Zeng Jing-Qi, Gao Yi-Wei, Jia Xiao-Bin
School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211198, Jiangsu Province, China.
World J Radiol. 2025 May 28;17(5):108011. doi: 10.4329/wjr.v17.i5.108011.
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.
低剂量放射疗法已成为一种很有前景的癌症治疗方式，因为它能够刺激抗肿瘤免疫反应，同时将对健康组织的损害降至最低。然而，患者之间免疫反应的显著异质性使其临床应用变得复杂，阻碍了结果预测和治疗个性化。人工智能（AI）通过整合免疫组学、放射组学和临床特征等多维数据，为解码复杂的免疫模式和预测个体治疗结果提供了一种变革性的解决方案。这篇社论探讨了人工智能在解决低剂量放射疗法中免疫反应异质性方面的潜力，并提出了一个由人工智能驱动的精准免疫治疗框架。尽管前景广阔，但要确保成功融入肿瘤学实践，必须克服包括数据标准化、模型可解释性和临床验证在内的挑战。
