Serum N-glycan markers for diagnosing liver fibrosis induced by hepatitis B virus

BACKGROUND Hepatitis B virus (HBV) infection is the primary cause of hepatitis with chronic HBV infection,which may develop into liver fibrosis,cirrhosis and hepatocellular carcinoma.Detection of early-stage fibrosis related to HBV infection is of great clinical significance to block the progression of liver lesion.Direct liver biopsy is regarded as the gold standard to detect and assess fibrosis;however,this method is invasive and prone to clinical sampling error.In order to address these issues,we attempted to find more convenient and effective serum markers for detecting HBV-induced early-stage liver fibrosis.AIM To investigate serum N-glycan profiling related to HBV-induced liver fibrosis and verify multiparameter diagnostic models related to serum N-glycan changes.METHODS N-glycan profiles from the sera of 432 HBV-infected patients with liver fibrosis were analyzed.Significant changed N-glycan levels (peaks)(P <0.05) in differentfibrosis stages were selected in the modeling group,and multiparameter diagnostic models were established based on changed N-glycan levels by logistic regression analysis.The receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic efficacy of N-glycans models.These models were then compared with the aspartate aminotransferase to platelet ratio index (APRI),fibrosis index based on the four factors (FIB-4),glutamyltranspeptidase platelet albumin index (S index),GlycoCirrho-test,and GlycoFibro-test.Furthermore,we combined multiparameter diagnostic models with alanine aminotransferase (ALT) and platelet (PLT) tests and compared their diagnostic power.In addition,the diagnostic accuracy of N-glycan models was also verified in the validation group of patients.RESULTS Multiparameter diagnostic models constructed based on N-glycan peak 1,3,4and 8 could distinguish between different stages of liver fibrosis.The area under ROC curves (AUROCs) of Model A and Model B were 0.890 and 0.752,respectively differentiating fibrosis F0-F1 from F2-F4,and F0-F2 from F3-F4,and surpassing other serum panels.However,AUROC (0.747) in Model C used for the diagnosis of F4 from F0-F3 was lower than AUROC (0.795) in FIB-4.In combination with ALT and PLT,the multiparameter models showed better diagnostic power (AUROC=0.912,0.829,0.885,respectively) when compared with other models.In the validation group,the AUROCs of the three combined models (0.929,0.858,and 0.867,respectively) were still satisfactory.We also applied the combined models to distinguish adjacent fibrosis stages of 432patients (F0-F1/F2/F3/F4),and the AUROCs were 0.917,0.720 and 0.785.CONCLUSION Multiparameter models based on serum N-glycans are effective supplementary markers to distinguish between adjacent fibrosis stages of patients caused by HBV,especially in combination with ALT and PLT.

LEARN algorithm:a novel option for predicting non-alcoholic steatohepatitis

Background:There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis(NASH).Since impedance-based measurements of body composition are simple,repeatable and have a strong association with non-alcoholic fatty liver disease(NAFLD)severity,we aimed to develop a novel and fully automatic machine learning algorithm,consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH[the bioeLectrical impEdance Analysis foR Nash(LEARN)algorithm].Methods:A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China,of which 766 patients with biopsy-proven NAFLD were included in final analysis.These patients were randomly subdivided into the training and validation groups,in a ratio of 4:1.The LEARN algorithm was developed in the training group to identify NASH,and subsequently,tested in the validation group.Results:The LEARN algorithm utilizing impedance-based measurements of body composition along with age,sex,pre-existing hypertension and diabetes,was able to predict the likelihood of having NASH.This algorithm showed good discriminatory ability for identifying NASH in both the training and validation groups[area under the receiver operating characteristics(AUROC):0.81,95%CI:0.77-0.84 and AUROC:0.80,95%CI:0.73-0.87,respectively].This algorithm also performed better than serum cytokeratin-18 neoepitope M30(CK-18 M30)level or other non-invasive NASH scores(including HAIR,ION,NICE)for identifying NASH(P value<0.001).Additionally,the LEARN algorithm performed well in identifying NASH in different patient subgroups,as well as in subjects with partial missing body composition data.Conclusions:The LEARN algorithm,utilizing simple easily obtained measures,provides a fully automated,simple,non-invasive method for identifying NASH.

A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease:a derivation and independent validation study

Background:Currently,there are no effective methods for assessing hepatic inflammation without resorting to histological examination of liver tissue obtained by biopsy.T2-weighted images(T2WI)are routinely obtained from liver magnetic resonance imaging(MRI)scan sequences.We aimed to establish a radiomics signature based on T2WI(T2-RS)for assessment of hepatic inflammation in people with nonalcoholic fatty liver disease(NAFLD).Methods:A total of 203 individuals with biopsy-confirmed NAFLD from two independent Chinese cohorts with liver MRI examination were enrolled in this study.The hepatic inflammatory activity score(IAS)was calculated by the unweighted sum of the histologic scores for lobular inflammation and ballooning.One thousand and thirty-two radiomics features were extracted from the localized region of interest(ROI)in the right liver lobe of T2WI and,subsequently,selected by minimum redundancy maximum relevance and least absolute shrinkage and selection operator(LASSO)methods.The T2-RS was calculated by adding the selected features weighted by their coefficients.Results:Eighteen radiomics features from Laplacian of Gaussian,wavelet,and original images were selected for establishing T2-RS.The T2-RS value differed significantly between groups with increasing grades of hepatic inflammation(P<0.01).The T2-RS yielded an area under the receiver operating characteristic(ROC)curve(AUROC)of 0.80[95%confidence interval(CI):0.71-0.89]for predicting hepatic inflammation in the training cohort with excellent calibration.The AUROCs of T2-RS in the internal cohort and external validation cohorts were 0.77(0.61-0.93)and 0.75(0.63-0.84),respectively.Conclusions:The T2-RS derived from radiomics analysis of T2WI shows promising utility for predicting hepatic inflammation in individuals with NAFLD.