Share：
Share this content in WeChat
X
[Chinese][PDF]2481
• Review •
Research progress of preoperative magnetic resonance imaging techniques in axillary lymph node metastasis of breast cancer
WANG Ao  ZHAO Siqi  ZHANG Moyun  ZHANG Lina 

Cite this article as: WANG A, ZHAO S Q, ZHANG M Y, et al. Research progress of preoperative magnetic resonance imaging techniques in axillary lymph node metastasis of breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(9): 183-188. DOI:10.12015/issn.1674-8034.2024.09.032.


[Abstract] Breast cancer has become the world's leading female cancer mortality rate. The clinical treatment methods for breast cancer patients are mainly surgical treatment and targeted treatment. The preoperative axillary lymph node metastasis of breast cancer is an important factor affecting the treatment and prognosis of breast cancer patients. With the rapid development of magnetic resonance imaging technology, researchers not only evaluate axillary lymph node metastasis based on conventional imaging features such as size, edge morphology, and cortical thickness, but also use functional imaging methods to understand the microstructure information of lymph nodes and quantitatively evaluate lymph node heterogeneity. In addition, emerging imaging omics, imaging combined with artificial intelligence can obtain more parameters and have achieved more in-depth research results in the study of axillary lymph nodes. This article reviews the research progress of preoperative magnetic resonance imaging in axillary lymph node metastasis in breast cancer, aiming to summarize the advantages and disadvantages of each imaging sequence in the application of axillary lymph node metastasis in breast cancer, and provide a new direction for subsequent imaging science research.
[Keywords] breast cancer；axillary lymph nodes；magnetic resonance imaging；functional magnetic resonance imaging；dynamic contrast enhanced magnetic resonance imaging；radiomics；neoadjuvant chemotherapy

WANG Ao1, 2   ZHAO Siqi1   ZHANG Moyun1   ZHANG Lina1*  

1 Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2 Department of CT, Anshan Central Hospital, Anshan 114000, China

Corresponding author: ZHANG L N, E-mail: zln201045@163.com

Conflicts of interest   None.

Received  2024-05-04
Accepted  2024-08-12
DOI: 10.12015/issn.1674-8034.2024.09.032
Cite this article as: WANG A, ZHAO S Q, ZHANG M Y, et al. Research progress of preoperative magnetic resonance imaging techniques in axillary lymph node metastasis of breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(9): 183-188. DOI:10.12015/issn.1674-8034.2024.09.032.

