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Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy

Liver fibrosis assessment by biopsy and conventional staining scores is based on histopathological criteria. Variations in sample preparation and the use of semi-quantitative histopathological methods commonly result in discrepancies between medical centers. Thus, minor changes in liver fibrosis mig...

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Autores principales: Wang, Tong-Hong, Chen, Tse-Ching, Teng, Xiao, Liang, Kung-Hao, Yeh, Chau-Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4531344/
https://www.ncbi.nlm.nih.gov/pubmed/26260921
http://dx.doi.org/10.1038/srep12962
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author Wang, Tong-Hong
Chen, Tse-Ching
Teng, Xiao
Liang, Kung-Hao
Yeh, Chau-Ting
author_facet Wang, Tong-Hong
Chen, Tse-Ching
Teng, Xiao
Liang, Kung-Hao
Yeh, Chau-Ting
author_sort Wang, Tong-Hong
collection PubMed
description Liver fibrosis assessment by biopsy and conventional staining scores is based on histopathological criteria. Variations in sample preparation and the use of semi-quantitative histopathological methods commonly result in discrepancies between medical centers. Thus, minor changes in liver fibrosis might be overlooked in multi-center clinical trials, leading to statistically non-significant data. Here, we developed a computer-assisted, fully automated, staining-free method for hepatitis B-related liver fibrosis assessment. In total, 175 liver biopsies were divided into training (n = 105) and verification (n = 70) cohorts. Collagen was observed using second harmonic generation (SHG) microscopy without prior staining, and hepatocyte morphology was recorded using two-photon excitation fluorescence (TPEF) microscopy. The training cohort was utilized to establish a quantification algorithm. Eleven of 19 computer-recognizable SHG/TPEF microscopic morphological features were significantly correlated with the ISHAK fibrosis stages (P < 0.001). A biphasic scoring method was applied, combining support vector machine and multivariate generalized linear models to assess the early and late stages of fibrosis, respectively, based on these parameters. The verification cohort was used to verify the scoring method, and the area under the receiver operating characteristic curve was >0.82 for liver cirrhosis detection. Since no subjective gradings are needed, interobserver discrepancies could be avoided using this fully automated method.
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spelling pubmed-45313442015-08-12 Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy Wang, Tong-Hong Chen, Tse-Ching Teng, Xiao Liang, Kung-Hao Yeh, Chau-Ting Sci Rep Article Liver fibrosis assessment by biopsy and conventional staining scores is based on histopathological criteria. Variations in sample preparation and the use of semi-quantitative histopathological methods commonly result in discrepancies between medical centers. Thus, minor changes in liver fibrosis might be overlooked in multi-center clinical trials, leading to statistically non-significant data. Here, we developed a computer-assisted, fully automated, staining-free method for hepatitis B-related liver fibrosis assessment. In total, 175 liver biopsies were divided into training (n = 105) and verification (n = 70) cohorts. Collagen was observed using second harmonic generation (SHG) microscopy without prior staining, and hepatocyte morphology was recorded using two-photon excitation fluorescence (TPEF) microscopy. The training cohort was utilized to establish a quantification algorithm. Eleven of 19 computer-recognizable SHG/TPEF microscopic morphological features were significantly correlated with the ISHAK fibrosis stages (P < 0.001). A biphasic scoring method was applied, combining support vector machine and multivariate generalized linear models to assess the early and late stages of fibrosis, respectively, based on these parameters. The verification cohort was used to verify the scoring method, and the area under the receiver operating characteristic curve was >0.82 for liver cirrhosis detection. Since no subjective gradings are needed, interobserver discrepancies could be avoided using this fully automated method. Nature Publishing Group 2015-08-11 /pmc/articles/PMC4531344/ /pubmed/26260921 http://dx.doi.org/10.1038/srep12962 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Wang, Tong-Hong
Chen, Tse-Ching
Teng, Xiao
Liang, Kung-Hao
Yeh, Chau-Ting
Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy
title Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy
title_full Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy
title_fullStr Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy
title_full_unstemmed Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy
title_short Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy
title_sort automated biphasic morphological assessment of hepatitis b-related liver fibrosis using second harmonic generation microscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4531344/
https://www.ncbi.nlm.nih.gov/pubmed/26260921
http://dx.doi.org/10.1038/srep12962
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