Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors

Abstract

The diagnosis of cirrhosis in patients with hepatitis C virus (HCV) infection is currently made using a liver biopsy. In this study we have trained and validated artificial neural networks (ANN) with routine clinical host and viral parameters to predict the presence or absence of cirrhosis in patients with chronic HCV infection and assessed and interpreted the role of the different inputs on the ANN classification. Fifteen routine clinical and virological factors were collated from 112 patients who were HCV RNA positive by reverse transcriptase-polymerase chain reaction (RT-PCR). Standard and Ward-type feed-forward fully-connected ANN analyses were carried out both by training the networks with data from 82 patients and subsequently testing with data from 30 patients plus performing leave-one-out tests for the whole patient data set. The ANN results were also compared with those from multiple logistic regression. The performance of both ANN methods was superior compared with the logistic regression. The best performance was obtained with the Ward-type ANNs resulting in a sensitivity of 92% and a specificity of 98.9% together with a predictive value of a positive test of 95% and a predictive value of a negative test of 97% in the leave-one-out test. Hence, further validation of the ANN analysis is likely to provide a non-invasive test for diagnosing cirrhosis in HCV-infected patients.

Systematic review: non‐invasive methods of fibrosis analysis in chronic hepatitis C

“…Artificial neural networks (ANNs), built on clinical variables and patient data sets, have been developed to predict significant fibrosis in patients with chronic hepatitis C. Haydon developed a model of fifteen routine clinical and virological factors, which were collated from 112 HCV patients, with sensitivity, specificity, NPV and PPV values all greater than 92% for Ward‐type ANNs for prediction of cirrhosis 70 . Piscaglia also analysed ANNs in post‐liver transplant patients with 100% sensitivity and NPV 71 and Cucchetti found that ANNs measured the mortality risk of patients with cirrhosis more accurately than the model for end‐stage liver disease (MELD) score 72 …”

The HCV National Register: towards informing the natural history of hepatitis C infection in the UK

“…The registry is supporting an international research project based in Edinburgh. Data sets derived from registration and follow‐up data are being provided to the group to enable them to train and validate an artificial neural network that uses viral and clinical factors to predict cirrhosis in patients with chronic HCV [ 11].…”

Historical features are poor predictors of liver fibrosis in Canadian patients with chronic hepatitis C

“…splenomegaly, thrombocytopenia, elevated AST to ALT ratio) in such a model would hopefully improve its predictive power. In this regard, Haydon and colleagues recently reported promising results using an artificial neural network (ANN) to predict the presence or absence of cirrhosis in HCV‐infected patients [27]. Utilizing 15 routine clinical and virological factors, the ANN had a sensitivity of 92%, specificity of 99%, positive predictive value of 95% and negative predictive value of 97% in the prediction of HCV‐related cirrhosis [27].…”