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

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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.

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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

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Funding

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

Author information

Author notes

  1. Tianwen Xie and Qiufeng Zhao contributed equally to this work and are co-first authors for this study.

Authors and Affiliations

  1. Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong’an Road, Shanghai, 200032, People’s Republic of China

    Tianwen Xie, Li Liu, Yajia Gu & Weijun Peng

  2. Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China

    Qiufeng Zhao

  3. MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, People’s Republic of China

    Caixia Fu

  4. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China

    Qianming Bai & Xiaoyan Zhou

  5. Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People’s Republic of China

    Lihua Li

  6. MR Application Predevelopment, Siemens Healthineers, Erlangen, Germany

    Robert Grimm

Authors
  1. Tianwen Xie
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  2. Qiufeng Zhao
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  3. Caixia Fu
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  4. Qianming Bai
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  5. Xiaoyan Zhou
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  6. Lihua Li
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  7. Robert Grimm
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  8. Li Liu
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  9. Yajia Gu
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  10. Weijun Peng
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Corresponding author

Correspondence to Weijun Peng.

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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

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Xie, T., Zhao, Q., Fu, C. et al. Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging. Eur Radiol 29, 2535–2544 (2019). https://doi.org/10.1007/s00330-018-5804-5

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  • DOI: https://doi.org/10.1007/s00330-018-5804-5

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