[1]
HUANG J J, CHAN P S, LOK V, et al. Global incidence and mortality of breast cancer: a trend analysis[J]. Aging, 2021, 13(4): 5748-5803. DOI: 10.18632/aging.202502.
[2]
XIONG J, DUAN Z C, ZUO H Q, et al. Comparison of epidemiological characteristics and prevention of breast cancer in China and the United States[J]. Chinese Journal of Bases and Clinics in General Surgery, 2024, 31(1): 50-55. DOI: 10.7507/1007-9424.202311024.
[3]
WANG J, WU S G. Breast cancer: an overview of current therapeutic strategies, challenge, and perspectives[J/OL]. Breast Cancer (Dove Med Press), 2023, 15: 721-730 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/37881514/. DOI: 10.2147/BCTT.S432526.
[4]
LI L, ZHAO J, ZHANG Y, et al. Nomogram based on multiparametric analysis of early-stage breast cancer: prediction of high burden metastatic axillary lymph nodes[J]. Thorac Cancer, 2023, 14(35): 3465-3474. DOI: 10.1111/1759-7714.15139.
[5]
LIU Y, LI X, ZHU L N, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer based on intratumoral and peritumoral DCE-MRI radiomics nomogram[J/OL]. Contrast Media Mol Imaging, 2022, 2022: 6729473 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/36051932/. DOI: 10.1155/2022/6729473.
[6]
BODE, SCHRADING S, MASOUMI A, et al. Abbreviated MRI for comprehensive regional lymph node staging during pre-operative breast MRI[J/OL]. Cancers, 2023, 15(6): 1859 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/36980744/. DOI: 10.3390/cancers15061859.
[7]
CHEN H, LI W, WAN C, et al. Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers[J/OL]. Front Oncol, 2022, 12: 942943 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/35992872/. DOI: 10.3389/fonc.2022.942943.
[8]
LIU Y Y, LUO H B, WANG C H, et al. Diagnostic performance of T2-weighted imaging and intravoxel incoherent motion diffusion-weighted MRI for predicting metastatic axillary lymph nodes in T1 and T2 stage breast cancer[J]. Acta Radiol, 2022, 63(4): 447-457. DOI: 10.1177/02841851211002834.
[9]
SUN K, ZHU H, CHAI W M, et al. Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE[J]. Eur Radiol, 2020, 30(1): 57-65. DOI: 10.1007/s00330-019-06365-8.
[10]
SONG D L, YANG F, ZHANG Y J, et al. Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer[J/OL]. Cancer Imaging, 2022, 22(1): 17 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/35379339/. DOI: 10.1186/s40644-022-00450-w.
[11]
KORDE L A, SOMERFIELD M R, CAREY L A, et al. Neoadjuvant chemotherapy, endocrine therapy, and targeted therapy for breast cancer: ASCO guideline[J]. J Clin Oncol, 2021, 39(13): 1485-1505. DOI: 10.1200/JCO.20.03399.
[12]
PILEWSKIE M, MORROW M. Axillary nodal management following neoadjuvant chemotherapy: a review[J]. JAMA Oncol, 2017, 3(4): 549-555. DOI: 10.1001/jamaoncol.2016.4163.
[13]
KIM R, CHANG J M, LEE H B, et al. Predicting axillary response to neoadjuvant chemotherapy: breast MRI and US in patients with node-positive breast cancer[J]. Radiology, 2019, 293(1): 49-57. DOI: 10.1148/radiol.2019190014.
[14]
CIROCCHI R, AMABILE M I, LUCA A D, et al. New classifications of axillary lymph nodes and their anatomical-clinical correlations in breast surgery[J/OL]. World J Surg Oncol, 2021, 19(1): 93 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/33781279/. DOI: 10.1186/s12957-021-02209-2.
[15]
YU H T, LI Q, WU S S, et al. Research progress of radiomics based on MRI for prediction of axillary lymph node metastasis in breast cancer[J]. Chin J Magn Reson Imag, 2023, 14(7): 176-180. DOI: 10.12015/issn.1674-8034.2023.07.032.
[16]
BARAN M T, GUNDOGDU H, DEMIRAL G, et al. PET-CT and MR imaging in the management of axillary nodes in early stage breast cancer[J]. J Coll Physicians Surg Pak, 2020, 30(9): 946-950. DOI: 10.29271/jcpsp.2020.09.946.
[17]
CHE BAKRI N A, KWASNICKI R M, KHAN N, et al. Impact of axillary lymph node dissection and sentinel lymph node biopsy on upper limb morbidity in breast cancer patients: a systematic review and meta-analysis[J]. Ann Surg, 2023, 277(4): 572-580. DOI: 10.1097/SLA.0000000000005671.
[18]
SZEP M, PINTICAN R, BOCA B, et al. Multiparametric MRI features of breast cancer molecular subtypes[J/OL]. Medicina, 2022, 58(12): 1716 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/36556918/. DOI: 10.3390/medicina58121716.
[19]
CHANG J M, LEUNG J W T, MOY L, et al. Axillary nodal evaluation in breast cancer: state of the art[J]. Radiology, 2020, 295(3): 500-515. DOI: 10.1148/radiol.2020192534.
[20]
MARINO M A, AVENDANO D, ZAPATA P, et al. Lymph node imaging in patients with primary breast cancer: concurrent diagnostic tools[J/OL]. Oncologist, 2020, 25(2): e231-e242 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/32043792/. DOI: 10.1634/theoncologist.2019-0427.
[21]
