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Prediction of axillary lymph node metastasis in breast cancer based on radiomics nomogram of MRI
XIA Xudong  DUAN Chengzhou  LI Ming  WANG Yalong  ZHOU Xiaoshan  WANG Gongxia  WANG Haibin  CUI Zhenhua 

Cite this article as: Xia XD, Duan CZ, Li M, et al. Prediction of axillary lymph node metastasis in breast cancer based on radiomics nomogram of MRI[J]. Chin J Magn Reson Imaging, 2022, 13(1): 118-122. DOI:10.12015/issn.1674-8034.2022.01.024.


[Abstract] Objective To establish and verify a radiomics nomogram based on MRI for predicting small axillary lymph node (ALN) metastasis in breast cancer.Materials and Methods: A retrospective analysis of 238 patients with breast cancer confirmed by pathological from January 2018 to April 2021. ALN texture features were extracted based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and T2 fat suppression sequence, and stratified sampling was used to divide the group into training (n=168) and testing (n=70) groups according to ratio of 7∶3, linear regression and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the feature. Based on the regression coefficients of the screened features. Multi-factor Logistic regression models combining independent factors from radiomic signature and MR imaging characteristics were developed and presented in the form of nomogram. Receiver operating characteristic (ROC) curve was used to evaluate the performance of the model. Hosmer-Lemeshow test was used and calibration curve was plotted to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical application value of the model.Results Univariate and multivariate analysis showed that Rad-score, short-to-long axis ratio and ADC value were independent factors in identifying lymph node metastases. Rad-score was the most important factor (OR=1.413, P＜0.001) with areas under the ROC curve (AUC) of 0.867 and 0.887 for the training and testing groups, respectively. The model showed good calibration and discrimination with AUC of 0.972 (95% CI: 0.950—0.994) in the training set and 0.938 (95% CI: 0.882—0.993) in the validation set. DCA findings indicated that the nomogram model was clinically useful.Conclusions The MRI-based radiomics nomogram model could be used to preoperatively predict the ALN metastasis of breast cancer.
[Keywords] breast tumor；axillary lymph node；magnetic resonance imaging；radiomic

XIA Xudong   DUAN Chengzhou*   LI Ming   WANG Yalong   ZHOU Xiaoshan   WANG Gongxia   WANG Haibin   CUI Zhenhua  

Department of Radiology, Anyang Tumor Hospital, Anyang 455000, China

Duan CZ, E-mail: ayduancz@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Key Specialized Research and Development Breakthrough Program in Anyang City (No. 20313).
Received  2021-08-08
Accepted  2021-11-09
DOI: 10.12015/issn.1674-8034.2022.01.024
Cite this article as: Xia XD, Duan CZ, Li M, et al. Prediction of axillary lymph node metastasis in breast cancer based on radiomics nomogram of MRI[J]. Chin J Magn Reson Imaging, 2022, 13(1): 118-122. DOI:10.12015/issn.1674-8034.2022.01.024.

