Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 21, 2024; 30(7): 631-635
Published online Feb 21, 2024. doi: 10.3748/wjg.v30.i7.631
From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring
Mariana Michelle Ramírez-Mejía, Nahum Méndez-Sánchez
Mariana Michelle Ramírez-Mejía, Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
Mariana Michelle Ramírez-Mejía, Nahum Méndez-Sánchez, Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
Nahum Méndez-Sánchez, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
Author contributions: Méndez-Sánchez N and Ramírez-Mejía MM contributed to this paper; Méndez-Sánchez N designed the overall concept and outline of the manuscript; Ramírez-Mejía MM contributed to the discussion and design of the manuscript; Méndez-Sánchez N and Ramírez-Mejía MM contributed to the writing and editing of the manuscript, the illustrations, and the review of the literature.
Conflict-of-interest statement: All the authors declare that they have no conflicts of interest related to the manuscript.
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: Nahum Méndez-Sánchez, FAASLD, AGAF, FACG, MD, MSc, PhD, Doctor, Professor, Liver Research Unit, Medica Sur Clinic & Foundation, Puente de Piedra 150, Col. Toriello Guerra, Distrito Federal 14050, Mexico. nah@unam.mx
Received: November 13, 2023
Peer-review started: November 13, 2023
First decision: December 5, 2023
Revised: December 12, 2023
Accepted: January 22, 2024
Article in press: January 22, 2024
Published online: February 21, 2024
Processing time: 100 Days and 5.3 Hours
Core Tip

Core Tip: Machine learning is an important approach for personalized oncology care, as it paves the way for precise and individualized postoperative strategies, thereby enhancing patient outcomes in the field of hepatocellular carcinoma treatment. Ongoing collaboration, larger sample sizes, and multicenter studies are crucial for validating and refining this innovative predictive model, thus ensuring its applicability and reliability in diverse clinical settings.