Purpose: To assess the potential of machine learning with multiparametric MRI (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients. Materials and Methods: This IRB-approved prospective study included 38 women (median age 46.5 years; range 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3T with DCE, DWI and T2-weighted imaging prior to and after two cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS, quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time) and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, eight classifiers including linear support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), stochastic gradient descent (SGD), decision tree, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) were employed to rank the features. Histopathologic Residual Cancer Burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS) and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the

• The Tree scoring system shows high diagnostic accuracy in mass and non-mass lesions. • The Tree scoring system reduces inter-reader variability related to reader experience. • The Tree scoring system improves diagnostic accuracy in non-expert readers.

• Region of interest placement significantly influences apparent diffusion coefficient of breast tumours. • Minimum and mean apparent diffusion coefficient perform best and are reproducible. • 2D regions of interest perform best and provide rapid measurement times.

Objectives The aim of this study was to assess the potential of noncontrast magnetic resonance imaging (NC-MRI) with diffusion-weighted imaging (DWI) in characterization of breast lesions in comparison to dynamic contrast-enhanced MRI (DCE-MRI) at 3 T. Materials and Methods Consecutive patients with conventional imaging (mammography, ultrasound) BI-RADS 4/5 findings were included in this institutional review board–approved single-center study. All underwent 3 T breast MRI including readout-segmented DWI, DCE, and T2-weighted sequences. Final diagnosis was defined by histopathology or follow-up (>24 months). Two experienced radiologists (R1, R2) independently assigned lesion conspicuity (0 = minimal to 3 = excellent) and BI-RADS scores to NC-MRI (readout-segmented DWI including apparent diffusion coefficient maps) and DCE-MRI (DCE and T2-weighted). Receiver operating characteristics, κ statistics, and visual grading characteristics analysis were applied. Results Sixty-seven malignant and 56 benign lesions were identified in 113 patients (mean age, 54 ± 14 years). Areas under the receiver operating characteristics curves were similar: DCE-MRI: 0.901 (R1), 0.905 (R2); NC-MRI: 0.882 (R1), 0.854 (R2); P > 0.05, respectively. The κ agreement was 0.968 (DCE-MRI) and 0.893 (NC-MRI). Visual grading characteristics analysis revealed superior lesion conspicuity by DCE-MRI (0.661, P < 0.001). Conclusions Diagnostic performance and interreader agreement of both NC-MRI and DCE-MRI is high, indicating a potential use of NC-MRI as an alternative to DCE-MRI. However, inferior lesion conspicuity and lower interreader agreement of NC-MRI need to be considered.

Magnetic resonance imaging (MRI) is a well-established method in breast imaging, with manifold clinical applications, including the non-invasive differentiation between benign and malignant breast lesions, preoperative staging, detection of scar versus recurrence, implant assessment, and the evaluation of high-risk patients. At present, dynamic contrast-enhanced MRI is the most sensitive imaging technique for breast cancer diagnosis, and provides excellent morphological and to some extent also functional information. To compensate for the limited functional information, and to increase the specificity of MRI while preserving its sensitivity, additional functional parameters such as diffusion-weighted imaging and apparent diffusion coefficient mapping, and MR spectroscopic imaging have been investigated and implemented into the clinical routine. Several additional MRI parameters to capture breast cancer biology are still under investigation. MRI at high and ultra-high field strength and advances in hard- and software may also further improve this imaging technique. This article will review the current clinical role of breast MRI, including multiparametric MRI and abbreviated protocols, and provide an outlook on the future of this technique. In addition, the predictive and prognostic value of MRI as well as the evolving field of radiogenomics will be discussed.

The AUQV MR BD measurement system allows a fully automated, user-independent, robust, reproducible, as well as radiation- and compression-free volumetric quantitative BD assessment through different levels of BD. The AUQV MR BD measurements were significantly lower than the currently used qualitative and quantitative MG-based approaches, implying that the current assessment might overestimate breast density with MG.

Objectives To investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsies Methods This IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning-derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen's kappa coefficients. Results Application of the Kaiser score revealed a large area under the ROC curve (0.859-0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader. Conclusions Applying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies. Key Points • The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography. • Application of the Kaiser score may obviate 58.3-65.3% of unnecessary stereotactic biopsies of suspicious calcifications. • High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results.

BackgroundDiffusion‐weighted imaging (DWI) is an MRI technique with the potential to serve as an unenhanced breast cancer detection tool. Synthetic b‐values produce images with high diffusion weighting to suppress residual background signal, while avoiding additional measurement times and reducing artifacts.PurposeTo compare acquired DWI images (at b = 850 s/mm2) and different synthetic b‐values (at b = 1000–2000 s/mm2) in terms of lesion visibility, image quality, and tumor‐to‐tissue contrast in patients with malignant breast tumors.Study TypeRetrospective.PopulationFifty‐three females with malignant breast lesions.Field Strength/SequenceT2w, DWI EPI with STIR fat‐suppression, and dynamic contrast‐enhanced T1w at 3T.AssessmentFrom acquired images using b‐values of 50 and 850 s/mm2, synthetic images were calculated at b = 1000, 1200, 1400, 1600, 1800, and 2000 s/mm2. Four readers independently rated image quality, lesion visibility, preferred b‐value, as well as the lowest and highest b‐value, over the range of b‐values tested, to provide a diagnostic image.Statistical TestsMedians and mean ranks were calculated and compared using the Friedman test and Wilcoxon signed‐rank test. Reproducibility was analyzed by intraclass correlation (ICC), Fleiss, and Cohen's κ.ResultsRelative signal‐to‐noise and contrast‐to‐noise ratios decreased with increasing b‐values, while the signal‐intensity ratio between tumor and tissue increased significantly (P < 0.001). Intermediate b‐values (1200–1800 s/mm2) were rated best concerning image quality and lesion visibility; the preferred b‐value mostly lay at 1200–1600 s/mm2. Lowest and highest acceptable b‐values were 850 s/mm2 and 2000 s/mm2. Interreader agreement was moderate to high concerning image quality (ICC: 0.50–0.67) and lesion visibility (0.70–0.93), but poor concerning preferred and acceptable b‐values (κ = 0.032–0.446).Data ConclusionSynthetically increased b‐values may be a way to improve tumor‐to‐tissue contrast, lesion visibility, and image quality of breast DWI, while avoiding the disadvantages of performing DWI at very high b‐values. Level of Evidence: 3 Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2019;50:1754–1761.