Huang Junyan, Xiang Yizhen, Gan Shengqi, Wu Linrong, Yan Jiangyu, Ye Dong, Zhang Junjun
Department of Radiology, the Second People's Hospital of Lishui, Wenzhou Medical University, Lishui, Zhejiang, China.
Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany.
Discov Oncol. 2025 Aug 26;16(1):1625. doi: 10.1007/s12672-025-03307-3.
This narrative review provides a comprehensive and structured overview of recent advances in the application of artificial intelligence (AI) to medical imaging for tumor diagnosis and treatment. By synthesizing evidence from recent literature and clinical reports, we highlight the capabilities, limitations, and translational potential of AI techniques across key imaging modalities such as CT, MRI, and PET. Deep learning (DL) and radiomics have facilitated automated lesion detection, tumour segmentation, and prognostic assessments, improving early cancer detection across various malignancies, including breast, lung, and prostate cancers. AI-driven multi-modal imaging fusion integrates radiomics, genomics, and clinical data, refining precision oncology strategies. Additionally, AI-assisted radiotherapy planning and adaptive dose optimisation have enhanced therapeutic efficacy while minimising toxicity. However, challenges persist regarding data heterogeneity, model generalisability, regulatory constraints, and ethical concerns. The lack of standardised datasets and explainable AI (XAI) frameworks hinders clinical adoption. Future research should focus on improving AI interpretability, fostering multi-centre dataset interoperability, and integrating AI with molecular imaging and real-time clinical decision support. Addressing these challenges will ensure AI's seamless integration into clinical oncology, optimising cancer diagnosis, prognosis, and treatment outcomes.
本叙述性综述全面且系统地概述了人工智能（AI）在医学成像中用于肿瘤诊断和治疗的最新进展。通过综合近期文献和临床报告中的证据，我们强调了AI技术在CT、MRI和PET等关键成像模态中的能力、局限性及转化潜力。深度学习（DL）和放射组学推动了病变自动检测、肿瘤分割和预后评估，改善了包括乳腺癌、肺癌和前列腺癌在内的各种恶性肿瘤的早期癌症检测。AI驱动的多模态成像融合整合了放射组学、基因组学和临床数据，完善了精准肿瘤学策略。此外，AI辅助的放射治疗计划和自适应剂量优化提高了治疗效果，同时将毒性降至最低。然而，在数据异质性、模型通用性、监管限制和伦理问题方面仍存在挑战。缺乏标准化数据集和可解释人工智能（XAI）框架阻碍了临床应用。未来的研究应专注于提高AI的可解释性，促进多中心数据集的互操作性，并将AI与分子成像和实时临床决策支持相结合。应对这些挑战将确保AI无缝融入临床肿瘤学，优化癌症诊断、预后和治疗结果。
