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

  • Lu Han
  • Yongbei Zhu
  • Zhenyu Liu
  • Tao Yu
  • Cuiju He
  • Wenyan Jiang
  • Yangyang Kan
  • Di DongEmail author
  • Jie Tian
  • Yahong LuoEmail author
Breast
  • 59 Downloads

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.

Keywords

Breast cancer Axillary lymph node metastasis Radiomics Preoperative prediction MRI 

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

Notes

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).

Compliance with ethical standards

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.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Cancer Hospital of China Medical UniversityShenyangChina
  2. 2.Liaoning Cancer Hospital & InstituteShenyangChina
  3. 3.CAS Key Laboratory of Molecular Imaging, Chinese Academy of SciencesInstitute of AutomationBeijingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang UniversityBeijingChina

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