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Clinical Article
Predictive value of preoperative MRI-based nomogram for axillary lymph node metastasis in breast cancer
ZHU Yongqi  JI Hua  ZHU Yanfang  LÜ Jing  LIU Yun 

Cite this article as: Zhu YQ, Ji H, Zhu YF, et al. Predictive value of preoperative MRI-based nomogram for axillary lymph node metastasis in breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(5): 52-58. DOI:10.12015/issn.1674-8034.2022.05.010.


[Abstract] Objective To establish a preoperative breast MRI-based radiomics nomogram and to explore the predictive value of the MRI-based radiomics model for axillary lymph node metastasis (ALNM) in breast cancer.Materials and Methods Between August 2016 and December 2020, the MRI and clinicopathological data of 169 female patients (training set, n=118; validation set, n=51) identified as breast cancer by pathological examination were retrospectively collected and analyzed in the General Hospital of Ningxia Medical University. In the third phase of dynamic contrast-enhanced MRI, a volume of interest (VOI) of the primary breast tumor in each patient was delineated, and then the radiomics features of the VOI were extracted. The Mann-Whitney U test and LASSO regression were used to select the radiomics features and establish radiomics signature. The selected features were weighted by their corresponding coefficients of LASSO regression and then summed to calculate the radiomics score. The Logistic regression was used to select the clinical risk factors and establish a clinical predictive model. In addition, a combined predictive model including the clinical risk factors and radiomics signatures were constructed. The receiver operating characteristic (ROC) curve and the calibration curve were used to evaluate the performances of the models. The Delong test was used to compare the differences of the area under curve (AUC) values among different predictive models. The decision curve analysis (DCA) was conducted to assess the clinical use of these predictive models.Results Among 200 radiomics features extracted from each VOI, 10 of them were associated with the presence of ALNM in breast cancer patients. In the training set and validation set, the radiomics signature had an AUC value of 0.86 and 0.74, respectively; the clinical predictive model had an AUC value of 0.83 and 0.78, respectively; the combined predictive model had an AUC value of 0.86 and 0.80, respectively. DCA showed the clinical use of these three predictive models. The combined predictive model could be visualized by a nomogram.Conclusions A combined model incorporating preoperative MRI-based radiomics signatures and clinical risk factors can be used to predict the presence of ALNM in breast cancer. It provides a new method to preoperative assess the risk of the presence of ALNM in patients with breast cancer.
[Keywords] breast cancer;radiomics;lymph node metastasis;nomogram;magnetic resonance imaging

ZHU Yongqi1   JI Hua1   ZHU Yanfang1   LÜ Jing1   LIU Yun2*  

1 Ningxia Medical University, Yinchuan 750000, China

2 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750000, China

Liu Y, E-mail: yunliusky@163.com

Conflicts of interest   None.

Received  2021-11-30
Accepted  2022-04-08
DOI: 10.12015/issn.1674-8034.2022.05.010
Cite this article as: Zhu YQ, Ji H, Zhu YF, et al. Predictive value of preoperative MRI-based nomogram for axillary lymph node metastasis in breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(5): 52-58.DOI:10.12015/issn.1674-8034.2022.05.010

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