Publication Name	World Journal of Radiology
Manuscript ID	108011
Country	China

Received	2025-04-03 03:24
Peer-Review Started	2025-04-03 03:24
To Make the First Decision
Return for Revision	2025-04-11 09:55
Revised	2025-04-12 02:56
Second Decision	2025-05-08 02:48
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-05-08 08:41
Articles in Press	2025-05-08 08:41
Publication Fee Transferred
Edit the Manuscript by Language Editor	2025-05-13 20:28
Typeset the Manuscript	2025-05-20 02:44
Publish the Manuscript Online	2025-05-26 07:26

ISSN	1949-8470 (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	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Editorial
Article Title	Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy
Manuscript Source	Invited Manuscript
All Author List	Jing-Qi Zeng, Yi-Wei Gao and Xiao-Bin Jia
Funding Agency and Grant Number
Corresponding Author	Jing-Qi Zeng, Academic Fellow, Postdoc, School of Traditional Chinese Pharmacy, China Pharmaceutical University, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211198, PR China. Electronic address: zjingqi@163.com, Nanjing 211198, Jiangsu Province, China. zjingqi@163.com
Key Words	Low-dose radiation; Immune response; Heterogeneity; Artificial intelligence; Precision medicine; Immunotherapy; Radiomics
Core Tip	Artificial intelligence (AI) is revolutionizing low-dose radiation (LDR) therapy by addressing immune response heterogeneity in patients with cancer. By integrating multidimensional datasets such as immunomics, radiomics, and clinical profiles, AI employs advanced machine learning to decode complex immune patterns and predict individualized therapeutic outcomes. This enables tailored treatment strategies that enhance antitumor efficacy while minimizing side effects. Thus, AI paves the way for precision oncology and by enabling customized treatments for each patient’s unique biological signature.
Publish Date	2025-05-26 07:26
Citation	<p>Zeng JQ, Gao YW, Jia XB. Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy. <i>World J Radiol</i> 2025; 17(5): 108011</p>
URL	https://www.wjgnet.com/1949-8470/full/v17/i5/108011.htm
DOI	https://dx.doi.org/10.4329/wjr.v17.i5.108011

Full Article (PDF)	WJR-17-108011-with-cover.pdf
Manuscript File	108011_Auto_Edited_065755.docx
Answering Reviewers	108011-answering-reviewers.pdf
Audio Core Tip	108011-audio.mp3
Conflict-of-Interest Disclosure Form	108011-conflict-of-interest-statement.pdf
Copyright License Agreement	108011-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	108011-non-native-speakers.pdf
Peer-review Report	108011-peer-reviews.pdf
Scientific Misconduct Check	108011-scientific-misconduct-check.png
Scientific Editor Work List	108011-scientific-editor-work-list.pdf
CrossCheck Report	108011-crosscheck-report.pdf