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Research progress of radiomics based on MRI for prediction of axillary lymph node metastasis in breast cancer
YU Haitong  LI Qin  WU Shasha  LI Fangzheng  CHEN Yongsheng  NIU Qingliang 

Cite this article as: 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 Imaging, 2023, 14(7): 176-180. DOI:10.12015/issn.1674-8034.2023.07.032.


[Abstract] Axillary lymph node status is very important for the treatment of patients with early breast cancer. Therefore, accurate judgment and identification of axillary lymph node status can avoid unnecessary axillary lymph node dissection and its complications. In view of the limited ability of traditional imaging examination to assess axillary lymph node metastasis, radiomics, as a new research field of quantitative analysis of imaging features, can extract high-throughput quantitative features from standard medical images. At present, many studies have established radiomics models by analyzing MRI routine sequences, functional sequences, combined with clinical information, to predict lymph node metastasis of breast cancer noninvasively before surgery. We reviewed the recent progress of radiomics based on MRI in the study of axillary lymph node metastasis in breast cancer in this paper, in order to accurately and efficiently identify the status of lymph nodes before surgery, so as to guide clinical development of more accurate and personalized treatment strategies.
[Keywords] breast cancer；axillary lymph node；magnetic resonance imaging；radiomics；predicting

YU Haitong1   LI Qin2   WU Shasha2   LI Fangzheng1   CHEN Yongsheng2   NIU Qingliang2*  

1 School of Medical Imaging, Weifang Medical College, Weifang 261053, China

2 Center of Medical Imaging, Weifang Traditional Chinese Medicine Hospital, Weifang 261041, China

Corresponding author: Niu QL, E-mail: qingliangniu@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Shandong Province Natural Science Foundation (No. ZR202103060229).
Received  2022-08-23
Accepted  2023-06-25
DOI: 10.12015/issn.1674-8034.2023.07.032
Cite this article as: 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 Imaging, 2023, 14(7): 176-180. DOI:10.12015/issn.1674-8034.2023.07.032.

