Novel algorithm for non-invasive assessment of fibrosis in NAFLD.

<h4>Introduction</h4>Various conditions of liver disease and the downsides of liver biopsy call for a non-invasive option to assess liver fibrosis. A non-invasive score would be especially useful to identify patients with slow advancing fibrotic processes, as in Non-Alcoholic Fatty Liver...

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Autores principales:	Jan-Peter Sowa, Dominik Heider, Lars Peter Bechmann, Guido Gerken, Daniel Hoffmann, Ali Canbay
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eeaf9610c91c44919ceae67e4e1f43fc
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spelling	oai:doaj.org-article:eeaf9610c91c44919ceae67e4e1f43fc2021-11-18T07:47:29ZNovel algorithm for non-invasive assessment of fibrosis in NAFLD.1932-620310.1371/journal.pone.0062439https://doaj.org/article/eeaf9610c91c44919ceae67e4e1f43fc2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23638085/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Introduction</h4>Various conditions of liver disease and the downsides of liver biopsy call for a non-invasive option to assess liver fibrosis. A non-invasive score would be especially useful to identify patients with slow advancing fibrotic processes, as in Non-Alcoholic Fatty Liver Disease (NAFLD), which should undergo histological examination for fibrosis.<h4>Patients/methods</h4>Classic liver serum parameters, hyaluronic acid (HA) and cell death markers of 126 patients undergoing bariatric surgery for morbid obesity were analyzed by machine learning techniques (logistic regression, k-nearest neighbors, linear support vector machines, rule-based systems, decision trees and random forest (RF)). Specificity, sensitivity and accuracy of the evaluated datasets to predict fibrosis were assessed.<h4>Results</h4>None of the single parameters (ALT, AST, M30, M60, HA) did differ significantly between patients with a fibrosis score 1 or 2. However, combining these parameters using RFs reached 79% accuracy in fibrosis prediction with a sensitivity of more than 60% and specificity of 77%. Moreover, RFs identified the cell death markers M30 and M65 as more important for the decision than the classic liver parameters.<h4>Conclusion</h4>On the basis of serum parameters the generation of a fibrosis scoring system seems feasible, even when only marginally fibrotic tissue is available. Prospective evaluation of novel markers, i.e. cell death parameters, should be performed to identify an optimal set of fibrosis predictors.Jan-Peter SowaDominik HeiderLars Peter BechmannGuido GerkenDaniel HoffmannAli CanbayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e62439 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jan-Peter Sowa Dominik Heider Lars Peter Bechmann Guido Gerken Daniel Hoffmann Ali Canbay Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
description	<h4>Introduction</h4>Various conditions of liver disease and the downsides of liver biopsy call for a non-invasive option to assess liver fibrosis. A non-invasive score would be especially useful to identify patients with slow advancing fibrotic processes, as in Non-Alcoholic Fatty Liver Disease (NAFLD), which should undergo histological examination for fibrosis.<h4>Patients/methods</h4>Classic liver serum parameters, hyaluronic acid (HA) and cell death markers of 126 patients undergoing bariatric surgery for morbid obesity were analyzed by machine learning techniques (logistic regression, k-nearest neighbors, linear support vector machines, rule-based systems, decision trees and random forest (RF)). Specificity, sensitivity and accuracy of the evaluated datasets to predict fibrosis were assessed.<h4>Results</h4>None of the single parameters (ALT, AST, M30, M60, HA) did differ significantly between patients with a fibrosis score 1 or 2. However, combining these parameters using RFs reached 79% accuracy in fibrosis prediction with a sensitivity of more than 60% and specificity of 77%. Moreover, RFs identified the cell death markers M30 and M65 as more important for the decision than the classic liver parameters.<h4>Conclusion</h4>On the basis of serum parameters the generation of a fibrosis scoring system seems feasible, even when only marginally fibrotic tissue is available. Prospective evaluation of novel markers, i.e. cell death parameters, should be performed to identify an optimal set of fibrosis predictors.
format	article
author	Jan-Peter Sowa Dominik Heider Lars Peter Bechmann Guido Gerken Daniel Hoffmann Ali Canbay
author_facet	Jan-Peter Sowa Dominik Heider Lars Peter Bechmann Guido Gerken Daniel Hoffmann Ali Canbay
author_sort	Jan-Peter Sowa
title	Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
title_short	Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
title_full	Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
title_fullStr	Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
title_full_unstemmed	Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
title_sort	novel algorithm for non-invasive assessment of fibrosis in nafld.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/eeaf9610c91c44919ceae67e4e1f43fc
work_keys_str_mv	AT janpetersowa novelalgorithmfornoninvasiveassessmentoffibrosisinnafld AT dominikheider novelalgorithmfornoninvasiveassessmentoffibrosisinnafld AT larspeterbechmann novelalgorithmfornoninvasiveassessmentoffibrosisinnafld AT guidogerken novelalgorithmfornoninvasiveassessmentoffibrosisinnafld AT danielhoffmann novelalgorithmfornoninvasiveassessmentoffibrosisinnafld AT alicanbay novelalgorithmfornoninvasiveassessmentoffibrosisinnafld
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Novel algorithm for non-invasive assessment of fibrosis in NAFLD.

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