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Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer

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Abstract

Objectives

To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer.

Methods

A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram’s performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor.

Results

The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-determined ALN burden alone for predicting SLN burden (area under the curve [AUC]: 0.82 vs. 0.68 [p < 0.001] in training cohort; 0.81 vs. 0.68 in validation cohort [p = 0.04]; and 0.81 vs. 0.58 [p = 0.001] in test cohort). Compared with the MRI-based breast tumor combined descriptors, the radiomics nomogram achieved a higher AUC in test cohort (0.81 vs. 0.58, p = 0.005) and a comparable AUC in training (0.82 vs. 0.73, p = 0.15) and validation (0.81 vs. 0.65, p = 0.31) cohorts.

Conclusion

A multiparametric MRI-based radiomics nomogram can be used for preoperative prediction of the SLN burden in early-stage breast cancer.

Key Points

• Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer.

• Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors.

• Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.

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Abbreviations

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

AUC:

Area under the curve

CI:

Confidence interval

DCE:

Dynamic contrast-enhanced

DWI:

Diffusion-weighted imaging

ER:

Estrogen receptor

HER-2:

Human epidermal growth factor receptor-2

ICC:

Intra-class correlation coefficient

IDI:

Integrated discrimination improvement

LASSO:

Least absolute shrinkage and selection operator

NRI:

Net reclassification improvement

PR:

Progesterone receptor

pSLN:

Pathologic sentinel lymph node

ROC:

Receiver operating characteristic

SLN:

Sentinel lymph node

T1+C:

Delayed contrast-enhanced T1-weighted imaging

T1WI:

Pre-contrast-enhancement T1-weighted imaging

T2WI:

T2-weighted imaging

VOI:

Volume of interest

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Acknowledgements

We thank Yun Huang, Ph.D. (Sun Yat-Sen University), for her kind help with the statistical consultation.

Funding

This work was supported by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2017), the National Natural Science Foundation of China (U1801681), the Key Areas Research and Development Program of Guangdong (2019B020235001), the Medical Artificial Intelligence Project of Sun Yat-Sen Memorial Hospital (YXRGZN201905), Natural Science Foundation of Guangdong Province (2017A030313777, 2018A030313776), and Suzhou Institute of Biomedical Engineering and Technology (#Y753181305).

Author information

Author notes

  1. Xiang Zhang and Zehong Yang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People’s Republic of China

    Xiang Zhang, Zehong Yang, Chushan Zheng, Liejing Lu, Jiaji Mao, Weike Zeng & Jun Shen

  2. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People’s Republic of China

    Xiang Zhang, Zehong Yang, Chushan Zheng, Yudong Li, Liejing Lu, Jiaji Mao, Weike Zeng & Jun Shen

  3. Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou, 215163, People’s Republic of China

    Wenju Cui, Xiaodong Yang & Jian Zheng

  4. Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Shanghai, 200444, People’s Republic of China

    Wenju Cui

  5. Department of Radiology, Sun Yat-Sen University Cancer Center, Sun Yat-Sen University, No. 651 Dongfeng Road East, Guangzhou, 510060, People’s Republic of China

    Haojiang Li

  6. Department of Breast Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People’s Republic of China

    Yudong Li

Authors
  1. Xiang Zhang
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Corresponding authors

Correspondence to Jian Zheng or Jun Shen.

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Guarantor

The scientific guarantor of this publication is Jun Shen.

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.

Statistics and biometry

We thank Yun Huang, Ph.D. (Sun Yat-Sen University), for her kind help with the statistical consultation.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained from the Institutional Review Board of Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University (Guangzhou, China) and Sun Yat-Sen Cancer Center, Sun Yat-Sen University (Guangzhou, China), respectively.

Methodology

• retrospective

• diagnostic study

• multicenter study

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Zhang, X., Yang, Z., Cui, W. et al. Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer. Eur Radiol 31, 5924–5939 (2021). https://doi.org/10.1007/s00330-020-07674-z

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  • DOI: https://doi.org/10.1007/s00330-020-07674-z

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