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Research progress in predicting axillary lymph node metastasis of breast cancer by preoperative MRI
MA Qinqin  FENG Wen  CHEN Yuanyuan  WANG Sha  LEI Junqiang 

Cite this article as: Ma QQ, Feng W, Chen YY, et al. Research progress in predicting axillary lymph node metastasis of breast cancer by preoperative MRI[J]. Chin J Magn Reson Imaging, 2022, 13(9): 151-155. DOI:10.12015/issn.1674-8034.2022.09.036.


[Abstract] Axillary lymph node metastasis (ALNM) is one of the important factors affecting postoperative recurrence or distant metastasis of breast cancer, and has a profound impact on the choice of treatment options and long-term quality of life for patients. At present, many prediction studies for ALNM based on MRI methodology, radiomics, and genomics have been proposed, and their conclusions have clear scientific and clinical significance. This article reviews the research progress of preoperative multiparametric MRI, MRI-based radiomics and machine learning in predicting axillary lymph node (ALN) status in breast cancer.
[Keywords] breast cancer;axillary lymph node metastasis;magnetic resonance imaging;diffusion weight imaging;radiomics;predicting

MA Qinqin1   FENG Wen1   CHEN Yuanyuan1   WANG Sha1   LEI Junqiang2*  

1 The First Clinical Medical College, Lanzhou University, Lanzhou 730000, China

2 Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China

*Lei JQ, E-mail: leijq2011@126.com

Conflicts of interest   None.

Received  2022-05-06
Accepted  2022-08-10
DOI: 10.12015/issn.1674-8034.2022.09.036
Cite this article as: Ma QQ, Feng W, Chen YY, et al. Research progress in predicting axillary lymph node metastasis of breast cancer by preoperative MRI[J]. Chin J Magn Reson Imaging, 2022, 13(9): 151-155.DOI:10.12015/issn.1674-8034.2022.09.036

