Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy
Jie Yan
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Co-first authors
Search for more papers by this authorYang Yu
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Co-first authors
Search for more papers by this authorJeon Woong Kang
Laser Biomedical Research Center, George R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Co-first authors
Search for more papers by this authorZhi Yang Tam
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Search for more papers by this authorEliza Li Shan Fong
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
Search for more papers by this authorSurya Pratap Singh
Laser Biomedical Research Center, George R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Search for more papers by this authorZiwei Song
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
Search for more papers by this authorLisa Tucker-Kellogg
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Duke-NUS Graduate Medical School Singapore, National University of Singapore, Singapore, 169857
Search for more papers by this authorPeter T. C. So
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Search for more papers by this authorCorresponding Author
Hanry Yu
- [email protected]
- +65 65163466+65 65163466 | Fax: +65 68748261Fax: +65 68748261
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Mechanobiology Institute, National University of Singapore, Singapore, 117411
Search for more papers by this authorJie Yan
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Co-first authors
Search for more papers by this authorYang Yu
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Co-first authors
Search for more papers by this authorJeon Woong Kang
Laser Biomedical Research Center, George R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Co-first authors
Search for more papers by this authorZhi Yang Tam
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Search for more papers by this authorEliza Li Shan Fong
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
Search for more papers by this authorSurya Pratap Singh
Laser Biomedical Research Center, George R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Search for more papers by this authorZiwei Song
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
Search for more papers by this authorLisa Tucker-Kellogg
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Duke-NUS Graduate Medical School Singapore, National University of Singapore, Singapore, 169857
Search for more papers by this authorPeter T. C. So
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Search for more papers by this authorCorresponding Author
Hanry Yu
- [email protected]
- +65 65163466+65 65163466 | Fax: +65 68748261Fax: +65 68748261
Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive, Singapore, 117597
BioSyM, Singapore-MIT Alliance for Research and Technology, Singapore, 138602
Mechanobiology Institute, National University of Singapore, Singapore, 117411
Search for more papers by this authorAbstract
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.
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