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

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Abstract

Objectives

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).

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

ALN:

Axillary lymph node

AUC:

Area under the curve

DWI:

Diffusion-weighted MRI

ER:

Oestrogen receptor

PR:

Progesterone receptor

SLN:

Sentinel lymph node

T2FS:

T2-weighted fat suppression

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Author notes

    Authors and Affiliations

    1. Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People’s Republic of China

      Yuhao Dong, Lu Zhang, Zhouyang Lian, Jing Liu, Xiaoning Luo, Shufang Pei, Xiaokai Mo, Wenhui Huang, Changhong Liang, Bin Zhang & Shuixing Zhang

    2. Graduate College, Shantou University Medical College, Shantou, Guangdong, People’s Republic of China

      Yuhao Dong & Xiaokai Mo

    3. The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China

      Qianjin Feng, Wei Yang, Zixiao Lu & Chunyan Deng

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    1. Yuhao Dong
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    Correspondence to Shuixing Zhang.

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    Guarantor

    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.

    Funding

    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.

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    Written informed consent was waived by the institutional review board.

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    Institutional review board approval was obtained.

    Methodology

    • retrospective

    • diagnostic or prognostic study

    • performed at one institution

    Additional information

    Yuhao Dong, Qianjin Feng and Wei Yang contributed equally to this work.

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    Cite this article

    Dong, Y., Feng, Q., Yang, W. et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 28, 582–591 (2018). https://doi.org/10.1007/s00330-017-5005-7

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