[1]
SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2017[J]. CA A Cancer J Clin, 2017, 67(1): 7-30. DOI: 10.3322/caac.21387.
[2]
KIM M Y. Breast cancer metastasis[J]. Adv Exp Med Biol, 2021, 1187: 183-204. DOI: 10.1007/978-981-32-9620-6_9.
[3]
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 [2022-08-22]. https://pubmed.ncbi.nlm.nih.gov/32043792/. DOI: 10.1634/theoncologist.2019-0427.
[4]
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.
[5]
DIESSNER J, ANDERS L, HERBERT S, et al. Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm[J/OL]. J Cancer Res Clin Oncol, 2022 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/35948829. DOI: 10.1007/s00432-022-04221-9.
[6]
FOWLER A M, CHO S Y. PET imaging for breast cancer[J]. Radiol Clin North Am, 2021, 59(5): 725-735. DOI: 10.1016/j.rcl.2021.05.004.
[7]
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.
[8]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[9]
CIROCCHI R, AMABILE M I, DE LUCA A, 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 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/33781279/. DOI: 10.1186/s12957-021-02209-2.
[10]
BERG J W. The significance of axillary node levels in the study of breast carcinoma[J]. Cancer, 1955, 8(4): 776-778. DOI: 10.1002/1097-0142(1955)8:4<776:aid-cncr2820080421>3.0.co;2-b.
[11]
OKUR O, SAGIROGLU J, KIR G, et al. Diagnostic accuracy of sentinel lymph node biopsy in determining the axillary lymph node metastasis[J]. J Cancer Res Ther, 2020, 16(6): 1265-1268. DOI: 10.4103/jcrt.JCRT_1122_19.
[12]
RAM A S, MATUSZEWSKA K, PETRIK J, et al. Quantitative and semi-quantitative methods for assessing the degree of methylene blue staining in sentinel lymph nodes in dogs[J/OL]. Front Vet Sci, 2021, 8: 758295 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/34746290/. DOI: 10.3389/fvets.2021.758295.
[13]
DIALANI V, JAMES D F, SLANETZ P J. A practical approach to imaging the axilla[J]. Insights Imaging, 2015, 6(2): 217-229. DOI: 10.1007/s13244-014-0367-8.
[14]
ECANOW J S, ABE H, NEWSTEAD G M, et al. Axillary staging of breast cancer: what the radiologist should know[J]. Radiographics, 2013, 33(6): 1589-1612. DOI: 10.1148/rg.336125060.
[15]
BEDI D G, KRISHNAMURTHY R, KRISHNAMURTHY S, et al. Cortical morphologic features of axillary lymph nodes as a predictor of metastasis in breast cancer: in vitro sonographic study[J]. AJR Am J Roentgenol, 2008, 191(3): 646-652. DOI: 10.2214/AJR.07.2460.
[16]
LYMAN G H, TEMIN S, EDGE S B, 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.
[17]
VERONESI P, CORSO G. Standard and controversies in sentinel node in breast cancer patients[J]. Breast, 2019, 48(Suppl 1): S53-S56. DOI: 10.1016/S0960-9776(19)31124-5.
[18]
GIULIANO A E, BALLMAN K, MCCALL L, et al. Locoregional recurrence after sentinel lymph node dissection with or without axillary dissection in patients with sentinel lymph node metastases: long-term follow-up from the American college of surgeons oncology group (alliance) ACOSOG Z0011 randomized trial[J]. Ann Surg, 2016, 264(3): 413-420. DOI: 10.1097/SLA.0000000000001863.
[19]
DONKER M, VAN TIENHOVEN G, STRAVER M E, et al. Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer (EORTC 10981-22023 AMAROS): a randomised, multicentre, open-label, phase 3 non-inferiority trial[J]. Lancet Oncol, 2014, 15(12): 1303-1310. DOI: 10.1016/S1470-2045(14)70460-7.
[20]
GIULIANO A E, BALLMAN K V, 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.
[21]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[22]
SUN Y, REYNOLDS H M, PARAMESWARAN B, et al. Multiparametric MRI and radiomics in prostate cancer: a review[J]. Australas Phys Eng Sci Med, 2019, 42(1): 3-25. DOI: 10.1007/s13246-019-00730-z.
[23]
COZZI L, DINAPOLI N, FOGLIATA A, et al. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy[J/OL]. BMC Cancer, 2017, 17(1): 829 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/29207975/. DOI: 10.1186/s12885-017-3847-7.
[24]
HORVAT N, VEERARAGHAVAN H, KHAN M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy[J]. Radiology, 2018, 287(3): 833-843. DOI: 10.1148/radiol.2018172300.
[25]
THAWANI R, MCLANE M, BEIG N, et al. Radiomics and radiogenomics in lung cancer: a review for the clinician[J/OL]. Lung Cancer, 2018, 115: 34-41 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/29290259/. DOI: 10.1016/j.lungcan.2017.10.015.
[26]
CONTI A, DUGGENTO A, INDOVINA I, et al. Radiomics in breast cancer classification and prediction[J/OL]. Semin Cancer Biol, 2021, 72: 238-250 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/32371013/. DOI: 10.1016/j.semcancer.2020.04.002.
[27]
CHEON H, KIM H J, KIM T H, et al. Invasive breast cancer: prognostic value of peritumoral edema identified at preoperative MR imaging[J]. Radiology, 2018, 287(1): 68-75. DOI: 10.1148/radiol.2017171157.
[28]
