Christodoulou Rafail C, Papageorgiou Platon S, Pitsillos Rafael, Woodward Amanda, Papageorgiou Sokratis G, Solomou Elena E, Georgiou Michalis F
Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA.
2nd Department of Orthopaedic Surgery and Traumatology, Aghia Sophia Pediatric General Hospital, Thivon 3 Street, 15772 Athens, Greece.
Int J Mol Sci. 2025 Jul 31;26(15):7396. doi: 10.3390/ijms26157396.
This narrative review explores the integration of theranostics and artificial intelligence (AI) in neuro-oncology, addressing the urgent need for improved diagnostic and treatment strategies for brain tumors, including gliomas, meningiomas, and pediatric central nervous system neoplasms. A comprehensive literature search was conducted through PubMed, Scopus, and Embase for articles published between January 2020 and May 2025, focusing on recent clinical and preclinical advancements in personalized neuro-oncology. The review synthesizes evidence on novel theranostic agents-such as Lu-177-based radiopharmaceuticals, CXCR4-targeted PET tracers, and multifunctional nanoparticles-and highlights the role of AI in enhancing tumor detection, segmentation, and treatment planning through advanced imaging analysis, radiogenomics, and predictive modeling. Key findings include the emergence of nanotheranostics for targeted drug delivery and real-time monitoring, the application of AI-driven algorithms for improved image interpretation and therapy guidance, and the identification of current limitations such as data standardization, regulatory challenges, and limited multicenter validation. The review concludes that the convergence of AI and theranostic technologies holds significant promise for advancing precision medicine in neuro-oncology, but emphasizes the need for collaborative, multidisciplinary research to overcome existing barriers and enable widespread clinical adoption.
本叙述性综述探讨了治疗诊断学与人工智能（AI）在神经肿瘤学中的整合，以满足对改善脑肿瘤（包括胶质瘤、脑膜瘤和小儿中枢神经系统肿瘤）诊断和治疗策略的迫切需求。通过PubMed、Scopus和Embase对2020年1月至2025年5月发表的文章进行了全面的文献检索，重点关注个性化神经肿瘤学的最新临床和临床前进展。该综述综合了关于新型治疗诊断剂（如基于镥-177的放射性药物、靶向CXCR4的PET示踪剂和多功能纳米颗粒）的证据，并强调了人工智能在通过先进的成像分析、放射基因组学和预测建模增强肿瘤检测、分割和治疗规划方面的作用。主要发现包括用于靶向药物递送和实时监测的纳米治疗诊断学的出现、应用人工智能驱动的算法改善图像解释和治疗指导，以及识别当前的局限性，如数据标准化、监管挑战和多中心验证有限。该综述得出结论，人工智能和治疗诊断技术的融合在推进神经肿瘤学精准医学方面具有重大前景，但强调需要开展协作性多学科研究，以克服现有障碍并实现广泛的临床应用。
