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

In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.

Keywords: Hepatocellular carcinoma, Early recurrence, Machine learning, XGBoost model, Predictive precision medicine, Clinical utility, Personalized interventions

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.