侵入性方法。然而，在PDFF图上进行手动测量很耗时。本研究旨在验证利用MRI-PDFF进行全肝脂肪自动定量以评估肝脂肪变性的方法。
在这项前瞻性研究中，2020年8月至2023年1月纳入了80例患者。收集了基线MRI-PDFF和磁共振波谱（MRS）数据。MRI-PDFF分析包括自动全肝分割值（autoPDFF）和八个节段测量的平均值（avePDFF）。还收集了20例autoPDFF值≥10%且接受了24周运动训练的患者进行时间序列评估。计算了各值之间的相关性和一致性系数（r和ρ）以及差异。
autoPDFF与avePDFF、autoPDFF与MRS、avePDFF与MRS之间均存在强相关性（r = 0.963、r = 0.955和r = 0.977，均p < 0.001）。autoPDFF值与avePDFF和MRS值也高度一致（ρ = 0.941和ρ = 0.942）。运动24周后，autoPDFF、avePDFF和MRS值持续下降。autoPDFF的变化与avePDFF和MRS的变化也高度相关（r = 0.961和r = 0.870，均p < 0.001）。
全肝脂肪自动定量在临床试验和实践中可能是可行的，其结果与从PDFF图进行的耗时手动测量值以及MRS高度复杂处理得到的值具有高度相关性和一致性（ClinicalTrials.gov标识符：NCT04463667）。
