Toward surface quantification of liver fibrosis progression
Name
He-2010-Toward surface quantification of liver fibrosis progression.pdf
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Checksum (MD5)
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Author(s)
He, Yuting
Peng, Qiwen
Kang, Chiang Huen
Tai, Dean C. S.
Xu, Shuoyu
Tuo, Xiaoye
Trasti, Scott
Raja, Anju Mythreyi
So, Peter T. C.
Rajapakse, Jagath
Date Issued
September 2010
Journal
Journal of Biomedical Optics
Publisher
SPIE
Citation
Yuting He, Chiang Huen Kang, Shuoyu Xu, Xiaoye Tuo, Scott Trasti, Dean C. S. Tai, Anju Mythreyi Raja, Qiwen Peng, Peter T. C. So, Jagath C. Rajapakse, Roy Welsch and Hanry Yu, "Toward surface quantification of liver fibrosis progression", J. Biomed. Opt. 15, 056007 (Sep 24, 2010); doi:10.1117/1.3490414 © 2010 SPIE
Version
Final published version
Abstract
Monitoring liver fibrosis progression by liver biopsy is important for certain treatment decisions, but repeated biopsy is invasive. We envision redefinition or elimination of liver biopsy with surface scanning of the liver with minimally invasive optical methods. This would be possible only if the information contained on or near liver surfaces accurately reflects the liver fibrosis progression in the liver interior. In our study, we acquired the second-harmonic generation and two-photon excitation fluorescence microscopy images of liver tissues from bile duct-ligated rat model of liver fibrosis. We extracted morphology-based features, such as total collagen, collagen in bile duct areas, bile duct proliferation, and areas occupied by remnant hepatocytes, and defined the capsule and subcapsular regions on the liver surface based on image analysis of features. We discovered a strong correlation between the liver fibrosis progression on the anterior surface and interior in both liver lobes, where biopsy is typically obtained. The posterior surface exhibits less correlation with the rest of the liver. Therefore, scanning the anterior liver surface would obtain similar information to that obtained from biopsy for monitoring liver fibrosis progression.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Sloan School of Management
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DOI of Published Version
https://doi.org/10.1117/1.3490414
