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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:

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.

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