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J Biophotonics. 2017 Dec;10(12):1703-1713. doi: 10.1002/jbio.201600303. Epub 2017 Jun 21.

Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy.

Yan J1, Yu Y1,2,3, Kang JW4, Tam ZY3, Xu S5, Fong ELS2, Singh SP4, Song Z1,2, Tucker-Kellogg L3,6, So PTC3,7,8, Yu H1,2,3,9.

Author information

1
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669.
2
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597.
3
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602.
4
Laser Biomedical Research Center, George R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
5
InvitroCue Pte Ltd, Singapore, 138667.
6
Duke-NUS Graduate Medical School Singapore, National University of Singapore, Singapore, 169857.
7
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
8
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
9
Mechanobiology Institute, National University of Singapore, Singapore, 117411.

Abstract

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries [1]. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85-0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.

KEYWORDS:

Raman micro-spectroscopic imaging; biochemical component analysis; model fitting; non-alcoholic fatty liver disease; non-alcoholic steatohepatitis

PMID:
28635128
PMCID:
PMC5902180
DOI:
10.1002/jbio.201600303
[Indexed for MEDLINE]
Free PMC Article

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