Purpose To investigate the performance of integrated approaches that combined intravoxel incoherent motion (IVIM) and non-Gaussian diffusion parameters compared with the Breast Imaging and Reporting Data System (BI-RADS) to establish multiparameter thresholds scores or probabilities by using Bayesian analysis to distinguish malignant from benign breast lesions and their correlation with molecular prognostic factors. Materials and Methods Between May 2013 and March 2015, 411 patients were prospectively enrolled and 199 patients (allocated to training [n = 99] and validation [n = 100] sets) were included in this study. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm [ADC] and kurtosis [K]) by using IVIM and kurtosis models were estimated from diffusion-weighted image series (16 b values up to 2500 sec/mm), as well as a synthetic ADC (sADC) calculated by using b values of 200 and 1500 (sADC) and a standard ADC calculated by using b values of 0 and 800 sec/mm (ADC). The performance of two diagnostic approaches (combined parameter thresholds and Bayesian analysis) combining IVIM and diffusion parameters was evaluated and compared with BI-RADS performance. The Mann-Whitney U test and a nonparametric multiple comparison test were used to compare their performance to determine benignity or malignancy and as molecular prognostic biomarkers and subtypes of breast cancer. Results Significant differences were found between malignant and benign breast lesions for IVIM and non-Gaussian diffusion parameters (ADC, K, fIVIM, fIVIM · D*, sADC and ADC; P < .05). Sensitivity and specificity for the validation set by radiologists A and B were as follows: sensitivity, 94.7% and 89.5%, and specificity, 75.0% and 79.2% for sADC, respectively; sensitivity, 94.7% and 96.1%, and specificity, 75.0% and 66.7%, for the combined thresholds approach, respectively; sensitivity, 92.1% and 92.1%, and specificity, 83.3% and 66.7%, for Bayesian analysis, respectively; and sensitivity and specificity, 100% and 79.2%, for BI-RADS, respectively. The significant difference in values of sADC in progesterone receptor status (P = .002) was noted. sADC was significantly different between histologic subtypes (P = .006). Conclusion Approaches that combined various IVIM and non-Gaussian diffusion MR imaging parameters may provide BI-RADS-equivalent scores almost comparable to BI-RADS categories without the use of contrast agents. Non-Gaussian diffusion parameters also differed by biologic prognostic factors. RSNA, 2017 Online supplemental material is available for this article.

This study demonstrates an inverse correlation between ADC and Ki-67 index in MBC and the ability of ADC to identify highly proliferating MBC. Considering that ADC can evaluate whole lesions noninvasively, ADC may be a promising noninvasive surrogate marker for Ki-67 index in the risk stratification of MBC.

Purpose: To evaluate the feasibility of ultrafast dynamic contrast-enhanced (UF-DCE) magnetic resonance imaging (MRI) with compressed sensing (CS) for the separate identification of breast arteries/veins and perform temporal evaluations of breast arteries and veins with a focus on the association with ipsilateral cancers. Materials and Methods: Our Institutional Review Board approved this study with retrospective design. Twenty-five female patients who underwent UF-DCE MRI at 3T were included. UF-DCE MRI consisting of 20 continuous frames was acquired using a prototype 3D gradient-echo volumetric interpolated breath-hold sequence including a CS reconstruction: temporal resolution, 3.65 sec/frame; spatial resolution, 0.9 3 1.3 3 2.5 mm. Two readers analyzed 19 maximum intensity projection images reconstructed from subtracted images, separately identified breast arteries/veins and the earliest frame in which they were respectively visualized, and calculated the time interval between arterial and venous visualization (A-V interval) for each breast. Results: In total, 49 breasts including 31 lesions (breast cancer, 16; benign lesion, 15) were identified. In 39 of the 49 breasts (breasts with cancers, 16; breasts with benign lesions, 10; breasts with no lesions, 13), both breast arteries and veins were separately identified. The A-V intervals for breasts with cancers were significantly shorter than those for breasts with benign lesions (P 5 0.043) and no lesions (P 5 0.007). Conclusion: UF-DCE MRI using CS enables the separate identification of breast arteries/veins. Temporal evaluations calculating the time interval between arterial and venous visualization might be helpful in the differentiation of ipsilateral breast cancers from benign lesions. Level of Evidence: 3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;47:97-104. B reast magnetic resonance imaging (MRI) has become an established modality for the identification of breast cancer with high sensitivity (90-100%). [1][2][3][4][5][6][7] The specificity of MR, however, varies widely across studies (65-97%) 1-5 and remains only moderate with 72-75% pooled specificity in meta-analyses. 6,7 The diagnosis of breast MRI is mainly based on morphological evaluation and kinetic curve assessment in clinical practice. 8,9 As a completely different approach to diagnose breast cancer, previous studies have demonstrated an increased breast vascularity associated with ipsilateral breast cancer on MR angiograms generated from conventional dynamic contrast- enhanced (C-DCE) MRI. 10-17 Possible mechanisms for this observation include decreased flow resistance in tumor vessels, high tumor metabolism, and angiogenic stimulation of the entire breast harboring the tumor. 10,12 One of the limitations in these previous reports was the limited temporal resolution. On C-DCE MRI, it is not possible to differentiate breast arteries from veins because of the long scan time (60-120 sec) required for acceptable spatial resolution (in-plane pixel size, 1.0 3 1.0 mm; through-plane...

Background: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery. Methods: This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre-and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre-and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature prefiltering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results: Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions: This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.

Background: Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods: Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant. Results: Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001).Conclusions: Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.