BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
3/26/2026 9:22:59 AM | Browse: 19 | Download: 68
 |
Received |
|
2025-12-08 06:58 |
|
Peer-Review Started |
|
2025-12-08 06:59 |
|
First Decision by Editorial Office Director |
|
2025-12-25 08:24 |
|
Return for Revision |
|
2025-12-25 08:24 |
|
Revised |
|
2025-12-28 14:08 |
|
Publication Fee Transferred |
|
2025-12-31 13:31 |
|
Second Decision by Editor |
|
2026-02-02 02:52 |
 |
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2026-02-02 12:25 |
|
Articles in Press |
|
2026-02-02 12:25 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2026-03-11 08:29 |
|
Publish the Manuscript Online |
|
2026-03-26 09:22 |
| ISSN |
1948-5182 (online) |
| 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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Azra Bashir, Renuka Arora, Deepti Mehrotra, Manju Bala, Arshed H Parry, Asif Iqball, Shabir A Bhat and Zeeshan A Wani |
| ORCID |
|
| Funding Agency and Grant Number |
|
| Corresponding Author |
Arshed H Parry, Assistant Professor, Department of Radiodiagnosis and Imaging, Government Medical College, 10, Karanagar, Srinagar 190010, Jammu and Kashmir, India. arshedparry@gmail.com |
| Key Words |
Chronic hepatitis C; Non-invasive fibrosis assessment; Hepatic fibrosis; Lipid biomarkers; Platelet count; Machine learning; Random forest; AdaBoost |
| Core Tip |
This study developed and validated a simple non-invasive fibrosis risk score for hepatitis C virus using platelet count, lipid markers, and liver function parameters. The score outperformed commonly used clinical tools for detecting significant hepatic fibrosis. Additionally, machine-learning models were evaluated, which outperformed the commonly used clinical risk scoring systems with random forest and AdaBoost demonstrating the highest diagnostic performance for detecting significant hepatic fibrosis in hepatitis C virus. |
| Publish Date |
2026-03-26 09:22 |
| Citation |
Bashir A, Arora R, Mehrotra D, Bala M, Parry AH, Iqball A, Bhat SA, Wani ZA. Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models. World J Hepatol 2026; 18(3): 117465 |
| URL |
https://www.wjgnet.com/1948-5182/full/v18/i3/117465.htm |
| DOI |
https://dx.doi.org/10.4254/wjh.v18.i3.117465 |
All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.
