Department of Radiological, Oncological and Pathological Sciences, "Sapienza" - University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy.
Department of Radiological, Oncological and Pathological Sciences, "Sapienza" - University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy.
Eur J Radiol. 2022 Jan;146:110051. doi: 10.1016/j.ejrad.2021.110051. Epub 2021 Nov 19.
Breast cancer is a heterogeneous disease nowadays, including different biological subtypes with a variety of possible treatments, which aim to achieve the best outcome in terms of response to therapy and overall survival. In recent years breast imaging has evolved considerably, and the ultimate goal is to predict these strong phenotypic differences noninvasively. Indeed, breast cancer multiparametric studies can highlight not only qualitative imaging parameters, as the presence/absence of a likely malignant finding, but also quantitative parameters, suggesting clinical-pathological features through the evaluation of imaging biomarkers. A further step has been the introduction of artificial intelligence and in particular radiogenomics, that investigates the relationship between breast cancer imaging characteristics and tumor molecular, genomic and proliferation features. In this review, we discuss the main techniques currently in use for breast imaging, their respective fields of use and their technological and diagnostic innovations.
如今，乳腺癌是一种异质性疾病，包括不同的生物学亚型和多种可能的治疗方法，这些方法旨在在治疗反应和总体生存方面取得最佳效果。近年来，乳腺成像技术有了很大的发展，其最终目标是无创地预测这些明显的表型差异。事实上，乳腺癌多参数研究不仅可以突出定性成像参数，如可能的恶性发现的存在/不存在，还可以突出定量参数，通过评估成像生物标志物提示临床病理特征。更进一步的是引入了人工智能，特别是放射组学，它研究了乳腺癌成像特征与肿瘤分子、基因组和增殖特征之间的关系。在这篇综述中，我们讨论了目前用于乳腺成像的主要技术、它们各自的应用领域以及它们的技术和诊断创新。
