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European Radiology

, Volume 29, Issue 5, pp 2535–2544 | Cite as

Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging

  • Tianwen Xie
  • Qiufeng Zhao
  • Caixia Fu
  • Qianming Bai
  • Xiaoyan Zhou
  • Lihua Li
  • Robert Grimm
  • Li Liu
  • Yajia Gu
  • Weijun PengEmail author
Breast

Abstract

Purpose

To identify triple-negative (TN) breast cancer imaging biomarkers in comparison to other molecular subtypes using multiparametric MR imaging maps and whole-tumor histogram analysis.

Materials and methods

This retrospective study included 134 patients with invasive ductal carcinoma. Whole-tumor histogram-based texture features were extracted from a quantitative ADC map and DCE semi-quantitative maps (washin and washout). Univariate analysis using the Student’s t test or Mann–Whitney U test was performed to identify significant variables for differentiating TN cancer from other subtypes. The ROC curves were generated based on the significant variables identified from the univariate analysis. The AUC, sensitivity, and specificity for subtype differentiation were reported.

Results

The significant parameters on the univariate analysis achieved an AUC of 0.710 (95% confidence interval [CI] 0.562, 0.858) with a sensitivity of 63.6% and a specificity of 73.1% at the best cutoff point for differentiating TN cancers from Luminal A cancers. An AUC of 0.763 (95% CI 0.608, 0.917) with a sensitivity of 86.4% and a specificity of 72.2% was achieved for differentiating TN cancers from human epidermal growth factor receptor 2 (HER2) positive cancers. Also, an AUC of 0.683 (95% CI 0.556, 0.809) with a sensitivity of 54.5% and a specificity of 83.9% was achieved for differentiating TN cancers from non-TN cancers. There was no significant feature on the univariate analysis for TN cancers versus Luminal B cancers.

Conclusions

Whole-tumor histogram-based imaging features derived from ADC, along with washin and washout maps, provide a non-invasive analytical approach for discriminating TN cancers from other subtypes.

Key Points

Whole-tumor histogram-based features on MR multiparametric maps can help to assess biological characterization of breast cancer.

• Histogram-based texture analysis may predict the molecular subtypes of breast cancer.

• Combined DWI and DCE evaluation helps to identify triple-negative breast cancer.

Keywords

Triple-negative breast cancer Magnetic resonance imaging Classification Immunologic subtyping ROC curve 

Abbreviations

ADC

Apparent diffusion coefficient

AUC

Area under the curve

DCE

Dynamic contrast-enhanced imaging

DWI

Diffusion-weighted imaging

ER

Estrogen receptor

HER2

Human epidermal growth factor receptor 2

IDC

Invasive ductal carcinoma

IHC

Immunohistochemical

PR

Progesterone receptor

SD

Standard deviation

SI

Signal intensity

TN

Triple-negative

Notes

Funding

This study has received funding by the National Natural Science Foundation of China (61731008).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Weijun Peng.

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

No complex statistical methods were necessary for this paper.

Informed consent

This study is retrospective study and does not require informed consent.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2018_5804_MOESM1_ESM.docx (80 kb)
ESM 1 (DOCX 80 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyFudan University Shanghai Cancer CenterShanghaiPeople’s Republic of China
  2. 2.Department of Radiology, Longhua HospitalShanghai University of Traditional Chinese MedicineShanghaiPeople’s Republic of China
  3. 3.MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd.ShenzhenPeople’s Republic of China
  4. 4.Department of PathologyFudan University Shanghai Cancer CenterShanghaiPeople’s Republic of China
  5. 5.Institute of Biomedical Engineering and InstrumentationHangzhou Dianzi UniversityHangzhouPeople’s Republic of China
  6. 6.MR Application Predevelopment, Siemens HealthineersErlangenGermany

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