title: Focal liver disease: neural network-aided diagnosis based on clinical and laboratory data.
abstract: OBJECTIVE
  The purpose of this study was to evaluate the use of clinical and laboratory data in the diagnosis of benign or malignant focal liver disease.
  
  
  METHODS
  Diagnosis was made by artificial neural network (NN), a system of simple computing units connected in a specific structural network. Seven clinical and laboratory variables were retrospectively studied in 172 patients with a liver mass (93 benign, 79 malignant) detected with ultrasound. The diagnostic efficacy of NN was compared with a score based on the logistic regression model (Beaujon score).
  
  
  RESULTS
  Although the sensitivity of the Beaujon score and the neural network was similar (4 malignant tumors inversely classified), neural network-aided diagnosis was characterized by higher specificity and accuracy (respectively 98.9% vs 82.5%, P < 0.001, and 97.1% vs 88.4%, P < 0.002).
  
  
  CONCLUSIONS
  In patients with a hepatic mass, neural network is a valuable method for differentiating malignant and benign tumors.
authors.affiliation: Department of Nuclear Medicine, Technical University of Silesia, Katowice, Pologne.
authors.forename: K
authors.initials: K
authors.lastname: Rudzki
authors.forename: M
authors.initials: M
authors.lastname: Hartleb
authors.forename: T
authors.initials: T
authors.lastname: Sadowski
authors.forename: J
authors.initials: J
authors.lastname: Rudzka
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis*; D008113:Liver Neoplasms / Q000175:diagnosis*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D012680:Sensitivity and Specificity