摘要
全球男性所患恶性肿瘤中,前列腺癌(prostate cancer,PCa)发病率排第二,其精准诊断与治疗决策方面亟需更精准的辅助工具。人工智能(artificial intelligence,AI)技术的兴起,为PCa的早期诊断和精准治疗带来了前所未有的机遇。本文系统综述了AI在PCa三个核心领域中的现状:(1)诊断与预后评估——回顾了当下传统PCa诊断手段的应用情形,并重点介绍AI在多模态成像技术的应用进展;(2)分子机制研究——探讨了AI在基因组学、蛋白组学等高通量组学数据中的应用模式,包括生物标志物筛选与药物疗效预测,揭示疾病发生发展的关键分子机制;(3)治疗决策优化——阐述了AI在手术规划与术中导航、靶向治疗方案个性化设计及术后动态监测中的创新实践,凸显了AI在提升疗效、减少并发症风险上的潜在价值。本文介绍了临床级的PCa专用AI系统,分析其在提高诊疗效率和精度方面的优势。针对AI在PCa应用中面临的数据来源单一、模型泛化能力不足、“黑箱”特性以及多模态数据标准化缺失等挑战,未来研究应聚焦于构建跨中心、多模态标准化数据库,并引入联邦学习等隐私计算技术应用;开发可解释性AI框架,以增强临床信任度;持续优化算法性能,提高模型的实用性与可靠性。本综述旨在总结AI在PCa领域的最新应用进展和挑战,为未来的研究方向提供指导,以助力AI技术与PCa临床研究与实践的深度融合。
Prostate cancer(PCa)has the second highest incidence of malignant tumors among men worldwide,and its precise diagnosis and treatment decision-making urgently needs more accurate auxiliary tools.The rise of artificial intelligence(AI)technology has brought unprecedented opportunities for early diagnosis and precision treatment of PCa.This paper provides a systematic review of the current state of AI in three core areas of prostate cancer:(1)Diagnosis and prognosis assessment,we review the current status of the application of traditional PCa diagnostic tools and focus on the progress of the application of AI in multimodal imaging technology;(2)Molecular mechanism research,we explore the application model of AI in genomics,proteomics and other high-throughput genomics data,revealing key molecular mechanisms of disease development;(3)Treatment decision optimization,we illustrate the innovative practice of AI in surgical planning and intraoperative navigation,personalized design of targeted treatment protocols,and postoperative dynamic monitoring,highlighting the potential value of AI in improving outcomes and reducing and minimizing the risk of complications.This paper describes clinical-grade PCa-specific AI systems and analyzes their advantages in improving the efficiency and accuracy of diagnosis and treatment.Aiming at the challenges faced by AI in PCa applications,such as single data source,insufficient model generalization ability,"black box"characteristics,and lack of multimodal data standardization,future research should focus on building cross-center,multimodal standardized databases and introducing privacy computing technology applications such as federated learning;developing interpretable AI frameworks to enhance clinical trust;and continuously optimizing algorithmic performance to improve the utility and reliability of models.The purpose of this review is to summarize the latest advances and challenges in the application of AI in the field of PCa,and to provide guidance for future research directions in order to promote the deep integration of AI technology with PCa clinical research and practice.
作者
叶梦梦
周陶胡
葛艳明
范丽
YE Mengmeng;ZHOU Taohu;GE Yanming;FAN Li(Shandong Second Medical University,Weifang 261000,China;Department of Radiology,Changzheng Hospital,Naval Medical University,Shanghai 200003,China;Medical Imaging Center,Affiliated Hospital of Shandong Second Medical University,Weifang 261000,China)
出处
《磁共振成像》
北大核心
2025年第7期192-201,共10页
Chinese Journal of Magnetic Resonance Imaging
基金
国家重点研发计划项目(编号:2022YFC2010002)
国家自然科学基金项目(编号:82171926,82430065)。
关键词
前列腺癌
人工智能
磁共振成像
影像分割
个性化治疗
prostate cancer
artificial intelligence
magnetic resonance imaging
image segmentation
personalized treatment
