关键词: 乳腺癌, 新辅助治疗, 磁共振成像, 影像组学, 深度学习

Abstract: Breast cancer is one of the most common primary malignant tumors in women．Currently,breast cancer has become one of the most effective solid tumors by using comprehensive treatment methods,including surgery,neoadjuvant therapy,adjuvant radiotherapy and chemotherapy．Among them,neoadjuvant therapy（NAT）,including neoadjuvant chemotherapy,targeted therapy and endocrine therapy,is a very important part of the current comprehensive treatment of breast cancer．It aims to reduce the tumor stage,preserve the breast,preserve the armpit,and observe the drug sensitivity．Its therapeutic effect is crucial to the choice of surgical methods and prognosis of patients．Although pathological evaluation is recognized as the gold standard in evaluating the response to NAT,its limitation is that it can only be performed after treatment by invasive means,and cannot accurately predict response before treatment．As a widely used breast imaging technology,magnetic resonance imaging（MRI）plays a key role in evaluating the response to NAT．However,traditional MRI evaluation methods are limited by the individual differences of interobserver and the low repeatability of evaluation results,which affects the accuracy of efficacy evaluation to a certain extent．In recent years,artificial intelligence technology,especially radiomics and deep learning,has made significant progress in the field of medical image analysis．These techniques can extract a large number of features that are difficult to be recognized by the naked eye from medical images,reveal the internal microstructure and biological behavior of the lesion,and fully reflect the heterogeneity of the tumor．This not only helps clinicians to distinguish benign and malignant tumors more accurately,but also makes a more accurate assessment of the prognosis of malignant tumors．This article reviews the application and progress of MRI-based artificial intelligence technology in evaluating the response to neoadjuvant therapy for breast cancer in the past five years,aiming to promote the application and development of artificial intelligence in NAT clinical practice,and provide a scientific basis for the optimization of NAT treatment strategy and the realization of personalized medicine for breast cancer．

Key words: breast cancer, neoadjuvant therapy, magnetic resonance imaging, radiomics, deep learning