Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes

ObjectivesTo assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques.MethodsA total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (Ktrans, Ve, Kep), semiquantitative parameters (W-in, W-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds.ResultsYounger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower Kep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher Kep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10-3 mm2/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811.ConclusionsKep and TTP were independently associated with hormone receptor status. In addition, the Kep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.

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   Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI · 2017

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