基于MRI的人工智能技术在乳腺癌新辅助治疗疗效评估中的应用与进展

来源期刊：广州医药 | 1381-1388 发布时间：2025-01-08 收稿时间：2025/11/13 18:41:40 阅读量：80

作者：: 潘立文,

唐文洁,

郭媛,

关键词：: 乳腺癌新辅助治疗磁共振成像影像组学深度学习; breast cancer neoadjuvant therapy magnetic resonance imaging radiomics deep learning

DOI：: 10.20223/j.cnki.1000-8535.2024.12.001

收稿时间：: 2024-07-10
修订日期：
接收日期：
引用总数：: 1

乳腺癌是女性最常见的原发恶性肿瘤之一。目前,通过采用综合治疗手段,包括手术、新辅助治疗、辅助放化疗等多种手段,乳腺癌已成为疗效最佳的实体肿瘤之一。其中,新辅助治疗（NAT）包括新辅助化疗、靶向治疗和内分泌治疗,目的是使肿瘤降期、保乳、保腋窝,并可观察药物敏感性,是当前乳腺癌综合治疗中非常重要的组成部分,其治疗疗效对患者手术方式的选择和预后至关重要。尽管病理学评价在评估NAT疗效方面被公认为金标准,但其局限性在于只能通过有创手段在治疗后进行,无法在治疗前对患者做出准确预测。磁共振成像（MRI）作为一项广泛使用的乳腺成像技术,在评估NAT疗效中扮演着关键角色。近年来,人工智能技术,尤其是影像组学（Radiomics）和深度学习（Deep Learning）,在医学影像分析领域取得了显著进展。这些技术能够从医学图像中提取大量肉眼难以识别的特征,揭示病变内部的微观结构和生物学行为,全面反映肿瘤的异质性,这不仅有助于临床医生更准确地区分良、恶性肿瘤,还能对恶性肿瘤的预后进行更为精确的评估。本文系统综述了近年来基于MRI的人工智能技术在乳腺癌新辅助治疗疗效评估中的应用研究,旨在促进人工智能在NAT临床实践中的应用和发展,为乳腺癌NAT治疗策略的优化和个性化医疗的实现提供科学依据。

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．

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