OBJECTIVES: Interest in developing noninvasive markers of liver fibrosis continues to increase, especially in recurrent hepatitis C virus infection after liver transplantation. Recently, a model for predicting significant fibrosis (bridging fibrosis and cirrhosis) on the basis of logistic regression and routine laboratory data has been proposed (logit model). The aim of the present study was to evaluate the accuracy of an artificial neural network, a technique reported to work better than logit models in complex biological situations, built on those same clinical variables and data set of patients, in predicting significant fibrosis. METHODS: The neural network was constructed on the training set of 414 protocol biopsies, from liver transplant recipients, and then tested on the remaining 96 biopsies, as validation set. Model performances of neural network and logit model were evaluated and compared by means of areas under receiver operating characteristic curves. RESULTS: With a cutoff value of >0.4 to predict significant fibrosis, the neural network provided sensitivity, specificity, positive and negative predictive values, respectively, of 100, 79.5, 60.5 and 100%, in the validation set. The performance of the neural network was significantly better than that of the logit model (in the validation set area under the curve = 0.93 vs. 0.84; P = 0.045). CONCLUSIONS: Artificial neural network provides accurate prediction of the presence or absence of significant fibrosis from clinical variables, allowing theoretically protocol liver biopsy to be avoided in several instances, a result of particular interest, given the lack of other types of reliable noninvasive indexes of fibrosis in the setting of transplantation.
OBJECTIVES: Interest in developing noninvasive markers of liver fibrosis continues to increase, especially in recurrent hepatitis C virus infection after liver transplantation. Recently, a model for predicting significant fibrosis (bridging fibrosis and cirrhosis) on the basis of logistic regression and routine laboratory data has been proposed (logit model). The aim of the present study was to evaluate the accuracy of an artificial neural network, a technique reported to work better than logit models in complex biological situations, built on those same clinical variables and data set of patients, in predicting significant fibrosis. METHODS: The neural network was constructed on the training set of 414 protocol biopsies, from liver transplant recipients, and then tested on the remaining 96 biopsies, as validation set. Model performances of neural network and logit model were evaluated and compared by means of areas under receiver operating characteristic curves. RESULTS: With a cutoff value of >0.4 to predict significant fibrosis, the neural network provided sensitivity, specificity, positive and negative predictive values, respectively, of 100, 79.5, 60.5 and 100%, in the validation set. The performance of the neural network was significantly better than that of the logit model (in the validation set area under the curve = 0.93 vs. 0.84; P = 0.045). CONCLUSIONS: Artificial neural network provides accurate prediction of the presence or absence of significant fibrosis from clinical variables, allowing theoretically protocol liver biopsy to be avoided in several instances, a result of particular interest, given the lack of other types of reliable noninvasive indexes of fibrosis in the setting of transplantation.