Zeng JQ, Gao YW, Jia XB. Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy. World J Radiol 2025; 17(5): 108011 [DOI: 10.4329/wjr.v17.i5.108011]
Corresponding Author of This Article
Jing-Qi Zeng, Academic Fellow, Postdoc, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Jiangning District, Nanjing 211198, Jiangsu Province, China. zjingqi@163.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Editorial
Open-Access Policy of This Article
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/
World J Radiol. May 28, 2025; 17(5): 108011 Published online May 28, 2025. doi: 10.4329/wjr.v17.i5.108011
Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy
Jing-Qi Zeng, Yi-Wei Gao, Xiao-Bin Jia
Jing-Qi Zeng, Yi-Wei Gao, Xiao-Bin Jia, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211198, Jiangsu Province, China
Author contributions: Zeng JQ conceptualized the editorial, reviewed the literature, and drafted the manuscript; Gao YW contributed to the literature review, assisted in drafting sections related to emerging technologies, and provided editorial feedback; Jia XB provided critical insights, revised the content for scientific accuracy, and contributed to the discussion on artificial intelligence applications. All authors finalized the manuscript and approved the submitted version.
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: Jing-Qi Zeng, Academic Fellow, Postdoc, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Jiangning District, Nanjing 211198, Jiangsu Province, China. zjingqi@163.com
Received: April 3, 2025 Revised: April 12, 2025 Accepted: May 8, 2025 Published online: May 28, 2025 Processing time: 53 Days and 19.2 Hours
Abstract
Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its ability to stimulate antitumor immune responses while minimizing damage to healthy tissues. However, the significant heterogeneity in immune responses among patients complicates its clinical application, hindering outcome prediction and treatment personalization. Artificial intelligence (AI) offers a transformative solution by integrating multidimensional data such as immunomics, radiomics, and clinical features to decode complex immune patterns and predict individual therapeutic outcomes. This editorial explored the potential of AI to address immune response heterogeneity in low-dose radiation therapy and proposed an AI-driven framework for precision immunotherapy. While promising, challenges, including data standardization, model interpretability, and clinical validation, must be overcome to ensure successful integration into oncological practice.
Core Tip: Artificial intelligence (AI) is revolutionizing low-dose radiation 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 enables customized treatments for each patient’s unique biological signature.