HANKÓ-BAUER O, PODOLEANU C, GEORGESCU R, et al. The accuracy of the preoperative axillary ultrasound examination in predicting the status of the sentinel lymph node involvement in patients with infiltrating breast carcinoma[J]. Chirurgia, 2019, 114(3): 384-391. DOI: 10.21614/chirurgia.114.3.384.
[22]
MORAWITZ J, BRUCKMANN N M, DIETZEL F, et al. Comparison of nodal staging between CT, MRI, and [18F]-FDG PET/MRI in patients with newly diagnosed breast cancer[J]. Eur J Nucl Med Mol Imaging, 2022, 49(3): 992-1001. DOI: 10.1007/s00259-021-05502-0.
[23]
HA S M, CHAE E Y, CHA J H, et al. Diagnostic performance of standard breast MR imaging compared to dedicated axillary MR imaging in the evaluation of axillary lymph node[J/OL]. BMC Med Imaging, 2020, 20(1): 45 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/32357942/. DOI: 10.1186/s12880-020-00449-4.
[24]
ARSLAN G, ALTINTOPRAK K M, YIRGIN I K, et al. Diagnostic accuracy of metastatic axillary lymph nodes in breast MRI[J/OL]. Springerplus, 2016, 5(1): 735 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/27376003/. DOI: 10.1186/s40064-016-2419-7.
[25]
ABDEL KHALEK ABDEL RAZEK A, ABDEL LATTIF M, DENEWER A, et al. Assessment of axillary lymph nodes in patients with breast cancer with diffusion-weighted MR imaging in combination with routine and dynamic contrast MR imaging[J]. Breast Cancer, 2016, 23(3): 525-532. DOI: 10.1007/s12282-015-0598-7.
[26]
CUI Q, SUN L, ZHANG Y, et al. Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis[J/OL]. Ann Transl Med, 2021, 9(22): 1677 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/34988186/. DOI: 10.21037/atm-21-5441.
[27]
CLAUSER P, KRUG B, BICKEL H, et al. Diffusion-weighted imaging allows for downgrading MR BI-RADS 4 lesions in contrast-enhanced MRI of the breast to avoid unnecessary biopsy[J]. Clin Cancer Res, 2021, 27(7): 1941-1948. DOI: 10.1158/1078-0432.CCR-20-3037.
[28]
JIN Y N, ZHANG Y, CHENG J L, et al. The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions[J/OL]. BMC Med Inform Decis Mak, 2020, 20(1): 239 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/32957985/. DOI: 10.1186/s12911-020-01257-0.
[29]
KIM S H, SHIN H J, SHIN K C, et al. Diagnostic performance of fused diffusion-weighted imaging using T1-weighted imaging for axillary nodal staging in patients with early breast cancer[J]. Clin Breast Cancer, 2017, 17(2): 154-163. DOI: 10.1016/j.clbc.2016.10.010.
[30]
CHO P, PARK C S, PARK G E, et al. Diagnostic usefulness of diffusion-weighted MRI for axillary lymph node evaluation in patients with breast cancer[J/OL]. Diagnostics, 2023, 13(3): 513 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/36766617/. DOI: 10.3390/diagnostics13030513.
[31]
CATALDO C D, BRUNO F, PALUMBO P, et al. Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review[J]. Gland Surg, 2020, 9(6): 2225-2234. DOI: 10.21037/gs-20-546.
[32]
LIMA M, HONDA M, SIGMUND E E, et al. Diffusion MRI of the breast: current status and future directions[J]. J Magn Reson Imaging, 2020, 52(1): 70-90. DOI: 10.1002/jmri.26908.
[33]
KURT N, BINBOGA KURT B, GULSARAN U, et al. Diffusion tensor imaging and diffusion-weighted imaging on axillary lymph node status in breast cancer patients[J]. Diagn Interv Radiol, 2022, 28(4): 329-336. DOI: 10.5152/dir.2022.21460.
[34]
SODANO C, CLAUSER P, DIETZEL M, et al. Clinical relevance of total choline (tCho) quantification in suspicious lesions on multiparametric breast MRI[J]. Eur Radiol, 2020, 30(6): 3371-3382. DOI: 10.1007/s00330-020-06678-z.
[35]
WANG Z, WU Q, NING N, et al. Study on the value of mDixon-Quant technique in the diagnosis and prognosis evaluation of invasive ductal breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 65-71. DOI: 10.12015/issn.1674-8034.2023.03.012.
[36]
BUUS T W, SIVESGAARD K, FRIS T L, et al. Fat fractions from high-resolution 3D radial Dixon MRI for predicting metastatic axillary lymph nodes in breast cancer patients[J/OL]. Eur J Radiol Open, 2020, 7:100284 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/33204769/. DOI: 10.1016/j.ejro.2020.100284.
[37]
LIANG H B, NIN N, ZHAO S Q, et al. 3D-MIP reconstruction and multi parameter evaluation of BI-RADS 4 breast tumors based on DCE-MRI[J]. Chin J Magn Reson Imaging, 2024, 15(5): 94-101. DOI: 10.12015/issn.1674-8034.2024.05.016.
[38]
The Society of Breast Cancer China Anti-Cancer Association, Breast Oncology Group of the Oncology Branch of the Chinese Medical Association. Guidelines for breast cancer diagnosis and treatment by China Anti-cancer Association(2024 edition)[J]. China Oncol, 2023, 33(12): 1092-1186. DOI: 10.19401/j.cnki.1007-3639.2023.12.004.
[39]
YUN S J, SOHN Y M, SEO M. Differentiation of benign and metastatic axillary lymph nodes in breast cancer: additive value of MRI computer-aided evaluation[J/OL]. Clin Radiol, 2016, 71(4): 403.e1-403.e7 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/26875621/. DOI: 10.1016/j.crad.2016.01.008.
