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Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer

European Radiology volume 29, pages 3820–3829 (2019)Cite this article

Abstract

Objective

To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients.

Methods

Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram.

Results

The radiomic signature based on 12 LN status–related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79).

Conclusions

We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients.

Key Points

• ALNM is an important factor affecting breast cancer patients’ treatment and prognosis.

• Traditional imaging examinations have limited value for evaluating axillary LNs status.

• We developed a radiomic nomogram based on MR imagings to predict LN metastasis.

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Abbreviations

ALND:

Axillary lymph node dissection

ALNM:

Axillary lymph node metastasis

AUC:

Area under the curve

CI:

Confidence interval

ER:

Estrogen receptor

GLCM:

Gray level co-occurrence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

HER2:

Human epidermal growth factor receptor 2

LN:

Lymph node

PR:

Progesterone receptor

ROC:

Receiver operating characteristic

SLNB:

Sentinel lymph node biopsy

T1-DCE:

T1-weighted images of dynamic contrast enhanced

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Funding

This study has received funding by Special Fund for Research in the Public Interest of China (201402020), National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1308701, 2017YFC1309100, 2016YFC0103803), National Natural Science Foundation of China (81227901, 81771924, 81501616, 81671851, 81671854, and 81527805), the Beijing Natural Science Foundation (L182061), the Bureau of International Cooperation of Chinese Academy of Sciences (173211KYSB20160053), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the Beijing Municipal Science and Technology Commission (Z171100000117023, Z161100002616022), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201502), and the Youth Innovation Promotion Association CAS (2017175).

Author information

Author notes

  1. Lu Han and Yongbei Zhu contributed equally as the first authors.

Affiliations

  1. Cancer Hospital of China Medical University, Shenyang, 110042, China

    Lu Han, Tao Yu, Cuiju He, Wenyan Jiang, Yangyang Kan & Yahong Luo

  2. Liaoning Cancer Hospital & Institute, Shenyang, 110042, China

    Lu Han, Tao Yu, Cuiju He, Wenyan Jiang, Yangyang Kan & Yahong Luo

  3. CAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China

    Yongbei Zhu, Zhenyu Liu, Di Dong & Jie Tian

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhenyu Liu, Di Dong & Jie Tian

  5. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China

    Jie Tian

Authors
  1. Lu Han
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  2. Yongbei Zhu
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  3. Zhenyu Liu
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  4. Tao Yu
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  8. Di Dong
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  9. Jie Tian
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  10. Yahong Luo
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Corresponding authors

Correspondence to Di Dong or Yahong Luo.

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Guarantor

The scientific guarantor of this publication is Yahong Luo.

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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Cite this article

Han, L., Zhu, Y., Liu, Z. et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol 29, 3820–3829 (2019). https://doi.org/10.1007/s00330-018-5981-2

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Keywords

  • Breast cancer
  • Axillary lymph node metastasis
  • Radiomics
  • Preoperative prediction
  • MRI