title: Irregularity of parenchymal echo patterns of liver analyzed with a neural network and risk of hepatocellular carcinoma in liver cirrhosis.
abstract: OBJECTIVE
  In this study, we scored the hepatic parenchymal echo patterns as the coarse score (CS) analyzed with a neural network in cirrhosis patients and calculated the variations in CS as the coefficient of variation, and evaluate their usefulness as predictor of the development of hepatocellular carcinoma (HCC).
  
  
  METHODS
  The relationship between the degree of variation in CS and histopathological findings was assessed in 10 autopsied livers fixed in formalin. The degree of intrahepatic variation in CS was calculated as the coefficient of variation of CS (CVCS). Irregular regeneration of liver cells in autopsied livers was classified into two categories, slight and severe. A total of 56 cirrhosis patients were prospectively followed to evaluate the predictors of HCC.
  
  
  RESULTS
  A significant positive correlation was observed between CVCS and the coefficient of variation of the diameter of the regenerative nodules. Coefficient variation of nodule diameter and CVCS in patients in whom irregular regeneration was severe were significantly higher than those in patients in whom irregular regeneration was mild (p < 0.05). Concerning the relationship between hepatitis virus markers and CS or CVCS, CVCS was significantly higher in those who were hepatitis C virus antibody positive and those who were hepatitis B surface antigen negative (p < 0.01). Using a combination of CS and CVCS, the incidence of HCC, as determined by the Kaplan-Meier method, was significantly higher in patients whose CS was > or = 1.5 and CVCS was > or = 15%, as compared with that in patients whose CS was <1.5 and CVCS was <15% (p < 0.01) and whose CS was > or = 1.5 and CVCS was <15% (p < 0.05). Multivariate analysis of the predictors for HCC using the Cox's proportional hazards model showed a significant correlation between the risk of development of HCC and CVCS, CS and serum alpha-fetoprotein level.
  
  
  CONCLUSIONS
  CVCS and CS can be calculated from liver echo patterns and are useful for identifying a high-risk group for HCC.
authors.affiliation: Department of Medicine and Clinical Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan. fukuhiro@ho.chiba-u.ac.jp
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Fukuda
authors.forename: Masaaki
authors.initials: M
authors.lastname: Ebara
authors.forename: Akira
authors.initials: A
authors.lastname: Kobayashi
authors.forename: Nobuyuki
authors.initials: N
authors.lastname: Sugiura
authors.forename: Masaharu
authors.initials: M
authors.lastname: Yoshikawa
authors.forename: Hiromitsu
authors.initials: H
authors.lastname: Saisho
authors.forename: Kazuki
authors.initials: K
authors.lastname: Kato
authors.forename: Fukuo
authors.initials: F
authors.lastname: Kondo
authors.forename: Takashi
authors.initials: T
authors.lastname: Yahagi
mesh_terms: D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D006528:Carcinoma, Hepatocellular / Q000097:blood / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006514:Hepatitis B Surface Antigens / Q000032:analysis; D018937:Hepatitis C Antibodies / Q000097:blood; D006801:Humans; D015994:Incidence; D008099:Liver / Q000473:pathology; D008103:Liver Cirrhosis / Q000097:blood / Q000000981:diagnostic imaging* / Q000473:pathology; D008110:Liver Extracts; D008113:Liver Neoplasms / Q000097:blood / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012307:Risk Factors; D014463:Ultrasonography; D000509:alpha-Fetoproteins / Q000032:analysis
chemical_list: D015415:Biomarkers; D006514:Hepatitis B Surface Antigens; D018937:Hepatitis C Antibodies; D008110:Liver Extracts; D000509:alpha-Fetoproteins; D000410:Alanine Transaminase