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.

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.

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.

Objectives MRI is an integral part of breast cancer screening in high-risk patients. We investigated whether the application of the Kaiser score, a clinical decision-support tool, may be used to exclude malignancy in contrast-enhancing lesions classified as BI-RADS 4 on breast MRI screening exams. Methods This retrospective study included 183 consecutive, histologically proven, suspicious (MR BI-RADS 4) lesions detected within our local high-risk screening program. All lesions were evaluated according to the Kaiser score for breast MRI by three readers blinded to the final histopathological diagnosis. The Kaiser score ranges from 1 (lowest, cancer very unlikely) to 11 (highest, cancer very likely) and reflects increasing probabilities of malignancy, with scores greater than 4 requiring biopsy. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. Results There were 142 benign and 41 malignant lesions, diagnosed in 159 patients (mean age, 43.6 years). Median Kaiser scores ranged between 2 and 5 in benign and 7 and 8 in malignant lesions. For all lesions, the Kaiser score’s accuracy, represented by the area under the curve (AUC), ranged between 86.5 and 90.2. The sensitivity of the Kaiser score was high, between 95.1 and 97.6% for all lesions, and was best in mass lesions. Application of the Kaiser score threshold for malignancy (≤ 4) could have potentially avoided 64 (45.1%) to 103 (72.5%) unnecessary biopsies in 142 benign lesions previously classified as BI-RADS 4. Conclusions The use of Kaiser score in high-risk MRI screening reliably excludes malignancy in more than 45% of contrast-enhancing lesions classified as BI-RADS 4. Key Points • The Kaiser score shows high diagnostic accuracy in identifying malignancy in contrast-enhancing lesions in patients undergoing high-risk screening for breast cancer. • The application of the Kaiser score may avoid > 45% of unnecessary breast biopsies in high-risk patients. • The Kaiser score aids decision-making in high-risk breast cancer MRI screening programs.

ObjectivesTo assess whether using the Tree flowchart obviates unnecessary magnetic resonance imaging (MRI)-guided biopsies in breast lesions only visible on MRI.MethodsThis retrospective IRB-approved study evaluated consecutive suspicious (BI-RADS 4) breast lesions only visible on MRI that were referred to our institution for MRI-guided biopsy. All lesions were evaluated according to the Tree flowchart for breast MRI by experienced readers. The Tree flowchart is a decision rule that assigns levels of suspicion to specific combinations of diagnostic criteria. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. To assess reproducibility by kappa statistics, a second reader rated a subset of 82 patients.ResultsThere were 454 patients with 469 histopathologically verified lesions included (98 malignant, 371 benign lesions). The area under the curve (AUC) of the Tree flowchart was 0.873 (95% CI: 0.839–0.901). The inter-reader agreement was almost perfect (kappa: 0.944; 95% CI 0.889–0.998). ROC analysis revealed exclusively benign lesions if the Tree node was ≤2, potentially avoiding unnecessary biopsies in 103 cases (27.8%).ConclusionsUsing the Tree flowchart in breast lesions only visible on MRI, more than 25% of biopsies could be avoided without missing any breast cancer.Key Points• The Tree flowchart may obviate >25% of unnecessary MRI-guided breast biopsies. • This decrease in MRI-guided biopsies does not cause any false-negative cases. • The Tree flowchart predicts 30.6% of malignancies with >98% specificity. • The Tree’s high specificity aids in decision-making after benign biopsy results. Electronic supplementary materialThe online version of this article (doi:10.1007/s00330-017-4755-6) contains supplementary material, which is available to authorized users.

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.

BackgroundAvailable data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI.MethodsIn this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10−3 mm2/s) or malignant (≤ 1.3 × 10−3 mm2/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured.ResultsThere were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = − 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy.ConclusionsDiagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.