Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	102151
Country	United States

Received	2024-10-14 16:29
Peer-Review Started	2024-10-14 16:29
To Make the First Decision
Return for Revision	2024-11-11 07:39
Revised	2024-11-20 01:43
Second Decision	2024-12-02 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-12-02 08:04
Articles in Press	2024-12-02 08:04
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-12-17 09:09
Publish the Manuscript Online	2025-01-18 08:12

ISSN	1948-5204 (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/
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Category	Oncology
Manuscript Type	Letter to the Editor
Article Title	Enhancing rectal cancer liver metastasis prediction: Magnetic resonance imaging-based radiomics, bias mitigation, and regulatory considerations
Manuscript Source	Unsolicited Manuscript
All Author List	Yuwei Zhang
ORCID	Author(s) ORCID Number Yuwei Zhang http://orcid.org/0009-0006-2377-9522
Funding Agency and Grant Number
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
Key Words	Metachronous liver metastasis; Radiomics; Machine learning; Rectal cancer; Magnetic resonance imaging variability; Bias mitigation; Food and drug administration regulations; Predictive modeling
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.
Publish Date	2025-01-18 08:12
Citation	<p>Zhang Y. Enhancing rectal cancer liver metastasis prediction: Magnetic resonance imaging-based radiomics, bias mitigation, and regulatory considerations. <i>World J Gastrointest Oncol</i> 2025; 17(2): 102151</p>
URL	https://www.wjgnet.com/1948-5204/full/v17/i2/102151.htm
DOI	https://dx.doi.org/10.4251/wjgo.v17.i2.102151

Full Article (PDF)	WJGO-17-102151-with-cover.pdf
Manuscript File	102151_Auto_Edited_014319.docx
Answering Reviewers	102151-answering-reviewers.pdf
Audio Core Tip	102151-audio.m4a
Conflict-of-Interest Disclosure Form	102151-conflict-of-interest-statement.pdf
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Peer-review Report	102151-peer-reviews.pdf
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Scientific Editor Work List	102151-scientific-editor-work-list.pdf
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CrossCheck Report	102151-crosscheck-report.pdf