Automated Whole-Liver Fat Quantification with Magnetic Resonance Imaging-Derived Proton Density Fat Fraction Map: A Prospective Study in Taiwan.
Authors
Affiliations (12)
Affiliations (12)
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
- Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan.
- Center of Minimal-Invasive Interventional Radiology, National Taiwan University Hospital, Taipei, Taiwan.
- School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan.
- Physical Therapy Center, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan.
- Bachelor Program of Biotechnology and Food Nutrition, College of Bio-Resources and Agriculture, National Taiwan University, Taipei, Taiwan.
- Division of Chest Medicine, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
- Center of Sleep Disorder, National Taiwan University Hospital, Taipei, Taiwan.
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
- Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, Taiwan.
- Clinical Trial Center, National Taiwan University Hospital, Taipei, Taiwan.
Abstract
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).