[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
Rose BS, Jiang W, Punglia RS. Effect of lymph node metastasis size on breast cancer-specific and overall survival in women with node-positive breast cancer[J].Breast Cancer Res Treat, 2015, 152(1): 209-216. DOI: 10.1007/s10549-015-3451-y.
[3]
To B, Isaac D, Andrechek ER. Studying lymphatic metastasis in breast cancer: current models, strategies, and clinical perspectives[J]. J Mammary Gland Biol Neoplasia, 2020, 25(3): 191-203. DOI: 10.1007/s10911-020-09460-5.
[4]
Giuliano AE. Axillary dissection vs No axillary dissection in women with invasive breast cancer and sentinel node metastasis[J]. JAMA, 2011, 305(6): 569-575. DOI: 10.1001/jama.2011.90.
[5]
Lyman GH, Temin S, Edge SB, et al. Sentinel lymph node biopsy for patients with early-stage breast cancer: American Society of Clinical Oncology clinical practice guideline update[J]. J Clin Oncol, 2014, 32(13): 1365-1383. DOI: 10.1200/JCO.2013.54.1177.
[6]
Kootstra J, Hoekstra-Weebers JEHM, Rietman H, et al. Quality of life after sentinel lymph node biopsy or axillary lymph node dissection in stage Ⅰ/Ⅱ breast cancer patients: a prospective longitudinal study[J]. Ann Surg Oncol, 2008, 15(9): 2533-2541. DOI: 10.1245/s10434-008-9996-9.
[7]
Liang Y, Yi L, Liu P, et al. CX3CL1 involves in breast cancer metastasizing to the spine via the Src/FAK signaling pathway[J]. J Cancer, 2018, 9(19): 3603-3612. DOI: 10.7150/jca.26497.
[8]
Giuliano AE, Ballman KV, McCall L, et al. Effect of axillary dissection vs No axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (alliance) randomized clinical trial[J]. JAMA, 2017, 318(10): 918-926. DOI: 10.1001/jama.2017.11470.
[9]
Liu YY, Luo HB, Wang CH, 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.
[10]
Gao Y, Feng W, Lu XR, et al. Difference of DCE-MRI parameters at different time points and their predictive value for axillary lymph node metastasis of breast cancer[J]. Acad Radiol, 2022, 29(Suppl 1): S79-S86. DOI: 10.1016/j.acra.2021.01.013.
[11]
Sun K, Zhu H, Chai WM, 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.
[12]
Song DL, Yang F, Zhang YJ, et al. Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer[J/OL]. Cancer Imaging, 2022, 22(1) [2022-05-03]. https://doi.org/10.1186/s40644-022-00450-w. DOI: 10.1186/s40644-022-00450-w.
[13]
Meinel LA, Abe H, Bergtholdt M, et al. Multi-modality morphological correlation of axillary lymph nodes[J].Int J Comput Assist Radiol Surg, 2010, 5(4): 343-350. DOI: 10.1007/s11548-010-0421-z.
[14]
Baltzer PA, Dietzel M, Burmeister HP, et al. Application of MR mammography beyond local staging: is there a potential to accurately assess axillary lymph nodes? evaluation of an extended protocol in an initial prospective study[J]. AJR Am J Roentgenol, 2011, 196(5): W641-W647. DOI: 10.2214/AJR.10.4889.
[15]
Luciani A, Dao TH, Lapeyre M, et al. Simultaneous bilateral breast and high-resolution axillary MRI of patients with breast cancer: preliminary results[J]. AJR Am J Roentgenol, 2004, 182(4): 1059-1067. DOI: 10.2214/ajr.182.4.1821059.
[16]
Ji J, Sheng MH, Tang WX, et al. Application of multi-parameter MRI and Cyclin D1 in predicting axillary lymph node metastasis of breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(10): 1-5, 11. DOI: 10.12015/issn.1674-8034.2021.10.001.
[17]
Kettunen T, Okuma H, Auvinen P, et al. Peritumoral ADC values in breast cancer: region of interest selection, associations with hyaluronan intensity, and prognostic significance[J]. Eur Radiol, 2020, 30(1): 38-46. DOI: 10.1007/s00330-019-06361-y.
[18]
Kato F, Kudo K, Yamashita H, et al. Predicting metastasis in clinically negative axillary lymph nodes with minimum apparent diffusion coefficient value in luminal A-like breast cancer[J]. Breast Cancer, 2019, 26(5): 628-636. DOI: 10.1007/s12282-019-00969-0.
[19]
Surov A, Chang YW, Li LH, et al. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis[J/OL]. BMC Cancer, 2019, 19(1) [2022-05-03]. https://doi.org/10.1186/s12885-019-6298-5. DOI: 10.1186/s12885-019-6298-5.
[20]
Liu CL, Wang K, Chan Q, et al. Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging[J]. Eur Radiol, 2016, 26(11): 3888-3898. DOI: 10.1007/s00330-016-4241-6.
[21]
Le Bihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders[J]. Radiology, 1986, 161(2): 401-407. DOI: 10.1148/radiology.161.2.3763909.
[22]
Suo ST, Cheng F, Cao MQ, et al. Multiparametric diffusion-weighted imaging in breast lesions: association with pathologic diagnosis and prognostic factors[J]. J Magn Reson Imaging, 2017, 46(3): 740-750. DOI: 10.1002/jmri.25612.
[23]
Feng W, Gao Y, Lu XR, et al. Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer[J]. Magn Reson Imaging, 2022, 85: 262-270. DOI: 10.1016/j.mri.2021.10.027.
[24]
Zhao M, Wu Q, Guo LL, et al. Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer[J/OL]. Eur J Radiol, 2020, 129 [2022-05-03]. https://doi.org/10.1016/j.ejrad.2020.109093. DOI: 10.1016/j.ejrad.2020.109093.
[25]
Liang JY, Zeng SH, Li ZP, et al. Intravoxel incoherent motion diffusion-weighted imaging for quantitative differentiation of breast tumors: a Meta-analysis[J/OL]. Front Oncol, 2020, 10(8) [2022-05-03]. https://doi.org/10.3389/fonc.2020.585486. DOI: 10.3389/fonc.2020.585486.
[26]
Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications[J]. J Magn Reson Imaging, 2001, 13(4): 534-546. DOI: 10.1002/jmri.1076.
[27]
Furman-Haran E, Grobgeld D, Nissan N, et al. Can diffusion tensor anisotropy indices assist in breast cancer detection?[J]. J Magn Reson Imaging, 2016, 44(6): 1624-1632. DOI: 10.1002/jmri.25292.
[28]
