Publication Name	World Journal of Gastroenterology
Manuscript ID	117409
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Editorial
Article Title	Artificial intelligence morphology and host complexity for precision prediction of nodal metastasis in colorectal cancer
Manuscript Source	Invited Manuscript
All Author List	Gang Wang and Sheng-Jie Pan
Funding Agency and Grant Number
Corresponding Author	Gang Wang, MD, PhD, Professor, Department of General Surgery, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou City Jiangsu Province, China, Suzhou 215006, Jiangsu Province, China. 286651551@qq.com
Key Words	Artificial intelligence; Deep learning; Computational pathology; Multiple instance learning; Colorectal cancer; Lymph node metastasis; Tumor microenvironment; Systemic inflammation; Physiologic complexity; Precision surgical oncology
Core Tip	This editorial highlights how a case-level multiple instance learning approach can reveal morphologic patterns predictive of lymph node metastasis in colorectal cancer. Yet morphology alone is insufficient. Integrating artificial intelligence–derived histology with systemic host factors-including inflammation, metabolic reserve, autonomic balance, and physiologic complexity-offers a more biologically coherent foundation for risk stratification and precision surgical decision-making.
Citation	Wang G, Pan SJ. Artificial intelligence morphology and host complexity for precision prediction of nodal metastasis in colorectal cancer. World J Gastroenterol 2026; In press

Received	2025-12-08 02:32
Peer-Review Started	2025-12-08 02:32
First Decision by Editorial Office Director	2026-01-08 09:29
Return for Revision	2026-01-08 09:29
Revised	2026-01-09 06:39
Publication Fee Transferred
Second Decision by Editor	2026-01-21 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-21 08:17
Articles in Press	2026-01-21 08:17
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (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/
Copyright	The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
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