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|Title:||Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy|
Kang, Jeon Woong
Tam, Zhi Yang
Fong Li Shan Eliza
Singh, Surya Pratap
So, Peter T. C.
|Keywords:||Raman micro-spectroscopic imaging|
biochemical component analysis
non-alcoholic fatty liver disease
|Source:||Yan, Jie, Yu, Yang, Kang, Jeon Woong, Tam, Zhi Yang, Xu, Shuoyu, Fong Li Shan Eliza, Singh, Surya Pratap, Song, Ziwei, Lisa Tucker-Kellogg, So, Peter T. C., Yu,Hanry (2017-12-01). Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy. Journal of biophotonics 10 (12) : 1703-1713. ScholarBank@NUS Repository. https://doi.org/10.1002/jbio.201600303|
|Abstract:||Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries . 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.|
|Source Title:||Journal of biophotonics|
|Appears in Collections:||Staff Publications|
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