4.7 Article

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

期刊

EUROPEAN RADIOLOGY
卷 29, 期 5, 页码 2535-2544

出版社

SPRINGER
DOI: 10.1007/s00330-018-5804-5

关键词

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

资金

  1. National Natural Science Foundation of China [61731008]

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PurposeTo 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 methodsThis 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.ResultsThe 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.ConclusionsWhole-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 center dot Whole-tumor histogram-based features on MR multiparametric maps can help to assess biological characterization of breast cancer.center dot Histogram-based texture analysis may predict the molecular subtypes of breast cancer.center dot Combined DWI and DCE evaluation helps to identify triple-negative breast cancer.

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