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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.

[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
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(11): 1362-1389. DOI: 10.6004/jnccn.2018.0083.
[3]
Esposito E, di Micco R, Gentilini OD. Sentinel node biopsy in early breast cancer. A review on recent and ongoing randomized trials[J]. Breast, 2017, 36: 14-19. DOI: 10.1016/j.breast.2017.08.006.
[4]
Han L, Zhu YB, Liu ZY, et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer[J]. Eur Radiol, 2019, 29(7): 3820-3829. DOI: 10.1007/s00330-018-5981-2.
[5]
Sadeghi M, Alamdaran SA, Daneshpajouhnejad P, et al. A logistic regression nomogram to predict axillary lymph node metastasis in early invasive breast cancer patients[J]. Breast J, 2019, 25(4): 769-771. DOI: 10.1111/tbj.13340.
[6]
Zhu YY, Lv WH, Wu H, et al. A preoperative nomogram for predicting the risk of sentinel lymph node metastasis in patients with T1-2N0 breast cancer[J/OL]. Jpn J Radiol, 2022. (2022-1-22)[2022-3-23]. https://link.springer.com/article/10.1007/s11604-021-01236-z. DOI: 10.1007/s11604-021-01236-z.
[7]
Chen L, Duan HY, Tang XM, et al. A mammography-based nomogram for prediction of malignancy in breast suspicious calcification[J]. Acad Radiol, 2021: (21)00416-5. DOI: 10.1016/j.acra.2021.09.003.
[8]
Ma WM, Li J, He N, et al. The application value of a nomogram based on breast MRI and axillary ultrasonography for predicting sentinel lymph node metastasis of early-stage breast cancer[J]. Chin J Radiol, 2020, 54(7): 694-701. DOI: 10.3760/cma.j.cn112149-20200420-00576.
[9]
D'Orsi CJ, Sickles EA, Mendelson EB, et al. ACR BI-RADS® atlas, breast imaging reporting and data system[Z]. Reston: American College of Radiology, 2013.
[10]
Yun SJ, Sohn YM, Seo M. Differentiation of benign and metastatic axillary lymph nodes in breast cancer: additive value of MRI computer-aided evaluation[J]. Clin Radiol, 2016, 71(4): 403.e1-403.e7. DOI: 10.1016/j.crad.2016.01.008.
[11]
Hyun SJ, Kim EK, Yoon JH, et al. Adding MRI to ultrasound and ultrasound-guided fine-needle aspiration reduces the false-negative rate of axillary lymph node metastasis diagnosis in breast cancer patients[J]. Clin Radiol, 2015, 70(7): 716-722. DOI: 10.1016/j.crad.2015.03.004.
[12]
Xue M, Li J, Che SN, et al. The correlation between multiparametric MR imaging characteristics of breast cancer and axillary lymph node metastasis[J]. Chin J Magn Reson Imaging, 2020, 11(7): 540-545. DOI: 10.12015/issn.1674-8034.2020.07.013.
[13]
Wang CH, Chen XY, Luo HB, et al. Development and internal validation of a preoperative prediction model for sentinel lymph node status in breast cancer: combining radiomics signature and clinical factors[J]. Front Oncol, 2021, 11: 754843. DOI: 10.3389/fonc.2021.754843.
[14]
Fusco R, Sansone M, Granata V, et al. Use of quantitative morphological and functional features for assessment of axillary lymph node in breast dynamic contrast-enhanced magnetic resonance imaging[J]. Biomed Res Int, 2018, 2018: 2610801. DOI: 10.1155/2018/2610801.
[15]
Zong QQ, Deng J, Ge WL, et al. Establishment of simple nomograms for predicting axillary lymph node involvement in early breast cancer[J]. Cancer Manag Res, 2020, 12: 2025-2035. DOI: 10.2147/CMAR.S241641.
[16]
Chen K, Liu JQ, Li SR, et al. Development of nomograms to predict axillary lymph node status in breast cancer patients[J]. BMC Cancer, 2017, 17(1): 561. DOI: 10.1186/s12885-017-3535-7.
[17]
Chen WX, Wang C, Fu FM, et al. A model to predict the risk of lymph node metastasis in breast cancer based on clinicopathological characteristics[J]. Cancer Manag Res, 2020, 12: 10439-10447. DOI: 10.2147/CMAR.S272420.
[18]
Chen JY, Chen JJ, Yang BL, et al. Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram[J]. Breast Cancer Res Treat, 2012, 135(3): 839-848. DOI: 10.1007/s10549-012-2219-x.
[19]
Xie F, Yang HP, Wang S, et al. A logistic regression model for predicting axillary lymph node metastases in early breast carcinoma patients[J]. Sensors (Basel), 2012, 12(7): 9936-9950. DOI: 10.3390/s120709936.
[20]
Okuno J, Miyake T, Sota Y, et al. Development of prediction model including microRNA expression for sentinel lymph node metastasis in ER-positive and HER2-negative breast cancer[J]. Ann Surg Oncol, 2021, 28(1): 310-319. DOI: 10.1245/s10434-020-08735-9.
[21]
Yu Y, Wang ZJ, Wei ZY, et al. Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients with 1-2 positive sentinel lymph node macro-metastases: a retrospective analysis of two independent cohorts[J]. BMC Cancer, 2021, 21(1): 466. DOI: 10.1186/s12885-021-08178-9.
[22]
Hu XE, Xue JY, Peng SJ, et al. Preoperative nomogram for predicting sentinel lymph node metastasis risk in breast cancer: a potential application on omitting sentinel lymph node biopsy[J]. Front Oncol, 2021, 11: 665240. DOI: 10.3389/fonc.2021.665240.
[23]
Zhang X, Yang ZH, Cui WJ, et al. Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer[J]. Eur Radiol, 2021, 31(8): 5924-5939. DOI: 10.1007/s00330-020-07674-z.
[24]
Liu C, Zhao ZY, Gu X, et al. Establishment and verification of a bagged-trees-based model for prediction of sentinel lymph node metastasis for early breast cancer patients[J]. Front Oncol, 2019, 9: 282. DOI: 10.3389/fonc.2019.00282.
[25]
Wang LN, Li JT, Qiao JH, et al. Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features[J]. Gland Surg, 2021, 10(5): 1701-1712. DOI: 10.21037/gs-21-245.
[26]
Liu CL, Ding J, Spuhler K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2019, 49(1): 131-140. DOI: 10.1002/jmri.26224.
[27]
Yang ZB, Lan XW, Huang Z, et al. Development and external validation of a nomogram to predict four or more positive nodes in breast cancer patients with one to three positive sentinel lymph nodes[J]. Breast, 2020, 53: 143-151. DOI: 10.1016/j.breast.2020.08.001.

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