Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy
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Author(s)
Yan, Jie
Yu, Yang
Kang, Jeon Woong
Tam, Zhi Yang
Xu, Shuoyu
Fong, Eliza Li Shan
Singh, Surya Pratap
Song, Ziwei
Tucker-Kellogg, Lisa
So, Peter T. C.
Date Issued
June 2017
Journal
Journal of Biophotonics
Publisher
Wiley
Citation
Yan, Jie, Yang Yu, Jeon Woong Kang, Zhi Yang Tam, Shuoyu Xu, Eliza Li Shan Fong, Surya Pratap Singh, et al. “Development of a Classification Model for Non-Alcoholic Steatohepatitis (NASH) Using Confocal Raman Micro-Spectroscopy.” Journal of Biophotonics 10, no. 12 (June 21, 2017): 1703–1713.
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Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Spectroscopy Laboratory
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DOI of Published Version
https://doi.org/10.1002/JBIO.201600303
