The Journal of Practical Medicine ›› 2023, Vol. 39 ›› Issue (22): 2861-2865.doi: 10.3969/j.issn.1006-5725.2023.22.001

• Symposiums:Breast tumors •     Next Articles

Advance in predicting lymph node metastasis of breast cancer by multimodal MRI

Xiran SHI,Heng WANG,Libing HE,Zhiqiang QIU,Hongjian LI,Xiaoxue. XU()   

  1. Department of Radiology,the Affiliated Hospital of North Sichuan Medical College,Nanchong 637000,China
  • Received:2023-07-17 Online:2023-11-25 Published:2023-12-11
  • Contact: Xiaoxue. XU E-mail:nclittlesnownc@163.com

Abstract:

Breast cancer is one of the most common tumor-related causes of death in women, accounting for the first place in the incidence of female malignant tumors, with its incidence increasing yearly and the age of onset gradually becoming younger. Lymph node(LN)metastasis is the most important predictor to evaluate the recurrence and survival rate of patients with breast cancer. Accurate assessment of axillary lymph node involvement is an important part for breast cancer staging. Sentinel lymph node biopsy(SLNB)and axillary lymph node dissection(ALND)are both invasive procedures with high risk of complications and false negative rate. Therefore, using non-invasive imaging methods to predict axillary lymph node metastasis have become a research hotspot in recent years. Multimodal magnetic resonance has the advantages of no radiation, high soft tissue resolution, multiple parameters, and multiple sequences compared with other imaging methods in predicting lymph node metastasis of breast cancer. It is widely used in the differentiation of benign and malignant breast lesions, evaluation of prognosis of breast cancer patients, evaluation of lymph node metastasis status, and evaluation of the efficacy of neoadjuvant chemotherapy. This article reviews the research progress of multimodal MRI in predicting axillary lymph node metastasis of breast cancer with various index models and kurtosis diffusion imaging.

Key words: breast cancer, lymph node metastasis, multimodal MRI, diffusion weighted imaging, Diffusion kurtosis imaging

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