TY - JOUR
T1 - Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future
AU - Yip, Terry Cheuk-Fung
AU - Lyu, Fei
AU - Lin, Huapeng
AU - Li, Guanlin
AU - Yuen, Pong-Chi
AU - Wong, Vincent Wai-Sun
AU - Wong, Grace Lai-Hung
N1 - Funding Information:
This work was supported by the Health and Medical Research Fund (HMRF) of the Food and Health Bureau (Reference no.: 07180216) awarded to Grace Wong.
Publisher Copyright:
© 2023 by Korean Association for the Study of the Liver.
PY - 2023/2
Y1 - 2023/2
N2 - Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.
AB - Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.
KW - Cytokeratin-18
KW - Deep learning
KW - Fatty liver
KW - Liver cancer
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85152058067&partnerID=8YFLogxK
U2 - 10.3350/cmh.2022.0426
DO - 10.3350/cmh.2022.0426
M3 - Journal article
SN - 2287-2728
VL - 29
SP - S171-S183
JO - Clinical and Molecular Hepatology
JF - Clinical and Molecular Hepatology
IS - Suppl
ER -