TAN H N, GAN F W, WU Y P, et al. Preoperative prediction of axillary lymph node metastasis in breast carcinoma using radiomics features based on the fat-suppressed T2 sequence[J]. Acad Radiol, 2020, 27(9): 1217-1225. DOI: 10.1016/j.acra.2019.11.004.
[29]
SAMIEI S, GRANZIER R W Y, IBRAHIM A, et al. Dedicated axillary MRI-based radiomics analysis for the prediction of axillary lymph node metastasis in breast cancer[J/OL]. Cancers (Basel), 2021, 13(4): 757 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/33673071/. DOI: 10.3390/cancers13040757.
[30]
PARTRIDGE S C. Future applications and innovations of clinical breast magnetic resonance imaging[J]. Top Magn Reson Imaging, 2008, 19(3): 171-176. DOI: 10.1097/RMR.0b013e31818a4090.
[31]
IGARASHI T, FURUBE H, ASHIDA H, et al. Breast MRI for prediction of lymphovascular invasion in breast cancer patients with clinically negative axillary lymph nodes[J/OL]. Eur J Radiol, 2018, 107: 111-118 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/30292254/. DOI: 10.1016/j.ejrad.2018.08.024.
[32]
YUAN Y, CHEN X L, LI Z L, et al. The application of apparent diffusion coefficients derived from intratumoral and peritumoral zones for assessing pathologic prognostic factors in rectal cancer[J]. Eur Radiol, 2022, 32(8): 5106-5118. DOI: 10.1007/s00330-022-08717-3.
[33]
DONG Y H, FENG Q J, YANG W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J]. Eur Radiol, 2018, 28(2): 582-591. DOI: 10.1007/s00330-017-5005-7.
[34]
CHEN Y H, WANG L J, DONG X, et al. Deep learning radiomics of preoperative breast MRI for prediction of axillary lymph node metastasis in breast cancer[J/OL]. J Digit Imaging, 2023: 1-9 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/36973631/. DOI: 10.1007/s10278-023-00818-9.
[35]
SATAKE H, ISHIGAKI S, ITO R, et al. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence[J]. Radiol Med, 2022, 127(1): 39-56. DOI: 10.1007/s11547-021-01423-y.
[36]
LIU J, SUN D, CHEN L L, et al. Radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging for the prediction of sentinel lymph node metastasis in breast cancer[J/OL]. Front Oncol, 2019, 9: 980 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/31632912/. DOI: 10.3389/fonc.2019.00980.
[37]
CUI X Y, WANG N, ZHAO Y, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI[J/OL]. Sci Rep, 2019, 9(1): 2240 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/30783148/. DOI: 10.1038/s41598-019-38502-0.
[38]
LIU C L, DING J, SPUHLER K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2019, 49(1): 131-140. DOI: 10.1002/jmri.26224.
[39]
WANG D W, HU Y Q, ZHAN C N, et al. A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer[J/OL]. Front Oncol, 2022, 12: 940655 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/36338691/. DOI: 10.3389/fonc.2022.940655.
[40]
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 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/35379339/. DOI: 10.1186/s40644-022-00450-w.
[41]
THOMAS A A, AREVALO-PEREZ J, KALEY T, et al. Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma[J]. J Neurooncol, 2015, 125(1): 183-190. DOI: 10.1007/s11060-015-1893-z.
[42]
LIU M J, MAO N, MA H, et al. Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer[J/OL]. Cancer Imaging, 2020, 20(1): 65 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/32933585/. DOI: 10.1186/s40644-020-00342-x.
[43]
ZHANG X, YANG Z H, CUI W J, et al. Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer[J]. Eur Radiol, 2021, 31(8): 5924-5939. DOI: 10.1007/s00330-020-07674-z.
[44]
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.
[45]
YU Y F, TAN Y J, XIE C M, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer[J/OL]. JAMA Netw Open, 2020, 3(12): e2028086 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/33289845/. DOI: 10.1001/jamanetworkopen.2020.28086.
[46]
WANG Z J, 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.
[47]
REN T, LIN S, HUANG P, et al. Convolutional neural network of multiparametric MRI accurately detects axillary lymph node metastasis in breast cancer patients with pre neoadjuvant chemotherapy[J]. Clin Breast Cancer, 2022, 22(2): 170-177. DOI: 10.1016/j.clbc.2021.07.002.
[48]
ZHOU L Q, WU X L, HUANG S Y, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning[J]. Radiology, 2020, 294(1): 19-28. DOI: 10.1148/radiol.2019190372.
[49]
ZHENG X Y, YAO Z, HUANG Y N, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J/OL]. Nat Commun, 2020, 11(1): 1236 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/32144248/. DOI: 10.1038/s41467-020-15027-z.
[50]
GUO X, LIU Z Y, SUN C X, et al. Deep learning radiomics of ultrasonography: identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer[J/OL]. EBioMedicine, 2020, 60: 103018 [2022-06-18]. https://pubmed.ncbi.nlm.nih.gov/32980697/. DOI: 10.1016/j.ebiom.2020.103018.

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