Publication Name	World Journal of Gastroenterology
Manuscript ID	112090
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning
Manuscript Source	Unsolicited Manuscript
All Author List	Ling-Feng Zou, Xuan-Bing Wang, Jing-Wen Li, Xin Ouyang, Yi-Ying Luo, Yan Luo and Cheng-Long Wang
Funding Agency and Grant Number	Funding Agency Grant Number Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau) 2023MSXM060
Corresponding Author	Cheng-Long Wang, MD, PhD, Department of Pathology, Chongqing Traditional Chinese Medicine Hospital, No. 6 Panxi 7 Branch Road, Jiangbei District, Chongqing 400021, China. qq171909771@gmail.com
Key Words	Colorectal cancer; Lymph node metastasis; Deep learning; Multiple instance learning; Histopathology
Core Tip	To better predict lymph node metastasis (LNM) in advanced colorectal cancer, this pilot study developed a case-level deep learning framework. By analysing the pathology slides of all patients and emulating a pathologist's workflow, the model achieved a high area under the curve of 0.899, outperforming traditional methods. Integrating the clinical data further increased the accuracy to 0.904. This interpretable approach is a promising tool for refining LNM risk assessments and guiding adjuvant therapy decisions.
Citation	Zou LF, Wang XB, Li JW, Ouyang X, Luo YY, Luo Y, Wang CL. Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning. World J Gastroenterol 2025; In press

Received	2025-07-17 02:34
Peer-Review Started	2025-07-17 02:34
To Make the First Decision
Return for Revision	2025-07-24 09:03
Revised	2025-07-30 03:33
Second Decision	2025-11-27 02:37
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-11-27 07:24
Articles in Press	2025-11-27 07:24
Publication Fee Transferred	2025-07-31 02:57
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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