Publication Name	World Journal of Gastroenterology
Manuscript ID	90033
Country	New Zealand

Received	2023-11-21 04:26
Peer-Review Started	2023-11-21 04:27
To Make the First Decision
Return for Revision	2023-12-05 23:13
Revised	2023-12-19 02:29
Second Decision	2024-01-12 02:40
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-01-12 05:28
Articles in Press	2024-01-12 05:28
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-01-25 08:56
Publish the Manuscript Online	2024-01-31 07:03

ISSN	1007-9327 (print) and 2219-2840 (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: https://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
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Category	Gastroenterology & Hepatology
Manuscript Type	Editorial
Article Title	Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine
Manuscript Source	Invited Manuscript
All Author List	Abhimati Ravikulan and Kamran Rostami
Funding Agency and Grant Number
Corresponding Author	Abhimati Ravikulan, Doctor, Research Fellow, Researcher, Department of Gastroenterology, Palmerston North Hospital, No. 50 Ruahine Street, Roslyn, Palmerston North 4442, New Zealand. arav175@aucklanduni.ac.nz
Key Words	Machine learning; Artificial intelligence; Hepatocellular carcinoma; Hepatology; Early recurrence; Liver resection
Core Tip	This study addresses the crucial issue of early recurrence in hepatocellular carcinoma, emphasizing the significance of aggressive tumour characteristics. random survival forests, a machine learning model, surpasses conventional COX proportional hazard models, offering improved prediction, clinical usefulness, and overall performance. The model's ability to stratify risk facilitates targeted postoperative strategies, showcasing its potential as a guide for personalized patient care.
Publish Date	2024-01-31 07:03
Citation	Ravikulan A, Rostami K. Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine. World J Gastroenterol 2024; 30(5): 424-428
URL	https://www.wjgnet.com/1007-9327/full/v30/i5/424.htm
DOI	https://dx.doi.org/10.3748/wjg.v30.i5.424

Full Article (PDF)	WJG-30-424-with-cover.pdf
Full Article (Word)	WJG-30-424.docx
Manuscript File	90033_Auto_Edited-YJP.docx
Answering Reviewers	90033-Answering reviewers.pdf
Audio Core Tip	90033-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	90033-Conflict-of-interest statement .pdf
Copyright License Agreement	90033-Copyright license agreement.pdf
Peer-review Report	90033-Peer-review(s).pdf
Scientific Misconduct Check	90033-Bing-Qu XL-2.png
Scientific Editor Work List	90033-Scientific editor work list.pdf
CrossCheck Report	90033-CrossCheck report.pdf