MRI预测乳腺癌淋巴结状态的研究进展及展望

doi:10.19401/j.cnki.1007-3639.2025.08.009

摘要/Abstract

摘要：

乳腺癌是全球范围内女性常见的癌症类型及死亡原因，淋巴结状态是乳腺癌分期的重要信息，与患者预后密切相关。磁共振成像（magnetic resonance imaging，MRI）技术对淋巴结以及新辅助治疗应答效果的评估具有优势，可补充其他影像学检查的不足。标准化评分系统如淋巴结数据和报告系统（Node Reporting and Data System，Node-RADS）通过整合淋巴结大小、边缘、强化模式等特征，可有效地减少评估的主观差异。影像组学通过高通量提取定量特征，将医学图像转换为可挖掘的数据并对其进行分析，进一步整合MRI影像组学、临床病理学特征及分子亚型信息构建多组学模型，可有效地预测腋窝淋巴结转移，为个性化治疗提供生物学依据。人工智能能够通过广泛搜索模型和参数空间来生成预测模型，人工智能驱动的MRI影像分析可有效地预测淋巴结转移及治疗反应。在新辅助化疗评估中，基于深度学习的全自动集成系统（fully automated-integrated system based on deep learning，FAIS-DL）结合多区域动态对比增强-MRI（dynamic contrast enhanced-MRI，DCE-MRI）和临床数据可高效能地预测腋窝病理学完全缓解，将不必要的腋窝淋巴结清扫术率从47.9%降至6.8%。本文就不同发展阶段采用MRI预测乳腺癌淋巴结状态的研究进展作一综述，以期提高临床医师和影像科医师对MRI在乳腺癌淋巴结状态评估及新辅助治疗效果评价中应用的认知，并为精准预测乳腺癌淋巴结状态模型的构建提供帮助。

关键词: 乳腺癌, 新辅助治疗, 淋巴结, 磁共振成像, 影像组学, 人工智能

Abstract:

Breast cancer stands as the most prevalent malignancy and the primary cause of cancer-related mortality among women globally. The lymph node status is not only pivotal for accurate clinical staging of breast cancer but also significantly associated with patients’ prognosis. Magnetic resonance imaging (MRI) has advantages in evaluating lymph nodes status and the response effect of neoadjuvant therapy, serving as a valuable complement to other imaging modalities. Standardized scoring systems, such as Node Reporting and Data System (Node-RADS), integrate key features including lymph node size, margin characteristics, and enhancement patterns, effectively minimizing interobserver variability in evaluation. MRI radiomics, by extracting quantitative features at high throughput, converts medical images into mineable and analyzable data. Further integrating MRI radiomics, clinicopathological features and molecular subtype information to construct multi-omics models, can effectively predict axillary lymph node metastasis, thereby providing a biological basis for personalized treatment. Artificial intelligence (AI) leverages extensive search algorithms and parameter spaces to generate predictive models. AI-driven MRI analysis has proven effective in predicting lymph node metastasis and treatment responses. In the evaluation of neoadjuvant chemotherapy, the fully automated-integrated system based on deep learning (FAIS-DL) system, which combines multi-region dynamic contrast enhanced-MRI (DCE-MRI) and clinical data, can efficiently predict axillary pathological complete response. This innovation has substantially reduced the rate of unnecessary axillary lymph node dissection (ALND) from 47.9% to 6.8%. This article reviewed the prediction of lymph node status in breast cancer by MRI at different developmental stages, with the aim of enhancing the understanding of clinicians and radiologists regarding the application of MRI in the assessment of lymph node status in breast cancer and evaluating the efficacy of neoadjuvant therapy, and providing assistance for the construction of a model for accurately predicting lymph node status in breast cancer.

Key words: Breast cancer, Neoadjuvant therapy, Lymph nodes, Magnetic resonance imaging, Radiomics, Artificial intelligence

中图分类号:

R737.9

翟梓涵, 陈盛. MRI预测乳腺癌淋巴结状态的研究进展及展望[J]. 中国癌症杂志, 2025, 35(8): 799-807.

ZHAI Zihan, CHEN Sheng. Research progress and prospects of MRI in predicting lymph node status in breast cancer[J]. China Oncology, 2025, 35(8): 799-807.

图/表 1

表1

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Category		Research advances	References
Conventional MRI	DCE	Established the standardized scoring system Node-RADS, providing a quantitative basis for biopsy decision-making	[9-10]
	MRL	Sensitive to the morphology of lymphatic vessels and lymph nodes. Suitable for internal mammary lymph node examination	[14-16]
	MRS	When the total choline level is <2.4 mmol/L, no metastatic lymph nodes are detected. When it is >15 mmol/L, the probability of lymph node metastasis is 2.69 times that in the situation of total choline level <15 mmol/L	[18-19]
	DWI	In clinical scenarios of contrast agent allergy or renal insufficiency, morphological assessment of axillary lymph nodes on DWI can distinguish benign from malignant, and clearly visualize internal mammary lymph nodes	[22-26]
MRI radiomics	Combined with other features	Radiomics models integrating clinical, pathological, intra-tumoral, and peritumoral (3 or 4 mm) features demonstrate favorable predictive performance	[54-55]
	Based on machine learning	The XGBoost algorithm exhibits relatively stable performance. Multi-spatiotemporal model assisting surgical strategies can effectively reduce the false-negative rate of sentinel lymph nodes	[58-60,63]
	Based on deep learning	The CNN model demonstrates good predictive performance. The fully automatic integrated system FAIS-DL, which is based on deep learning, can efficiently predict pathological complete response of axillary lymph nodes, thereby helping to reduce unnecessary axillary lymph node dissection	[65-70,75 -76]