title: Parenchymal echo patterns of cirrhotic liver analysed with a neural network for risk of hepatocellular carcinoma.
abstract: BACKGROUND
  To objectively evaluate the parenchymal echo patterns of the liver in cirrhosis, an image analysing system in which a neural network is used has been found capable of numerically calculating coarse score (CS). Using this system, we analysed whether or not CS can serve as a predictive factor for the development of hepatocellular carcinoma (HCC).
  
  
  METHODS
  The risk factors for HCC were evaluated in 95 patients with liver cirrhosis with an average follow-up period of 2041 +/- 823 days. We used a three-layer feed-forward neural network and a back-propagation algorithm to calculate CS.
  
  
  RESULTS
  There were strong correlations between CS, alanine aminotransferase (ALT) and alpha-fetoprotein (AFP) and the average cumulative incidence rate of HCC evaluated by the Cox's proportional hazards model. The adjusted rate ratios were estimated to be 3.00, 2.80 and 2.01, respectively. The cumulative risks of HCC were significantly higher with an initial CS > or = 1.5 than with an initial CS < 1.5, with ALT > or = 80 IU/L than with initial ALT < 80 IU/L and with AFP > or = 20 ng/mL than with initial AFP < 20 ng/mL, all analysed by the log-rank test.
  
  
  CONCLUSIONS
  Coarse score is a useful predictor for development of HCC.
authors.affiliation: First Department of Medicine, School of Medicine, Chiba University, Japan. fukuhiro@ho.chiba-u.ac.jp
authors.forename: H
authors.initials: H
authors.lastname: Fukuda
authors.forename: M
authors.initials: M
authors.lastname: Ebara
authors.forename: A
authors.initials: A
authors.lastname: Kobayashi
authors.forename: N
authors.initials: N
authors.lastname: Sugiura
authors.forename: M
authors.initials: M
authors.lastname: Yoshikawa
authors.forename: H
authors.initials: H
authors.lastname: Saisho
authors.forename: F
authors.initials: F
authors.lastname: Kondo
authors.forename: T
authors.initials: T
authors.lastname: Yahagi
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D006528:Carcinoma, Hepatocellular / Q000097:blood / Q000000981:diagnostic imaging* / Q000473:pathology; D003952:Diagnostic Imaging; D005260:Female; D006801:Humans; D008103:Liver Cirrhosis / Q000097:blood / Q000000981:diagnostic imaging* / Q000473:pathology; D008113:Liver Neoplasms / Q000097:blood / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012307:Risk Factors; D014463:Ultrasonography; D000509:alpha-Fetoproteins / Q000032:analysis
chemical_list: D015415:Biomarkers; D000509:alpha-Fetoproteins; D000410:Alanine Transaminase