Background: To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. Methods: One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. Results: In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). Conclusions: In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Background: Diffusion‐weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization.Purpose: To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer.Study Type: Retrospective.Subjects: In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32–87) and underwent multiparametric breast MRI.Field Strength/Sequence: 3 T, readout‐segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast‐enhanced (DCE) T₁‐weighted imaging, T₂‐weighted turbo‐spin echo (TSE) with fatsat.Assessment: Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes.Statistical Tests: Wilcoxon rank sum, Mann–Whitney U‐tests for associations between radiologic features and histopathology; histogram and q‐q plots, Shapiro–Wilk's test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis.Results: Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER‐positive tumors, while PR‐positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers.Data Conclusion: Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer.Level of Evidence: 3Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2019;50:836–846.

Background: The MRI Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon recommends that a breast MRI protocol contain T₂‐weighted and dynamic contrast‐enhanced (DCE) MRI sequences. The addition of diffusion‐weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE‐MRI, DWI, and T₂‐weighted imaging are most strongly associated with a breast cancer diagnosis.Purpose/Hypothesis: To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI‐RADS recommended descriptors for breast MRI with DCE, T₂‐weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping.Study Type: Retrospective.Subjects: In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014.Field Strength/Sequence: IR inversion recovert DCE‐MRI dynamic contrast‐enhanced magnetic resonance imaging VIBE Volume‐Interpolated‐Breathhold‐Examination FLASH turbo fast‐low‐angle‐shot TWIST Time‐resolved angiography with stochastic Trajectories.Assessment: Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T₂‐weighted imaging according to BI‐RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE‐MRI BI‐RADS descriptors, T₂‐weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10⁻³ mm²/sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.Statistical Tests: χ² test, Fisher's exact test, Kruskal–Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer–Lemeshow test of goodness‐of‐fit, receiver operating characteristics analysis.Results: In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T₂‐weighted imaging variables were not included in the final models.Data Conclusion: mpMRI with DCE‐MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE‐MRI and DWI identifies breast cancer with a high diagnostic accuracy. T₂‐weighted imaging does not significantly contribute to breast cancer diagnosis.Level of Evidence: 3Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2019;49:864–874.