Two Classifiers Based on Serum Peptide Pattern for Prediction of HBV-Induced Liver Cirrhosis Using MALDI-TOF MS

Abstract

Chronic infection with hepatitis B virus (HBV) is associated with the majority of cases of liver cirrhosis (LC) in China. Although liver biopsy is the reference method for evaluation of cirrhosis, it is an invasive procedure with inherent risk. The aim of this study is to discover novel noninvasive specific serum biomarkers for the diagnosis of HBV-induced LC. We performed bead fractionation/MALDI-TOF MS analysis on sera from patients with LC. Thirteen feature peaks which had optimal discriminatory performance were obtained by using support-vector-machine-(SVM-) based strategy. Based on the previous results, five supervised machine learning methods were employed to construct classifiers that discriminated proteomic spectra of patients with HBV-induced LC from those of controls. Here, we describe two novel methods for prediction of HBV-induced LC, termed LC-NB and LC-MLP, respectively. We obtained a sensitivity of 90.9%, a specificity of 94.9%, and overall accuracy of 93.8% on an independent test set. Comparisons with the existing methods showed that LC-NB and LC-MLP held better accuracy. Our study suggests that potential serum biomarkers can be determined for discriminating LC and non-LC cohorts by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. These two classifiers could be used for clinical practice in HBV-induced LC assessment.

An MLP Classifier for Prediction of HBV-Induced Liver Cirrhosis Using Routinely Available Clinical Parameters

“…Although liver biopsy has been the “golden standard” for evaluation of stage of liver fibrosis and cirrhosis, it is limited as it is an invasive procedure with significant expense, manpower issues, and some risks [ 23 ]. Therefore, there is a need for a simple, reliable, and noninvasive alternative method for regular monitoring of disease progression [ 17 ]. In this study, we filtered out seven routine clinical parameters for the prediction of HBV-induced liver cirrhosis by statistical comparison of those of LC and CHB, including age, ALT, AST, PT, PLT, HGB, and RDW.…”

“…Classifiers were constructed based upon the training set using multilayered perceptron (MLP) and Naïve Bayes (NB) method in WEKA. A 10-fold cross-validation was performed to avoid model-specific overfitting, as previously described [ 17 ]. Briefly, all the entries were randomly divided into ten parts; nine sets were used for training and the remaining one for testing.…”

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

“…These tests have not been amenable to routine use in screening patients for cirrhosis: Although their specificity may be adequate, their sensitivity is insufficient. ML models have been used to detect fibrosis or cirrhosis secondary to different liver disease etiologies based on blood tests . These studies have predicted the risk of cirrhosis in order to reduce the overall number of liver biopsies done on patients with known risk factors.…”

“…ML models have been used to detect fibrosis or cirrhosis secondary to different liver disease etiologies based on blood tests. (34)(35)(36)(37)(38)(39)(40)(41) These studies have predicted the risk of cirrhosis in order to reduce the overall number of liver biopsies done on patients with known risk factors. Diagnostic performance of these models has varied depending on the etiology of liver disease and study size.…”

Rapid Detection System for Hepatitis B Surface Antigen (HBsAg) Based on Immunomagnetic Separation, Multi-Angle Dynamic Light Scattering and Support Vector Machine

“…Further, the literature study shows that extensive research has been carried out by using a machine learning approach to classify HBsAg for the identification of HBV. Yuan et al investigated five different classifiers for the HBV detection by using the acquired spectra from MALDI-TOF MS [12]. Runmin et al constructed and compared machine learning algorithms with the FIB-4 score for the clinical prediction of HBV [13].…”