Automated Whole-Liver Fat Quantification with Magnetic Resonance Imaging-Derived Proton Density Fat Fraction Map: A Prospective Study in Taiwan

Abstract

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. Methods : 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. Results : 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). Conclusions : 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).

Keywords

• fatty liver; magnetic resonance imaging; chemical shift imaging; deep learning; artificial intelligence