Baltzer Pascal A T
Universitätsklinik für Radiologie und Nuklearmedizin, allgemeines Krankenhaus der Medizinischen Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich.
Radiologe. 2021 Feb;61(2):192-198. doi: 10.1007/s00117-020-00802-2. Epub 2021 Jan 28.
CLINICAL/METHODOLOGICAL ISSUE: Central to breast imaging is the coordination of clinical and multimodal imaging information with percutaneous image-guided biopsies and surgical procedures. A wide range of problems arise due to this complexity: missed cancers, overdiagnosis, false-positive findings, unnecessary further imaging, biopsies and surgeries.
Breast imaging comprises the following diagnostic tests: mammography, tomosynthesis, contrast-enhanced mammography, (multiparametric) ultrasound, magnetic resonance imaging, computed tomography, nuclear medicine derived imaging and hybrid methods.
Artificial intelligence (AI) promises to alleviate practically all these problems of breast imaging. AI has the potential to avoid missed cancers and false-positive findings. Furthermore, it could guide an efficient use of imaging methods and it may potentially be used to define biological phenotypes of breast cancer.
AI-based software is being developed for various applications. Most developed are systems that support mammography screening. Problems are monocentric approaches and the focus on short-term financial success.
AI promises to improve breast imaging by simplifying and speeding up the workflow, by reducing monotonous tasks and by pointing out problems. This is likely to set free physician capacities that could be invested in improved communication with patients and interdisciplinary colleagues.
The present article mainly addresses clinical needs in breast imaging, pointing out potential areas of use for artificial intelligence. Depending on the definition, a wide array of helpful software tools for breast imaging are already available. Global solutions, however, are still missing.
临床/方法学问题：乳腺成像的核心是将临床和多模态成像信息与经皮图像引导活检及外科手术进行协调。由于这种复杂性出现了一系列问题：癌症漏诊、过度诊断、假阳性结果、不必要的进一步成像、活检和手术。
乳腺成像包括以下诊断检查：乳腺X线摄影、断层合成、对比增强乳腺X线摄影、（多参数）超声、磁共振成像、计算机断层扫描、核医学衍生成像及混合方法。
人工智能有望切实缓解乳腺成像的所有这些问题。人工智能有潜力避免癌症漏诊和假阳性结果。此外，它可以指导成像方法的有效使用，并且可能用于定义乳腺癌的生物学表型。
基于人工智能的软件正在为各种应用而开发。开发最多的是支持乳腺X线摄影筛查的系统。问题在于单中心方法以及对短期财务成功的关注。
人工智能有望通过简化和加速工作流程、减少单调任务以及指出问题来改善乳腺成像。这可能会释放医生的能力，可将其投入到改善与患者及跨学科同事的沟通中。
本文主要探讨乳腺成像的临床需求，指出人工智能的潜在应用领域。根据定义，已经有一系列用于乳腺成像的有用软件工具。然而，仍缺少全局解决方案。
