title: Prediction of asymptomatic cirrhosis in chronic hepatitis  C patients: accuracy of artificial neural networks compared  with logistic regression models.
abstract: OBJECTIVE
  Models based on logistic regression analysis are proposed as noninvasive tools to predict cirrhosis in chronic hepatitis C (CHC) patients. However, none showed to be sufficiently accurate to replace liver biopsy. Artificial neural networks (ANNs), providing a prediction based on nonlinear algorithms, can improve the diagnosis of cirrhosis, a syndrome characterized by complex, nonlinear biological alterations. We compared ANNs with two logistic regression analysis-based models in predicting CHC histologically proven cirrhosis.
  
  
  METHODS
  Liver biopsy was obtained in CHC patients of two different cohorts (an internal cohort including 244 patients and an external cohort including 220 patients). One hundred and forty-four patients from the internal cohort served as a training set to construct ANNs and a logistic regression model (LOGIT). These two models and the aspartate aminotransferase-to-platelet ratio index (APRI) were tested in the remaining 100 patients (internal validation set) and in the external cohort (external validation set). Diagnostic performances were evaluated by standard indices of accuracy.
  
  
  RESULTS
  In the internal validation set, ANNs, LOGIT, and APRI showed similar discrimination powers (0.88, 0.87, and 0.87 respectively). However, ANNs showed the best positive predictive value (0.86 vs. 0.67 and 0.56) and positive likelihood ratio (40.2 vs. 13.4 and 8.4). In the external validation set, the discrimination power of ANNs (0.76) was significantly higher than those of LOGIT (0.67) and APRI (0.67).
  
  
  CONCLUSIONS
  Compared to conventional models, ANNs performance in predicting CHC cirrhosis is slightly better and more reproducible.
authors.affiliation: Department of Internal Medicine, Policlinico San Donato, University of Milan, Milan, Italy. massimo.cazzaniga@unimi.it
authors.forename: Massimo
authors.initials: M
authors.lastname: Cazzaniga
authors.forename: Francesco
authors.initials: F
authors.lastname: Salerno
authors.forename: Gianmario
authors.initials: G
authors.lastname: Borroni
authors.forename: Roberto
authors.initials: R
authors.lastname: Ceriani
authors.forename: Giulia
authors.initials: G
authors.lastname: Stucchi
authors.forename: Patrizia
authors.initials: P
authors.lastname: Guerzoni
authors.forename: Maria Antonietta
authors.initials: MA
authors.lastname: Casiraghi
authors.forename: Maurizio
authors.initials: M
authors.lastname: Tommasini
mesh_terms: D000328:Adult; D000367:Age Factors; D001219:Aspartate Aminotransferases / Q000097:blood; D001707:Biopsy, Needle; D004812:Epidemiologic Methods; D005260:Female; D019698:Hepatitis C, Chronic / Q000150:complications* / Q000473:pathology; D006801:Humans; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000473:pathology / Q000821:virology*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010976:Platelet Count; D012737:Sex Factors
chemical_list: D001219:Aspartate Aminotransferases