Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2025; 17(2): 102151
Published online Feb 15, 2025. doi: 10.4251/wjgo.v17.i2.102151
Enhancing rectal cancer liver metastasis prediction: Magnetic resonance imaging-based radiomics, bias mitigation, and regulatory considerations
Yuwei Zhang
Yuwei Zhang, Department of Digital Health, Northern Medical Center, Middletown, NY 10940, United States
Author contributions: Zhang Y solely contributed to the conceptualization, drafting, and writing of this manuscript.
Conflict-of-interest statement: The author declares that they have 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: Yuwei Zhang, MD, PhD, Department of Digital Health, Northern Medical Center, 14 Jason Pl Ste 201, Middletown, NY 10940, United States. yuwei_zhang@gwu.edu
Received: October 14, 2024
Revised: November 20, 2024
Accepted: December 2, 2024
Published online: February 15, 2025
Processing time: 95 Days and 15.9 Hours
Core Tip

Core Tip: The early detection of metachronous liver metastasis (MLM) in rectal cancer patients remains a challenge due to tumor heterogeneity and limitations in imaging methods. Long et al’s magnetic resonance imaging (MRI)-based radiomics model, integrated with machine learning algorithms, offers a novel non-invasive solution for improving MLM prediction. This approach has the potential to enhance personalized treatment planning and patient outcomes. However, addressing sources of bias such as MRI variability and patient heterogeneity, along with aligning the model with Food and Drug Administration regulations on artificial intelligence-based medical technologies, will be critical to successful clinical integration.