Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging
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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.
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.
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.
• 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.
KeywordsTriple-negative breast cancer Magnetic resonance imaging Classification Immunologic subtyping ROC curve
Apparent diffusion coefficient
Area under the curve
Dynamic contrast-enhanced imaging
Human epidermal growth factor receptor 2
Invasive ductal carcinoma
This study has received funding by the National Natural Science Foundation of China (61731008).
Compliance with ethical standards
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.
This study is retrospective study and does not require informed consent.
Institutional Review Board approval was obtained.
• Diagnostic or prognostic study
• Performed at one institution
- 20.Choi Y, Kim SH, Youn IK, Kang BJ, Park WC, Lee A (2017) Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: comparison with ER-positive subtype. PLoS One. https://doi.org/10.1371/journal.phone.0177903