Advances in Quantitative Imaging Assessment of Hepatic Steatosis
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v7i1.5065
Abstract
The liver serves as a critical site for lipid metabolism. Under pathological conditions such as obesity and metabolic disorders, disrupted lipid metabolism leads to abnormal accumulation of triglycerides within hepatocytes. Persistent and excessive fat accumulation may exacerbate hepatocyte injury, progressively evolving into steatohepatitis, liver fibrosis, and even hepatocellular carcinoma. Therefore, quantifying hepatic fat content is crucial for early diagnosis and disease management. This article summarizes advances in imaging techniques for assessing hepatic steatosis, with a focus on the clinical application and progress of magnetic resonance imaging proton density fat fraction in quantitative hepatic fat measurement.
Keywords
hepatic steatosis, MRI-PDFF, MASLD
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[25] MCTEER M, APPLEGATE D, MESENBRINK P, et al. Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information [J]. Plos One, 2024, 19(2).
[26] PUGLIESI R A, BEN MANSOUR K, APITZSCH J, et al. Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact [J]. Journal of Clinical Medicine, 2025, 14(23).
[2] HUANG Y L, SUN C, WANG Y, et al. Ultrasound-guided attenuation parameter for identifying metabolic dysfunction- associated steatotic liver disease: a prospective study [J]. Ultrasonography, 2025, 44(2): 134-144.
[3] IMAJO K, TOYODA H, YASUDA S, et al. Utility of Ultrasound-Guided Attenuation Parameter for Grading Steatosis With Reference to MRI-PDFF in a Large Cohort [J]. Clinical Gastroenterology and Hepatology, 2022, 20(11): 2533.
[4] CANNELLA R, AGNELLO F, PORRELLO G, et al. Performance of ultrasound-guided attenuation parameter and 2D shear wave elastography in patients with metabolic dysfunction-associated steatotic liver disease [J]. European Radiology, 2025, 35(4): 2339-2350.
[5] NAKAMURA Y, HIROOKA M, KOIZUMI Y, et al. Diagnostic accuracy of ultrasound-derived fat fraction for the detection and quantification of hepatic steatosis in patients with liver biopsy [J]. Journal of Medical Ultrasonics, 2025, 52(1): 85-94.
[6] XUE L Y, ZHU Y L, CHENG G W, et al. Ultrasound-derived fat fraction for the noninvasive quantification of hepatic steatosis: a prospective multicenter study [J]. Insights into Imaging, 2025, 16(1).
[7] HUANG Y L, LI J, LIU C, et al. Noninvasive Quantification of Hepatic Steatosis Using Ultrasound-Derived Fat Fraction (CHESS2303): A Prospective Multicenter Study [J]. Medcomm, 2025, 6(3).
[8] Cao Xinge, Zhang Yali, Jia Lizhuo, et al. Comparative Diagnostic Value of Ultrasound-Derived Fat Fraction, Controlled Attenuation Parameter, and Hepatorenal Echo Ratio in Grading Hepatic Steatosis in Metabolic-Associated Fatty Liver Disease [J]. Journal of Clinical Hepatology, 2025, 41(09): 1788-1794.
[9] Wang Moxue, Shan Tao, Yu Jing, et al. Ultrasound-Based Quantitative Assessment of Fat Fraction in Chronic Liver Disease with Steatosis: A Controlled Study with Pathology [J]. Chinese Journal of Ultrasound in Medicine, 2025, 41(02): 161-164.
[10] GOTTFRIEDOVA H, DEZORTOVA M, SEDIVY P, et al. Comparison of ultrasound to MR and histological methods for liver fat quantification [J]. European Journal of Radiology, 2025, 183.
[11] NASIR M, XU Y X, HASENSTAB K, et al. Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study [J]. Plos One, 2025, 20(8).
[12] DERSTINE B A, HOLCOMBE S A, CHEN V L, et al. Quantification of hepatic steatosis on post-contrast computed tomography scans using artificial intelligence tools [J]. Abdominal Radiology, 2025.
[13] Hua Xuwen, Fang Min, You Wuyi, et al. Accuracy Analysis of Magnetic Resonance FACT Sequence in Diagnosing Non-Alcoholic Fatty Liver Disease [J]. Journal of Clinical Radiology, 2025, 44(06): 1030-1034.
[14] Xu Li, Glen MBlake, Guo Zhe, et al. Correlation Study Between Quantitative CT and MR mDixon-quant for Measuring Liver Fat Content [J]. Radiology Practice, 2017, 32(05): 456-461.
[15] Zhang Haoran, Bai Zhen, Su Danyang, et al. Artificial Intelligence Model-Based Measurement of Liver Fat Content Using Abdominal Plain CT: A Comparative Study with QCT[J].Radiology Practice, 2025, 40(08):1011-1017.
[16] Zhang Chengmeng, Ding Zhimin, Sun Xiaoyu, et al. Preliminary Study on the Combined Use of Quantitative CT Liver Fat Content and Clinical Indicators to Predict the Risk of Esophageal and Gastric Variceal Bleeding in Patients with Liver Cirrhosis [J]. Radiology Practice, 2024, 39(07): 902-906.
[17] SCHWARTZ F R, ASHTON J, WILDMAN-TOBRINER B, et al. Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience [J]. European Journal of Radiology, 2023, 161.
[18] LIN H M, XU X X, DENG R, et al. Photon-counting Detector CT for Liver Fat Quantification: Validation across Protocols in Metabolic Dysfunction-associated Steatotic Liver Disease [J]. Radiology, 2024, 312(3).
[19] PANTA R K, YIN Z, GRöNBERG F, et al. Liver fat quantification using deep silicon photon-counting CT: an in silico imaging study [J]. Radiology Advances, 2025, 2(5).
[20] LOOMBA R, NEUSCHWANDER-TETRI B A, SANYAL A, et al. Multicenter Validation of Association Between Decline in MRI-PDFF and Histologic Response in NASH [J]. Hepatology, 2020, 72(4): 1219-1229.
[21] STINE J G, MUNAGANURU N, BARNARD A, et al. Change in MRI-PDFF and Histologic Response in Patients With Nonalcoholic Steatohepatitis: A Systematic Review and Meta-Analysis [J]. Clinical Gastroenterology and Hepatology, 2021, 19(11): 2274.
[22] TAMAKI N, MUNAGANURU N, JUNG J H, et al. Clinical utility of 30% relative decline in MRI-PDFF in predicting fibrosis regression in non-alcoholic fatty liver disease [J]. Gut, 2022, 71(5): 983-990.
[23] CHEN Y Z, LAEVENS B P M, LEMAINQUE T, et al. Deep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease [J]. Liver International, 2025, 45(7).
[24] ZHU G F, SONG Y P, LU Z H, et al. Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics [J]. Journal of Translational Medicine, 2025, 23(1).
[25] MCTEER M, APPLEGATE D, MESENBRINK P, et al. Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information [J]. Plos One, 2024, 19(2).
[26] PUGLIESI R A, BEN MANSOUR K, APITZSCH J, et al. Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact [J]. Journal of Clinical Medicine, 2025, 14(23).
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