Department of Radiology, University Hospital Erlangen, Germany.
Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria.
Rofo. 2021 Aug;193(8):898-908. doi: 10.1055/a-1346-0095. Epub 2021 Feb 3.
Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information.
This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed.
Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine.
· In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review..
· Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
将放射学检查不仅仅视为图像,而是将其视为数据来源,已成为诊断成像领域的关键范例。这种视角的转变在乳腺成像中尤为流行。它使乳腺放射科医生能够应用源自计算机科学的算法,实现创新的临床应用,并完善已建立的方法。在这种情况下,“影像标志物”、“放射组学”和“人工智能”的术语至关重要。这些方法有望提供非侵入性、低成本(例如,与多基因阵列相比)且易于工作流程(自动化、仅一次检查、即时结果等)的临床相关信息。
本文旨在对上述范例进行叙述性综述。重点介绍乳腺成像中的关键概念,并解释重要的行话。讨论了乳腺成像的所有领域,包括示例研究和潜在的临床应用案例。
将放射学检查视为数据来源,可以通过在精准医学时代指导个体化乳腺癌诊断和肿瘤治疗,优化患者管理。
在传统的乳腺成像中,检查是基于视觉检查可感知的模式进行解释的。
放射组学范例将乳腺图像视为数据来源,其中包含超出我们肉眼可见的信息。
这导致了可以被视为影像标志物的放射组学特征,因为它们提供了诊断、预测和预后信息。
放射组学衍生的影像标志物可用于在精准医学时代个体化乳腺癌治疗。
本文将讨论乳腺成像领域的放射组学概念和关键研究。