摘要
新辅助化疗已成为乳腺癌标准治疗策略,而磁共振成像是评估乳腺癌对新辅助化疗反应的首选影像学方法。虽然磁共振成像能提供关于肿瘤位置、大小及微环境等详细信息,但肿瘤的多样性变化给乳腺癌新辅助化疗的精准评估带来挑战。基于机器学习和深度学习的人工智能方法展现出识别磁共振成像数据中复杂模式的能力。通过临床影像特征分析、影像组学分析和生境分析等方法,人工智能技术已显著提升乳腺癌新辅助化疗评估的性能和效率,有助于实现个性化治疗策略。本文介绍了乳腺癌新辅助化疗评估所用的磁共振成像数据及性能指标,总结了人工智能技术在此领域的应用进展,同时探讨了当前人工智能技术在实际应用中的挑战和未来可能的研究方向。
Neoadjuvant chemotherapy has become a standard treatment strategy for breast cancer,and magnetic resonance imaging(MRI)is the preferred imaging method for assessing the response of breast cancer to neoadjuvant chemotherapy.Although MRI can provide detailed information of tumor,including location,size,and microenvironment,the precise assessment of neoadjuvant chemotherapy of breast cancer suffers from the diverse changes in tumors present in MRI images.Artificial intelligence methods based on machine learning and deep learning have demonstrated the ability to recognize complex patterns in MRI data.Through clinical radiologic feature analysis,radiomics analysis,and habitat analysis,artificial intelligence technology has significantly enhanced the performance and efficiency of assessments for breast cancer neoadjuvant chemotherapy,aiding in the realization of personalized treatment strategies.This paper introduces the MRI data and performance indicators in assessing breast cancer neoadjuvant chemotherapy,summarizes the progress of artificial intelligence applications in this field,and discusses the current challenges and potential future research directions for artificial intelligence technology in practical applications.
作者
刘凯文
金莹莹
王守巨
LIU Kaiwen;JIN Yingying;WANG Shouju(Department of Radiology,The First Affiliated Hospital of Nanjing Medical University,Nanjing 210000,China)
出处
《数据采集与处理》
CSCD
北大核心
2024年第4期794-812,共19页
Journal of Data Acquisition and Processing
基金
中国博士后科学基金(2021TQ0158,2022M711678)。
关键词
乳腺癌
新辅助化疗
磁共振成像
人工智能
影像组学
生境分析
breast cancer
neoadjuvant chemotherapy
magnetic resonance imaging(MRI)
artificial intelligence
radiomics
habitat analysis
