Sheth Deepa, Giger Maryellen L
Department of Radiology, University of Chicago, Chicago, Illinois, USA.
J Magn Reson Imaging. 2020 May;51(5):1310-1324. doi: 10.1002/jmri.26878. Epub 2019 Jul 25.
Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1310-1324.
成像技术和计算机技术的进步促使人工智能（AI）在乳腺成像的各种任务中的潜在应用不断增加，其应用范围已超越目前的计算机辅助检测，涵盖诊断、预后评估、治疗反应评估及风险评估。人工智能的自动化功能有望提升临床医生的诊断专业水平，包括精确划定肿瘤体积、提取癌症特征表型、将肿瘤表型特征转化为临床基因型意义以及进行风险预测。乳腺影像的特定表现与潜在的基因组、病理和临床特征相结合，在乳腺癌中的价值日益凸显。更新的成像技术的同时出现，为放射科医生提供了更多可用于分析和解读的诊断工具及图像数据集。在乳腺成像中整合基于人工智能的工作流程，能够将多个数据流整合到强大的多学科应用中，这可能引领个性化精准医疗的发展方向。在本文中，我们描述了人工智能在乳腺癌成像（尤其是磁共振成像）中的目标，并综述了与乳腺癌当前应用、潜力及局限性相关的文献。证据水平：3 技术效能：3 期 《磁共振成像杂志》2020 年；51:1310 - 1324。