Partridge SC, Ziadloo A, Murthy R, et al. Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors[J]. J Magn Reson Imaging, 2010, 31(2): 339-347. DOI: 10.1002/jmri.22045.
[29]
Ozal ST, Inci E, Gemici AA, et al. Can 3.0 Tesla diffusion tensor Imaging parameters be prognostic indicators in breast cancer?[J]. Clin Imaging, 2018, 51: 240-247. DOI: 10.1016/j.clinimag.2018.03.022.
[30]
Kim JY, Kim JJ, Kim S, et al. Diffusion tensor magnetic resonance imaging of breast cancer: associations between diffusion metrics and histological prognostic factors[J]. Eur Radiol, 2018, 28(8): 3185-3193. DOI: 10.1007/s00330-018-5429-8.
[31]
Iima M, Yano K, Kataoka M, et al. Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions[J]. Invest Radiol, 2015, 50(4): 205-211. DOI: 10.1097/RLI.0000000000000094.
[32]
Wang WW, Zhang XD, Zhu LM, et al. Prediction of prognostic factors and genotypes in patients with breast cancer using multiple mathematical models of MR diffusion imaging[J/OL]. Front Oncol, 2022, 12 [2022-05-03]. https://doi.org/10.3389/fonc.2022.825264. DOI: 10.3389/fonc.2022.825264.
[33]
Meng N, Wang XJ, Sun J, et al. A comparative study of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in the diagnosis and evaluation of breast cancer[J]. Eur Radiol, 2021, 31(3): 1707-1717. DOI: 10.1007/s00330-020-07169-x.
[34]
Huang Y, Lin Y, Hu W, et al. Diffusion kurtosis at 3.0T as an in vivo imaging marker for breast cancer characterization: correlation with prognostic factors[J]. J Magn Reson Imaging, 2019, 49(3): 845-856. DOI: 10.1002/jmri.26249.
[35]
You C, Li JW, Zhi WX, et al. The volumetric-tumour histogram-based analysis of intravoxel incoherent motion and non-Gaussian diffusion MRI: association with prognostic factors in HER2-positive breast cancer[J/OL]. J Transl Med, 2019, 17(1) [2022-05-03]. https://doi.org/10.1186/s12967-019-1911-6. DOI: 10.1186/s12967-019-1911-6.
[36]
Szczepankiewicz F, van Westen D, Englund E, et al. The link between diffusion MRI and tumor heterogeneity: mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)[J]. Neuroimage, 2016, 142: 522-532. DOI: 10.1016/j.neuroimage.2016.07.038.
[37]
Özarslan E, Westin CF, Mareci TH. Characterizing magnetic resonance signal decay due to Gaussian diffusion: the path integral approach and a convenient computational method[J]. Concepts Magn Reson Part A Bridg Educ Res, 2015, 44(4): 203-213. DOI: 10.1002/cmr.a.21354.
[38]
Lundell H, Nilsson M, Dyrby TB, et al. Multidimensional diffusion MRI with spectrally modulated gradients reveals unprecedented microstructural detail[J/OL]. Sci Rep, 2019, 9 [2022-05-03]. https://doi.org/10.1038/s41598-019-45235-7. DOI: 10.1038/s41598-019-45235-7.
[39]
Cho E, Baek HJ, Szczepankiewicz F, et al. Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer[J]. Quant Imaging Med Surg, 2022, 12(3): 2002-2017. DOI: 10.21037/qims-21-870.
[40]
Glunde K, Penet MF, Jiang L, et al. Choline metabolism-based molecular diagnosis of cancer: an update[J]. Expert Rev Mol Diagn, 2015, 15(6): 735-747. DOI: 10.1586/14737159.2015.1039515.
[41]
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.
[42]
Pickles MD, Lowry M, Manton DJ, et al. Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators[J]. Eur Radiol, 2015, 25(4): 1097-1106. DOI: 10.1007/s00330-014-3502-5.
[43]
Yi B, Kang DK, Yoon D, et al. Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?[J]. Eur Radiol, 2014, 24(5): 1089-1096. DOI: 10.1007/s00330-014-3100-6.
[44]
Drisis S, Metens T, Ignatiadis M, et al. Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy[J]. Eur Radiol, 2016, 26(5): 1474-1484. DOI: 10.1007/s00330-015-3948-0.
[45]
Gruber L, Rainer V, Plaikner M, et al. CAIPIRINHA-Dixon-TWIST (CDT)-VIBE MR imaging of the liver at 3.0T with gadoxetate disodium: a solution for transient arterial-phase respiratory motion-related artifacts?[J]. Eur Radiol, 2018, 28(5): 2013-2021. DOI: 10.1007/s00330-017-5210-4.
[46]
Li ZW, Ai T, Hu YQ, et al. Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique[J]. J Magn Reson Imaging, 2018, 47(1): 91-96. DOI: 10.1002/jmri.25762.
[47]
Xue M, Che SN, Tian Y, et al. Nomogram based on breast MRI and clinicopathologic features for predicting axillary lymph node metastasis in patients with early-stage invasive breast cancer: a retrospective study[J/OL]. Clin Breast Cancer, 2022, 22(4) [2022-05-03]. https://doi.org/10.1016/j.clbc.2021.10.014. DOI: 10.1016/j.clbc.2021.10.014.
[48]
Wang ZJ, Sun H, Li J, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer using CNN based on multiparametric MRI[J]. J Magn Reson Imaging, 2022, 56(3): 700-709. DOI: 10.1002/jmri.28082.
[49]
Zhan CN, Hu YQ, Wang XR, et al. Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging[J]. Acad Radiol, 2022, 29(Suppl 1): S107-S115. DOI: 10.1016/j.acra.2021.02.008.
[50]
Zhang J, Li LC, Zhe X, et al. The diagnostic performance of machine learning-based radiomics of DCE-MRI in predicting axillary lymph node metastasis in breast cancer: a Meta-analysis[J/OL]. Front Oncol, 2022, 12 [2022-05-03]. https://doi.org/10.3389/fonc.2022. DOI: 10.3389/fonc.2022.799209.
[51]
Qiu Y, Zhang X, Wu ZY, et al. MRI-based radiomics nomogram: prediction of axillary non-sentinel lymph node metastasis in patients with sentinel lymph node-positive breast cancer[J/OL]. Front Oncol, 2022, 12 [2022-05-03]. https://doi.org/10.3389/fonc.2022.811347. DOI: 10.3389/fonc.2022.811347.
[52]
Yu YF, He ZF, 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 [2022-05-03]. https://doi.org/10.1016/j.ebiom.2021.103460. DOI: 10.1016/j.ebiom.2021.103460.

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