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; 16(11): 110462 [DOI: 10.5306/wjco.v16.i11.110462]
Corresponding Author of This Article
Jian He, MD, PhD, Associate Professor, Chief Physician, 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@163.com
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Radiology, Nuclear Medicine & Medical Imaging
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Minireviews
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Nov 24, 2025 (publication date) through Nov 21, 2025
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World Journal of Clinical Oncology
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2218-4333
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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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; 16(11): 110462 [DOI: 10.5306/wjco.v16.i11.110462]
World J Clin Oncol. Nov 24, 2025; 16(11): 110462 Published online Nov 24, 2025. doi: 10.5306/wjco.v16.i11.110462
Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis
Shao-Chun Li, Xin Fan, Jian He
Shao-Chun Li, Xin Fan, Jian He, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, Jiangsu Province, China
Co-corresponding authors: Xin Fan and Jian He.
Author contributions: Li SC and Fan X reviewed the literature and drafted the manuscript; Li SC and He J conceived the idea for the manuscript; Fan X provided comprehensive perspectives; He J revised and finalized the manuscript; Fan X and He J have played important and indispensable roles in the manuscript preparation as the co-corresponding authors. All authors have read and approved the final version of the manuscript.
Supported by Clinical Trials from the Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 2021-LCYJ-MS-11; and Nanjing Drum Tower Hospital National Natural Science Foundation Youth Cultivation Project, No. 2024-JCYJ-QP-15.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Jian He, MD, PhD, Associate Professor, Chief Physician, 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@163.com
Received: June 9, 2025 Revised: June 24, 2025 Accepted: October 11, 2025 Published online: November 24, 2025 Processing time: 167 Days and 21.2 Hours
Core Tip
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 diagnosing lymph node metastasis in various cancer types, providing technical support for the accurate diagnosis of lymph node diseases, individualized treatment and prognostic evaluation.
