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: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836–846.

Purpose To describe imaging findings, detection rates and tumor characteristics of breast cancers in a large series of patients with BRCA1 and BRCA2 mutations to potentially streamline screening strategies. Methods An IRB-approved, HIPAA-compliant retrospective analysis of 496 BRCA mutation carriers diagnosed with breast carcinoma from 1999-2013 was performed. Institutional database and electronic medical records were reviewed for mammography and MRI imaging. Patient and tumor characteristics including age at diagnosis, tumor histology, grade, receptor and nodal status were recorded. Results Tumors in BRCA1 mutation carriers exhibited significantly higher nuclear and histological grade compared to BRCA2 (p<0.001). Triple-negative tumors were more frequent in BRCA1 mutation carriers, whereas hormone receptor positive tumors were more frequent in BRCA2 mutation carriers (p<0.001). BRCA2 mutation carriers more frequently presented with ductal carcinoma in situ (DCIS) alone 14% (35/246) and cancers more frequently exhibiting calcifications (p<0.001). Mammography detected fewer cancers in BRCA1 mutation carriers compared to BRCA2 (p=0.04): 81% (186/231) BRCA1 vs. 89% (212/237) BRCA2. MRI detected 99% cancers in each group. Mammography detected cancer in two patients with false negative MRI (1 invasive cancer, 1 DCIS). Detection rates on both mammography and MRI did not significantly differ for women over 40 years and women below 40 years. Conclusions Breast cancers in BRCA1 mutation carriers are associated with more aggressive tumor characteristics compared to BRCA2 and are less well seen on mammography. Mammography rarely identified cancers not visible on MRI. Thus, the omission of mammography in BRCA1 mutation carriers screened with MRI can be considered.

Purpose: To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures: In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), cooccurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, 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 applied for pairwise differentiation of receptor status and molecular Sunitha B. Thakur and Katja Pinker contributed equally to this work.

Background The MRI Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon recommends that a breast MRI protocol contain T 2 ‐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 T 2 ‐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, T 2 ‐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 T 2 ‐weighted imaging according to BI‐RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE‐MRI BI‐RADS descriptors, T 2 ‐weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10 −3 mm 2 /sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference. Statistical Tests χ 2 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 breas...