Observational Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Sep 20, 2024; 14(3): 91058
Published online Sep 20, 2024. doi: 10.5662/wjm.v14.i3.91058
Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population
Navneet Kaur, Gitanjali Goyal, Ravinder Garg, Chaitanya Tapasvi, Umit Demirbaga
Navneet Kaur, Department of Biochemistry, Guru Gobind Singh Medical College and Hospital, Faridkot 151203, Punjab, India
Gitanjali Goyal, Department of Biochemistry, All India Institute of Medical Sciences, Bathinda 151005, Punjab, India
Ravinder Garg, Department of Medicine, Guru Gobind Singh Medical College and Hospital, Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India
Chaitanya Tapasvi, Department of Radiodiagnosis, Guru Gobind Singh Medical College and Hospital, Baba Farid University of Health Sciences, Faridkot 151203, India
Umit Demirbaga, Department of Computer Engineering, Bartin University, Bartin 74100, Türkiye
Umit Demirbaga, Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
Umit Demirbaga, European Bioinformatics Institute, Wellcome Genome, Cambridge CB10 1SD, United Kingdom
Author contributions: Kaur N designed the research study, performed the research, contributed to the statistical and analytical analysis and did constructive writing along with main revision of the study; Goyal G conceptualized the study, guided in research and paper writing; Garg R and Tapasvi C guided in research and gave inputs in writing; Demirbaga U performed data analysis using machine learning and contributed to paper writing; All authors have read and finalized the manuscript.
Institutional review board statement: The study was reviewed and approved by the Institutional Ethics Committee, GGSMC, Faridkot, India (No. GGS/IEC/18/84).
Informed consent statement: All the recruited subjects gave informed consent, regardless of sex or age.
Conflict-of-interest statement: The authors declare that there are no conflicts of interest to disclose.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Navneet Kaur, MSc, Research Fellow, Department of Biochemistry, Guru Gobind Singh Medical College and Hospital, Sadiq Road, Faridkot- 151203, Faridkot 151203, Punjab, India. navmann23@yahoo.com
Received: December 21, 2023
Revised: January 28, 2024
Accepted: March 21, 2024
Published online: September 20, 2024
Processing time: 187 Days and 9 Hours
Core Tip

Core Tip: The role of non-invasive indices (including serum fibronectin) was investigated to assess and differentiate liver fibrosis in untreated hepatitis C virus (HCV)-infected patients. The overall assessment and prediction process involved the correlation of fibronectin, alanine aminotransferase ratio, aspartate aminotransferase to platelet ratio index, and fibrosis-4 with severity staging performed through shear wave elastography. The role of non-invasive indices to assess and differentiate liver fibrosis is further validated through the calculation of diagnostic accuracy measured using various standard methods such as, sensitivity and specificity, Youden's index, area under receiver operating characteristic curve, and likelihood test. We have explored machine learning-based analysis using a Bayesian Network to predict and validate the diagnostic ability of non-invasive indices for predicting liver fibrosis in HCV patients.