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• Clinical Article •
Value of a nomogram based on MRI, mammography and pathology for predicting sentinel lymph node metastasis of mass-type breast invasive ductal carcinoma
ZHU Yun  ZHANG Shuhai  WANG Xiaolei  YANG Zhao  LI Shuhua  YANG Li  TANG Xiaomin  MA Yichuan  XIE Zongyu 

Cite this article as: Zhu Y, Zhang SH, Wang XL, et al. Value of a nomogram based on MRI, mammography and pathology for predicting sentinel lymph node metastasis of mass-type breast invasive ductal carcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 45-51. DOI:10.12015/issn.1674-8034.2022.05.009.


[Abstract] Objective To explore the value of nomogram based on MRI, mammography imaging features and clinicopathological factors for predicting sentinel lymph node (SLN) metastasis in mass-type breast invasive ductal carcinoma.Materials and Methods The clinicopathological and imaging data of patients with invasive ductal carcinoma confirmed by pathology were analyzed retrospectively. A total of 312 cases were included, and randomly divided into training group (234 cases) and verification group (78 cases) according to the ratio of 3∶1. χ2 test or Fisher exact test were used for comparison between the two groups. In the training group, there were 158 cases with negative SLN and 76 cases with positive SLN. The clinicopathological factors, MRI and mammography imaging features were analyzed in these two groups. Multivariate Logistic regression analysis was used to select independent predictors to build a nomogram model for predicting SLN metastasis. Receiver operating characteristics (ROC) curve, calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the model.Results There was no significant difference in clinicopathological factors, MRI and mammography imaging features between the training group and the verification group (P＞0.05). In the training group, there were statistical significances in 11 variables including greatest tumor diameter, clinical T stage, lymph node palpation, progesterone receptor, human epidermal growth factor receptor 2, lymphovascular invasion, MRI [tumor shape, Breast Imaging Reporting and Data System (BI-RADS) classification, axillary lymph node status] , mammography (BI-RADS classification, axillary lymph node status) between negative SLN group and positive SLN group (P＜0.05). Multivariate Logistic regression analysis showed that greatest tumor diameter, lymph node palpation, MRI (axillary lymph node status), mammography (axillary lymph node status) and lymphovascular invasion were independent risk factors for predicting SLN metastasis. The Nomogram model was constructed based on these five variables, the area under ROC curve in the training group and verification group was 0.908 and 0.897, respectively; the P values in Hosmer-Lemeshow goodness-of-fit test were 0.883 and 0.579, respectively (P＞0.05).Conclusions The nomogram model based on MRI and mammography imaging features combined with clinicopathological factors can be used to predict the metastasis of SLN in patients with mass-type breast invasive ductal carcinoma.
[Keywords] breast cancer；invasive ductal carcinoma；nomogram；sentinel lymph node；magnetic resonance imaging；mammography

ZHU Yun1, 2   ZHANG Shuhai1   WANG Xiaolei1   YANG Zhao1   LI Shuhua1, 2   YANG Li1, 2   TANG Xiaomin1, 2   MA Yichuan1, 2   XIE Zongyu1, 2*  

1 Department of Radiology, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233004, China

2 Department of Medical Image Diagnostics Section, Bengbu Medical College, Bengbu 233004, China

Xie ZY, E-mail: zongyuxie@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Key Project of the Natural Science Fund of Anhui Provincial Department of Education (No. KJ2019A0402); Key Project of Natural Science of Bengbu Medical College (No. 2020byzd145).
Received  2021-07-31
Accepted  2022-04-02
DOI: 10.12015/issn.1674-8034.2022.05.009
Cite this article as: Zhu Y, Zhang SH, Wang XL, et al. Value of a nomogram based on MRI, mammography and pathology for predicting sentinel lymph node metastasis of mass-type breast invasive ductal carcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 45-51. DOI:10.12015/issn.1674-8034.2022.05.009.

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