Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy
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 carcinom...
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| Format: | Article |
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Wiley
2019
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| Online Access: | http://hdl.handle.net/1721.1/119875 https://orcid.org/0000-0003-2012-9023 https://orcid.org/0000-0003-4698-6488 https://orcid.org/0000-0002-0339-3685 |
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| author | 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. Yu, Hanry |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program 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. Yu, Hanry |
| author_sort | Yan, Jie |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:57:16Z |
| format | Article |
| id | mit-1721.1/119875 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:57:16Z |
| publishDate | 2019 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1198752022-09-29T17:16:14Z Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy 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. Yu, Hanry 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 Yu, Yang Kang, Jeon Woong Xu, Shuoyu Singh, Surya Pratap Song, Ziwei Tucker-Kellogg, Lisa So, Peter T. C. Yu, Hanry 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) 2019-01-08T17:51:51Z 2019-01-08T17:51:51Z 2017-06 2019-01-04T13:22:55Z Article http://purl.org/eprint/type/JournalArticle 1864063X http://hdl.handle.net/1721.1/119875 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. https://orcid.org/0000-0003-2012-9023 https://orcid.org/0000-0003-4698-6488 https://orcid.org/0000-0002-0339-3685 http://dx.doi.org/10.1002/JBIO.201600303 Journal of Biophotonics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley PMC |
| spellingShingle | 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. Yu, Hanry Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy |
| title | Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy |
| title_full | Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy |
| title_fullStr | Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy |
| title_full_unstemmed | Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy |
| title_short | Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy |
| title_sort | development of a classification model for non alcoholic steatohepatitis nash using confocal raman micro spectroscopy |
| url | http://hdl.handle.net/1721.1/119875 https://orcid.org/0000-0003-2012-9023 https://orcid.org/0000-0003-4698-6488 https://orcid.org/0000-0002-0339-3685 |
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