Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions’ identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC.

乳腺癌 (BC) 是女性常见的癌症形式。其诊断和筛查通常通过不同的成像方式进行，例如乳房 X 线摄影、磁共振成像和超声。然而，乳房 X 线摄影和超声成像技术在识别病变和区分恶性与良性病变方面的敏感性和特异性有限，特别是在存在致密乳腺实质的情况下。由于磁共振图像的分辨率更高，MRI在所有可用工具中代表了在病变识别和诊断方面具有更高特异性和敏感性的方法。然而，特别是对于诊断，即使是 MRI 也有局限性，如果与乳房 X 线摄影相结合，只能部分解决。不幸的是，由于所有这些成像工具的局限性，为了得到一定的诊断，患者经常接受痛苦且昂贵的活检手术。在这种情况下，已经开发了几种计算方法来提高 BC 诊断和筛查的敏感性，同时保持相同的特异性。其中，放射组学在肿瘤学中越来越受欢迎，以改善癌症的诊断、预后和治疗。Radiomics 从单个或多个医学成像模式中获得多个定量特征，突出肉眼不可见的图像特征，从而显着增强医学成像的判别和预测潜力。这篇综述文章旨在总结基于放射组学的 BC 研究的最新进展。从文献中提取的主要证据表明，放射组学在将恶性乳腺病变与良性乳腺病变区分开来、对 BC 类型和等级进行分类以及预测治疗反应和复发风险方面具有很高的潜力。在个性化医疗时代，放射组学具有改善 BC 的诊断、预后、预测、监测、基于图像的干预和治疗反应评估的潜力。