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Volume 12 Issue 6
Nov.  2021
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GAO Yuanjing, ZHU Qingli, JIANG Yuxin. Research Progress of Ultrasound Radiomics in Predicting Axillary Lymph Node Metastasis of Breast Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 989-993. DOI: 10.12290/xhyxzz.2021-0187
Citation: GAO Yuanjing, ZHU Qingli, JIANG Yuxin. Research Progress of Ultrasound Radiomics in Predicting Axillary Lymph Node Metastasis of Breast Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 989-993. DOI: 10.12290/xhyxzz.2021-0187
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Research Progress of Ultrasound Radiomics in Predicting Axillary Lymph Node Metastasis of Breast Cancer

  • Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

Funds: 

CAMS Innovation Fund for Medical Science 2020-I2M-C & T-B-033

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  • Corresponding author:

    ZHU Qingli  Tel: 86-10-69155494, E-mail: zqlpumch@126.com

  • Received Date: February 07, 2021
  • Accepted Date: March 04, 2021
  • Available Online: October 24, 2021
  • Issue Publish Date: November 29, 2021
    Graphical Abstract
    • Abstract
  • Breast cancer, as the most common malignant tumor in women worldwide, has become the focus of global attention. Axillary lymph node tumor burden is an important prognostic indicator of it. Ultrasound is the most commonly used imaging method, but its sensitivity is not high, especially for the diagnosis of small and micro lymph node metastasis. In recent years, the emerging Radiomics in machine learning field has been widely used in the field of medical imaging. Because it can extract high-level information of images that is difficult to be recognized by human eyes, it has been used to establish clinical prediction models. This paper introduced the value of preoperative sonography, sketched Radiomics, and summarized the research progress of this method in predicting lymph node metastasis of breast cancer. This new method is expected to provide a reliable basis for individualized and accurate diagnosis and treatment of breast cancer.
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    • References (40)
  • [1]
    DeSantis C, Ma J, Bryan L, et al. Breast cancer statistics, 2013[J]. CA Cancer J Clin, 2014, 64: 52-62. DOI: 10.3322/caac.21203
    [2]
    李贺, 郑荣寿, 张思维. 2014年中国女性乳腺癌发病与死亡分析[J]. 中华肿瘤杂志, 2018, 40: 166-171. DOI: 10.3760/cma.j.issn.0253-3766.2018.03.002

