BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
3/26/2026 9:22:59 AM | Browse: 0 | Download: 0
 |
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 |
© 1993-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
