Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population  

作　　者：Navneet Kaur Gitanjali Goyal Ravinder Garg Chaitanya Tapasvi Umit Demirbaga 

机构地区：[1]Department of Biochemistry,Guru Gobind Singh Medical College and Hospital,Faridkot 151203,Punjab,India [2]Department of Biochemistry,All India Institute of Medical Sciences,Bathinda 151005,Punjab,India [3]Department of Medicine,Guru Gobind Singh Medical College and Hospital,Baba Farid University of Health Sciences,Faridkot 151203,Punjab,India [4]Department of Radiodiagnosis,Guru Gobind Singh Medical College and Hospital,Baba Farid University of Health Sciences,Faridkot 151203,India [5]Department of Computer Engineering,Bartin University,Bartin 74100,Türkiye [6]Department of Medicine,University of Cambridge,Cambridge CB20QQ,United Kingdom [7]European Bioinformatics Institute,Wellcome Genome,Cambridge CB101SD,United Kingdom

出　　处：《World Journal of Methodology》2024年第3期90-105,共16页世界方法学杂志

摘　　要：BACKGROUND Hepatitis C virus(HCV)infection progresses through various phases,starting with inflammation and ending with hepatocellular carcinoma.There are several invasive and non-invasive methods to diagnose chronic HCV infection.The invasive methods have their benefits but are linked to morbidity and complications.Thus,it is important to analyze the potential of non-invasive methods as an alternative.Shear wave elastography(SWE)is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis.Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards.AIM To analyzed the diagnostic efficacy of non-invasive indices[serum fibronectin,aspartate aminotransferase to platelet ratio index(APRI),alanine aminotransferase ratio(AAR),and fibrosis-4(FIB-4)]in relation to SWE.We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity.METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay.We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis.We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects.RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages(mild,moderate,and severe).This was based on its sensitivity(100%,92.2%,96.2%),specificity(96%,100%,98.6%),Youden’s index(0.960,0.922,0.948),area under receiver operating characteristic curve(0.999,0.993,0.922),and Likelihood test(LR+>10 and LR-<0.1).Additionally,our Bayesian Network analysis revealed that fibronectin(>200),AAR(>1),APRI(>3),and FIB-4(>4)were all strongly associated with patients who had severe fibr

关 键 词：Hepatitis C virus Non-invasive biomarkers Shear wave elastography FIBRONECTIN Bayesian network Machine learning Liver fibrosis 

分 类 号：R512.63[医药卫生—内科学]