Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 7, 2024; 30(5): 424-428
Published online Feb 7, 2024. doi: 10.3748/wjg.v30.i5.424
Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine
Abhimati Ravikulan, Kamran Rostami
Abhimati Ravikulan, Kamran Rostami, Department of Gastroenterology, Palmerston North Hospital, Palmerston North 4442, New Zealand
Author contributions: Ravikulan A wrote the first draft; Rostami K reviewed the manuscript; and both authors finalized the editorial.
Conflict-of-interest statement: The authors declare no conflict of interest.
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: Abhimati Ravikulan, Doctor, Research Fellow, Researcher, Department of Gastroenterology, Palmerston North Hospital, 50 Ruahine Street, Roslyn, Palmerston North 4442, New Zealand. arav175@aucklanduni.ac.nz
Received: November 21, 2023
Peer-review started: November 21, 2023
First decision: December 5, 2023
Revised: December 19, 2023
Accepted: January 12, 2024
Article in press: January 12, 2024
Published online: February 7, 2024
Core Tip

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.