[1]
Ahmed M, Purushotham AD, Douek M. Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review[J]. Lancet Oncology, 2014, 15(8): e351-362. DOI: 10.1016/S1470-2045(13)70590-4.
[2]
Motomura K. Sentinel node biopsy for breast cancer: past, present, and future[J]. Breast Cancer, 2015, 22(3): 212-220. DOI: 10.1007/s12282-012-0421-7.
[3]
Killelea BK, Long JB, Dang W, et al. Associations between sentinel lymph node biopsy and complications for patients with ductal carcinoma in situ[J]. Ann Surg Oncol, 2018, 25(6): 1521-1529. DOI: 10.1245/s10434-018-6410-0.
[4]
Hindié E, Groheux D, Brenot-Rossi I, et al. The sentinel node procedure in breast cancer: nuclear medicine as the starting point[J]. J Nucl Med, 2011, 52(3): 405-414. DOI: 10.2967/jnumed.110.081711.
[5]
Layfield LJ, Zhang T, Esebua M, et al. Axillary lymph node metastasis: Fine-needle aspiration biopsy sensitivity as a function of node size, percent replacement of lymph node by tumor and tumor deposit size[J]. Diagn Cytopathol, 2021, 49(2): 181-186. DOI: 10.1002/dc.24617.
[6]
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.
[7]
Borggreve AS, Goense L, Brenkman HJF, et al. Imaging strategies in the management of gastric cancer: current role and future potential of MRI[J]. Br J Radiol, 2019, 92(1097): 20181044. DOI: 10.1259/bjr.20181044.
[8]
McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening[J]. Nature, 2020, 577(7788): 89-94. DOI: 10.1038/s41586-019-1799-6.
[9]
Xu D, Yu C, Zhang X. Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging[J]. BJR Open, 2019, 1(1): 20180023. DOI: 10.1259/bjro.20180023.
[10]
Zong Q, Deng J, Ge W, et al. Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer[J]. Cancer Manag Res, 2020, 12: 2025. DOI: 10.2147/CMAR.S241641.
[11]
Zhou WJ, Zhang YD, Kong WT, et al. Preoperative prediction of axillary lymph node metastasis in patients with breast cancer based on radiomics of gray-scale ultrasonography[J]. Gland Surg, 2021, 10(6): 1989-2001. DOI: 10.21037/gs-21-315.
[12]
Chang JM, Leung JWT, Moy L, et al. Axillary nodal evaluation in breast cancer: state of the art[J]. Radiology, 2020, 295: 500-15. DOI: 10.1148/radiol.2020192534.
[13]
Atallah D, Moubarak M, Arab W, et al.MRI-based predictive factors of axillary lymph node status in breast cancer[J]. Breast J, 2020, 26(11): 2177-2182. DOI: 10.1111/tbj.14089.
[14]
Schrembs P, Martin B, Anthuber M, et al. The prognostic significance of lymph node size in node-positive colon cancer[J]. Plos one, 2018, 13(8): e0201072. DOI: 10.1371/journal.pone.0201072.
[15]
Guvenc I, Whitman GJ, Liu P, et al. Diffusion-weighted MR imaging increases diagnostic accuracy of breast MR imaging for predicting axillary metastases in breast cancer patients[J]. Breast J, 2019, 25(1): 47-55. DOI: 10.1111/tbj.13151.
[16]
Razek AA, Lattif MA, 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.
[17]
Yu Y, He Z, 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]. EBioMedicine, 2021, 69: 103460. DOI: 10.1016/j.ebiom.2021.103460.
[18]
Samiei S, Granzier RWY, Ibrahim A, et al. Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer[J]. Cancers, 2021, 13(4): 757. DOI: 10.3390/cancers13040757.
[19]
Luo HB, Liu YY, Qing HM, et al. Preliminary study of DCE-MRI imaging features of axillary lymph nodes in breast cancer for the diagnosis of metastatic status[J]. J Clin Radiol, 2021, 40(3): 442-447. DOI: 10.13437/j.cnki.jcr.2021.03.008.
[20]
Yang C, Dong J, Liu Z, et al. Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography[J]. Front Oncol, 2021, 11: 726240. DOI: 10.3389/fonc.2021.726240.
[21]
Calabrese A, Santucci D, Landi R, et al. Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art. J Cancer Res Clin Oncol, 2021, 147(6): 1587-1597. DOI: 10.1007/s00432-021-03606-6.
[22]
Yu YF, Tan YJ, Xie CM, 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]. JAMA network open, 2020, 3(12): e2028086. DOI: 10.1001/jamanetworkopen.2020.28086.
[23]
Tan HN, Gan FW, Wu YP 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.
[24]
Shan YN, Xu W, Wang R et al. A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer[J]. Front Oncol, 2020, 10: 1463. DOI: 10.3389/fonc.2020.01463.

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