    Li H, Zheng RS, Zhang SW, et al. Incidence and mortality of female breast cancer in China, 2014[J]. Zhonghua Zhongliu Zazhi, 2018, 40: 166-171. DOI: 10.3760/cma.j.issn.0253-3766.2018.03.002
    [3]
    Harbeck N, Gnant M. Breast cancer[J]. Lancet, 2017, 389: 1134-1150. DOI: 10.1016/S0140-6736(16)31891-8
    [4]
    de Boer M, van Deurzen CH, van Dijck JA, et al. Micrometastases or isolated tumor cells and the outcome of breast cancer[J]. N Engl J Med, 2009, 361: 653-663. DOI: 10.1056/NEJMoa0904832
    [5]
    Napel S, Mu W, Jardim-Perassi BV, et al. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats[J]. Cancer, 2018, 124: 4633-4649. DOI: 10.1002/cncr.31630
    [6]
    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: 918-926. DOI: 10.1001/jama.2017.11470
    [7]
    Lyman GH, Somerfield MR, Bosserman LD, 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, 2017, 35: 561-564.
    [8]
    Valente SA, Levine GM, Silverstein MJ, et al. Accuracy of predicting axillary lymph node positivity by physical examination, mammography, ultrasonography, and magnetic resonance imaging[J]. Ann Surg Oncol, 2012, 19: 1825-1830. DOI: 10.1245/s10434-011-2200-7
    [9]
    Alvarez S, Añorbe E, Alcorta P, et al. Role of sonography in the diagnosis of axillary lymph node metastases in breast cancer: a systematic review[J]. AJR Am J Roentgenol, 2006, 186: 1342-1348. DOI: 10.2214/AJR.05.0936
    [10]
    Cools-Lartigue J, Meterissian S. Accuracy of axillary ultrasound in the diagnosis of nodal metastasis in invasive breast cancer: a review[J]. World J Surg, 2012, 36: 46-54. DOI: 10.1007/s00268-011-1319-9
    [11]
    Engohan-Aloghe C, Hottat N, Noël JC. Accuracy of lymph nodes cell block preparation according to ultrasound features in preoperative staging of breast cancer[J]. Diagn Cytopathol, 2010, 38: 5-8. http://www.onacademic.com/detail/journal_1000033827615110_890a.html
    [12]
    Chen X, He Y, Wang J, et al. Feasibility of using negative ultrasonography results of axillary lymph nodes to predict sentinel lymph node metastasis in breast cancer patients[J]. Cancer Med, 2018, 7: 3066-3072. DOI: 10.1002/cam4.1606
    [13]
    Zhu Y, Zhou W, Jia XH, et al. Preoperative Axillary Ultrasound in the Selection of Patients With a Heavy Axillary Tumor Burden in Early-Stage Breast Cancer: What Leads to False-Positive Results?[J]. J Ultrasound Med, 2018, 37: 1357-1365. DOI: 10.1002/jum.14545
    [14]
    Ahmed M, Jozsa F, Baker R, et al. Meta-analysis of tumour burden in pre-operative axillary ultrasound positive and negative breast cancer patients[J]. Breast Cancer Res Treat, 2017, 166: 329-336. DOI: 10.1007/s10549-017-4405-3
    [15]
    Bevilacqua JL, Kattan MW, Fey JV, et al. Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation[J]. J Clin Oncol, 2007, 25: 3670-3679. DOI: 10.1200/JCO.2006.08.8013
    [16]
    Chue KM, Yong WS, Thike AA, et al. Predicting the likelihood of additional lymph node metastasis in sentinel lymph node positive breast cancer: validation of the Memorial Sloan-Kettering Cancer Centre (MSKCC) nomogram[J]. J Clin Pathol, 2014, 67: 112-119. DOI: 10.1136/jclinpath-2013-201524
    [17]
    Rouzier R, Uzan C, Rousseau A, et al. Multicenter prospective evaluation of the reliability of the combined use of two models to predict non-sentinel lymph node status in breast cancer patients with metastatic sentinel lymph nodes: the MSKCC nomogram and the Tenon score. Results of the NOTEGS study[J]. Br J Cancer, 2017, 116: 1135-1140. DOI: 10.1038/bjc.2017.47
    [18]
    Barranger E, Coutant C, Flahault A, et al. An axilla scoring system to predict non-sentinel lymph node status in breast cancer patients with sentinel lymph node involvement[J]. Breast Cancer Res Treat, 2005, 91: 113-119. DOI: 10.1007/s10549-004-5781-z
    [19]
    Coutant C, Rouzier R, Fondrinier E, et al. Validation of the Tenon breast cancer score for predicting non-sentinel lymph node status in breast cancer patients with sentinel lymph node metastasis: a prospective multicenter study[J]. Breast Cancer Res Treat, 2009, 113: 537-543. DOI: 10.1007/s10549-008-9967-7
    [20]
