Prediction of Liver Fibrosis and Cirrhosis in Chronic Hepatitis B Infection by Serum Proteomic Fingerprinting: A Pilot Study

Background: Most noninvasive predictive models of liver fibrosis are complicated and have suboptimal sensitivity. This study was designed to identify serum proteomic signatures associated with liver fibrosis and to develop a proteome-based fingerprinting model for prediction of liver fibrosis. Methods: Serum proteins from 46 patients with chronic hepatitis B (CHB) were profiled quantitatively on sur-face-enhanced laser desorption/ionization (SELDI) Pro-teinChip arrays. The identified liver fibrosis-associated proteomic fingerprint was used to construct an artificial neural network (ANN) model that produced a fibrosis index with a range of 0–6. The clinical value of this index was evaluated by leave-one-out cross-validation. Results: Thirty SELDI proteomic features were signifi