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

• Main Authors: Yan, Jie (Author), Yu, Yang (Contributor), Kang, Jeon Woong (Contributor), Tam, Zhi Yang (Author), Xu, Shuoyu (Contributor), Fong, Eliza Li Shan (Author), Singh, Surya Pratap (Contributor), Song, Ziwei (Contributor), Tucker-Kellogg, Lisa (Contributor), So, Peter T. C. (Contributor), Yu, Hanry (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computational and Systems Biology Program (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemistry (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor), Massachusetts Institute of Technology. Spectroscopy Laboratory (Contributor)
• Format: Article
• Language: English
• Published: Wiley, 2019-01-08T17:51:51Z.
• Subjects: Article
• Online Access: Get fulltext

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.
Singapore. National Research Foundation (under its CREATE programme)
Singapore-MIT Alliance. BioSystems and Micromechanics (BioSyM) Inter-Disciplinary Research Group
Singapore. Agency for Science, Technology and Research (Project Number 1334i00051)
Singapore. National Medical Research Council (R-185-000-294-511)
National University of Singapore. Mechanobiology Institute (R-714-001-003-271)
National Institutes of Health (U.S.) (9P41EB015871-28)
Samsung Advanced Institute of Technology
Singapore. National Medical Research Council (Open Fund Individual Research Grant scheme (OFIRG15nov062)