title: Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Gastroenterology and Hepatology, University Hospital, University Duisburg-Essen, Essen, Germany.
authors: Jan-Peter
authors: JP
authors: Sowa
authors: Dominik
authors: D
authors: Heider
authors: Lars Peter
authors: LP
authors: Bechmann
authors: Guido
authors: G
authors: Gerken
authors: Daniel
authors: D
authors: Hoffmann
authors: Ali
authors: A
authors: Canbay
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D005234:Fatty Liver / Q000097:blood* / Q000150:complications* / Q000473:pathology; D005260:Female; D006801:Humans; D008099:Liver / Q000473:pathology*; D008103:Liver Cirrhosis / Q000097:blood* / Q000150:complications* / Q000175:diagnosis / Q000473:pathology; D008297:Male; D008875:Middle Aged; D065626:Non-alcoholic Fatty Liver Disease; D011379:Prognosis
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