Prediction of asymptomatic cirrhosis in chronic hepatitis  C patients: accuracy of artificial neural networks compared  with logistic regression models.
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.
Cazzaniga
Massimo
M
Department of Internal Medicine, Policlinico San Donato, University of Milan, Milan, Italy. massimo.cazzaniga@unimi.it
Salerno
Francesco
F
Borroni
Gianmario
G
Ceriani
Roberto
R
Stucchi
Giulia
G
Guerzoni
Patrizia
P
Casiraghi
Maria Antonietta
MA
Tommasini
Maurizio
M
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
D001219:Aspartate Aminotransferases