    Han L, Zhu Y, Liu Z, et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer[J]. Eur Radiol, 2019, 29: 3820-3829. DOI: 10.1007/s00330-018-5981-2
    [21]
    van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational Radiomics System to Decode the Radiographic Phenotype[J]. Cancer Res, 2017, 77: e104-e107. DOI: 10.1158/0008-5472.CAN-17-0339
    [22]
    Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278: 563-577. DOI: 10.1148/radiol.2015151169
    [23]
    Fung AD, Collins JA, Campassi C, et al. Performance characteristics of ultrasound-guided fine-needle aspiration of axillary lymph nodes for metastatic breast cancer employing rapid on-site evaluation of adequacy: analysis of 136 cases and review of the literature[J]. Cancer Cytopathol, 2014, 122: 282-291. DOI: 10.1002/cncy.21384
    [24]
    Lee SE, Sim Y, Kim S, et al. Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer[J]. Ultrasonography, 2021, 40: 93-102. DOI: 10.14366/usg.20026
    [25]
    Gao Y, Luo Y, Zhao C, et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients[J]. Eur Radiol, 2021, 31: 928-937. DOI: 10.1007/s00330-020-07181-1
    [26]
    Yu FH, Wang JX, Ye XH, et al. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer[J]. Eur J Radiol, 2019, 119: 108658. DOI: 10.1016/j.ejrad.2019.108658
    [27]
    Qiu X, Jiang Y, Zhao Q, et al. Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?[J]. J Ultrasound Med, 2020, 39: 1897-1905. DOI: 10.1002/jum.15294
    [28]
    Tibshirani R. Regression Shrinkage and Selection Via the Lasso[J]. J R Stat Soc Series B Stat Methodol, 1996, 58: 267-288. http://www.stat.ohio-state.edu/~yklee/882/yongganglasso.pdf
    [29]
    Koelliker SL, Chung MA, Mainiero MB, et al. Axillary lymph nodes: US-guided fine-needle aspiration for initial staging of breast cancer--correlation with primary tumor size[J]. Radiology, 2008, 246: 81-89. DOI: 10.1148/radiol.2463061463
    [30]
    Bevers TB, Helvie M, Bonaccio E, et al. Breast Cancer Screening and Diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology[J]. J Natl Compr Canc Netw, 2018, 16: 1362-1389. DOI: 10.6004/jnccn.2018.0083
    [31]
    Zhou LQ, Wu XL, Huang SY, et al. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning[J]. Radiology, 2020, 294: 19-28. DOI: 10.1148/radiol.2019190372
    [32]
    Sun Q, Lin X, Zhao Y, et al. Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region[J]. Front Oncol, 2020, 10: 53. DOI: 10.3389/fonc.2020.00053
    [33]
    Cheon H, Kim HJ, Kim TH, et al. Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging[J]. Radiology, 2018, 287: 68-75. DOI: 10.1148/radiol.2017171157
    [34]
    Zhou J, Zhan W, Dong Y, et al. Stiffness of the surround-ing tissue of breast lesions evaluated by ultrasound elastography[J]. Eur Radiol, 2014, 24: 1659-1667. DOI: 10.1007/s00330-014-3152-7
    [35]
    Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J]. Nat Commun, 2020, 11: 1236. DOI: 10.1038/s41467-020-15027-z
    [36]
    Guo X, Liu Z, Sun C, et al. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer[J]. EBioMedicine, 2020, 60: 103018. DOI: 10.1016/j.ebiom.2020.103018
    [37]
    索静峰, 张麒, 常婉英, 等. 依托弹性与B型双模态超声影像组学的腋窝淋巴结转移评价[J]. 中国医疗器械杂志, 2017, 41: 313-316. DOI: 10.3969/j.issn.1671-7104.2017.05.001

    Suo JF, Zhang L, Chang WY, et al. Evaluation of Axillary Lymph Node Metastasis by Using Radiomics of Dual-modal Ultrasound Composed of Elastography and B-mode[J]. Zhongguo Yiliao Qixie Zazhi, 2017, 41: 313-316. DOI: 10.3969/j.issn.1671-7104.2017.05.001
    [38]
    Turner RR, Chu KU, Qi K, et al. Pathologic features associated with nonsentinel lymph node metastases in patients with metastatic breast carcinoma in a sentinel lymph node[J]. Cancer, 2000, 89: 574-581. DOI: 10.1002/1097-0142(20000801)89:3<574::AID-CNCR12>3.0.CO;2-Y
    [39]
    Li Q, Bai H, Chen Y, et al. A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme[J]. Sci Rep, 2017, 7: 14331. DOI: 10.1038/s41598-017-14753-7
    [40]
    Ford RA, Price Ⅱ WN. Privacy and Accountability in Black-Box Medicine[EB/OL]. (2016-07-14)[2021-02-08]. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2758121.
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