European Radiology

, Volume 28, Issue 2, pp 582–591 | Cite as

Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI

  • Yuhao Dong
  • Qianjin Feng
  • Wei Yang
  • Zixiao Lu
  • Chunyan Deng
  • Lu Zhang
  • Zhouyang Lian
  • Jing Liu
  • Xiaoning Luo
  • Shufang Pei
  • Xiaokai Mo
  • Wenhui Huang
  • Changhong Liang
  • Bin Zhang
  • Shuixing ZhangEmail author



To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T2-weighted fat suppression (T2-FS) and diffusion-weighted MRI (DWI).


We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T2-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1–10) based on different combination of image features using stepwise forward method.


For T2-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T2-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.


Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.

Key Points

SLN biopsy to access breast cancer metastasis has multiple complications.

• Radiomics uses features extracted from medical images to characterise intratumour heterogeneity.

• We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively.


Imaging Breast cancer Sentinel lymph node metastasis Radiomics Preoperative prediction 



Axillary lymph node


Area under the curve


Diffusion-weighted MRI


Oestrogen receptor


Progesterone receptor


Sentinel lymph node


T2-weighted fat suppression


Compliance with ethical standards


The scientific guarantor of this publication is Shuixing Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.


This study has received funding by the National Scientific Foundation of China (81571664), the Science and Technology Planning Project of Guangdong Province (2014A020212244, 2016A020216020, 2015B010131011) and The Science and Technology Project of Guangdong Province (2015B010131011).

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2017_5005_MOESM1_ESM.docx (37 kb)
ESM 1 (DOCX 36 kb)


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Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Yuhao Dong
    • 1
    • 2
  • Qianjin Feng
    • 3
  • Wei Yang
    • 3
  • Zixiao Lu
    • 3
  • Chunyan Deng
    • 3
  • Lu Zhang
    • 1
  • Zhouyang Lian
    • 1
  • Jing Liu
    • 1
  • Xiaoning Luo
    • 1
  • Shufang Pei
    • 1
  • Xiaokai Mo
    • 1
    • 2
  • Wenhui Huang
    • 1
  • Changhong Liang
    • 1
  • Bin Zhang
    • 1
  • Shuixing Zhang
    • 1
    Email author
  1. 1.Department of RadiologyGuangdong General Hospital/Guangdong Academy of Medical SciencesGuangzhouPeople’s Republic of China
  2. 2.Graduate CollegeShantou University Medical CollegeShantouPeople’s Republic of China
  3. 3.The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical EngineeringSouthern Medical UniversityGuangzhouPeople’s Republic of China

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