Publication Name	World Journal of Clinical Oncology
Manuscript ID	110462
Country	China

Category	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Minireviews
Article Title	Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis
Manuscript Source	Invited Manuscript
All Author List	Shao-Chun Li, Xin Fan and Jian He
Funding Agency and Grant Number	Funding Agency Grant Number Clinical Trials from the Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University 2021-LCYJ-MS-11 Nanjing Drum Tower Hospital National Natural Science Foundation Youth Cultivation Project 2024-JCYJ-QP-15
Corresponding Author	Jian He, Associate Professor, Chief Physician, MD, PhD, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing 210008, Jiangsu Province, China. hjxueren@126.com, Nanjing 210008, Jiangsu Province, China. hjxueren@163.com
Key Words	Lymph node metastasis; Lymphoma; Deep learning; Convolutional neural network; Medical imaging analysis; Automatic segmentation; Radiomics
Core Tip	This paper reviews the progress of 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography lymph node disease diagnosis technology driven by artificial intelligence. The automatic segmentation technology based on deep learning has significantly improved the diagnostic efficiency and consistency in lymph node detection, precise segmentation and three-dimensional reconstruction, and made up for the shortcomings of poor efficiency and obvious subjectivity in traditional artificial segmentation. The deep learning model has performed well in predicting treatment responses, distinguishing benign and malignant lesions, and diagnosis of lymph node metastasis in various cancer types, providing technical support for the accurate diagnosis of lymph node diseases, individualized treatment and prognostic evaluation.
Citation	Li SC, Fan X, He J. Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis. World J Clin Oncol 2025; In press

Received	2025-06-09 06:03
Peer-Review Started	2025-06-09 06:03
To Make the First Decision
Return for Revision	2025-06-12 07:44
Revised	2025-06-24 07:15
Second Decision	2025-10-11 02:43
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-10-11 12:12
Articles in Press	2025-10-11 12:12
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2218-4333 (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) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com