[40]
RUAN M, WANG D B, CHEN W H, et al. MRI in differential diagnosis of metastatic axillary lymph nodes in breast cancer[J]. Chin J Interv Imag Ther, 2017, 14(8): 484-488. DOI: 10.13929/j.1672-8475.201703041.
[41]
GAO Y, FENG W, LU X R, et al. Difference of DCE-MRI parameters at different time points and their predictive value for axillary lymph node metastasis of breast cancer[J/OL]. Acad Radiol, 2022, 29: S79-S86 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/33504446/. DOI: 10.1016/j.acra.2021.01.013.
[42]
KATAOKA M, HONDA M, OHASHI A, et al. Ultrafast dynamic contrast-enhanced MRI of the breast: how is it used?[J]. Magn Reson Med Sci, 2022, 21(1): 83-94. DOI: 10.2463/mrms.rev.2021-0157.
[43]
KIKUCHI Y, MORI M, FUJIOKA T, et al. Feasibility of ultrafast dynamic magnetic resonance imaging for the diagnosis of axillary lymph node metastasis: a case report[J/OL]. Eur J Radiol Open, 2020, 7: 100261 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/32944596/. DOI: 10.1016/j.ejro.2020.100261.
[44]
YAMAGUCHI K, NAKAZONO T, EGASHIRA R, et al. Maximum slope of ultrafast dynamic contrast-enhanced MRI of the breast: comparisons with prognostic factors of breast cancer[J]. Jpn J Radiol, 2021, 39(3): 246-253. DOI: 10.1007/s11604-020-01049-6.
[45]
CONTI A, DUGGENTO A, INDOVINA I, et al. Radiomics in breast cancer classification and prediction[J/OL]. Semin Cancer Biol, 2021, 72: 238-250 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/32371013/. DOI: 10.1016/j.semcancer.2020.04.002.
[46]
CHAI R M, MA H, XU M J, et al. Differentiating axillary lymph node metastasis in invasive breast cancer patients: a comparison of radiomic signatures from multiparametric breast MR sequences[J]. J Magn Reson Imaging, 2019, 50(4): 1125-1132. DOI: 10.1002/jmri.26701.
[47]
SHIMIZU H, MORI N, MUGIKURA S, et al. Application of texture and volume model analysis to dedicated axillary high-resolution 3D T2-weighted MR imaging: a novel method for diagnosing lymph node metastasis in patients with clinically node-negative breast cancer[J]. Magn Reson Med Sci, 2024, 23(2): 161-170. DOI: 10.2463/mrms.mp.2022-0091.
[48]
SONG S E, WOO O H, CHO Y, et al. Prediction of axillary lymph node metastasis in early-stage triple-negative breast cancer using multiparametric and radiomic features of breast MRI[J/OL]. Acad Radiol, 2023, 30(Suppl 2): S25-S37 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/37331865/. DOI: 10.1016/j.acra.2023.05.025.
[49]
WANG Y X, SHANG Y Y, GUO Y X, et al. Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer[J/OL]. Front Oncol, 2024, 14: 1357145 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/38567148/. DOI: 10.3389/fonc.2024.1357145.
[50]
CHEN M Z, KONG C L, LIN G H, et al. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study[J/OL]. EClinicalMedicine, 2023, 63: 102176 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/37662514/. DOI: 10.1016/j.eclinm.2023.102176.
[51]
AN J S, PENG C, TANG H L, et al. New advances in the research of resistance to neoadjuvant chemotherapy in breast cancer[J/OL]. Int J Mol Sci, 2021, 22(17): 9644 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/34502549/. DOI: 10.3390/ijms22179644.
[52]
MANN R M, CHO N, MOY L. Breast MRI: state of the art[J]. Radiology, 2019, 292(3): 520-536. DOI: 10.1148/radiol.2019182947.
[53]
LIU Y, CHEN Z H, CHEN J H, et al. Pathologic complete response prediction in breast cancer lesion segmentation and neoadjuvant therapy[J/OL]. Front Med, 2023, 10: 1188207 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/38143443/. DOI: 10.3389/fmed.2023.1188207.
[54]
GAN L Y, MA M M, LIU Y H, et al. A clinical-radiomics model for predicting axillary pathologic complete response in breast cancer with axillary lymph node metastases[J/OL]. Front Oncol, 2021, 11: 786346 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/34993145/. DOI: 10.3389/fonc.2021.786346.
[55]
LIU S S, DU S Y, GAO S, et al. A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients[J/OL]. BMC Cancer, 2023, 23(1): 15 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/36604679/. DOI: 10.1186/s12885-022-10496-5.
[56]
YU Y F, HE Z F, OUYANG J, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study[J/OL]. EBioMedicine, 2021, 69: 103460 [2024-05-03]. https://pubmed.ncbi.nlm.nih.gov/34233259/. DOI: 10.1016/j.ebiom.2021.103460.

PREV Progress in diagnosis and prognosis of noncompaction cardiomyopathy with multimodal cardiac magnetic resonance
NEXT Application of radiomics based on different magnetic resonance imaging techniques in the differential diagnosis of breast lesions
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn