Background: In patients who are expected to achieve axillary pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC), omission of axillary lymph node (LN) dissection could prevent morbidity and complications. Purpose: To develop a clinical model to predict residual axillary LN metastasis in patients with clinically node-positive breast cancer after NAC by using MRI and US. Materials and Methods: In this retrospective study, women with clinically node-positive breast cancer who were treated with NAC following surgery between January 2015 and September 2017 were included. The patients were randomly assigned to a test and validation set (7:3 ratio). Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of residual axillary LN metastasis in the test set. A prediction risk score was developed based on the odds ratios from the multivariable analysis and validated in both sets. Results: A total of 408 women were included (mean age 6 standard deviation, 47.9 years 6 9.6). The axillary pCR rate was 56.6% (231 of 408). Independent predictors of residual axillary LN metastasis were clinical stage N2 or N3, presence of axillary lymphadenopathy at US after NAC, tumor size reduction less than 50% at MRI, Ki-67 negativity, hormone receptor positivity, and human epidermal growth factor receptor 2 negativity (all, P , .05). In a model using these predictors, the area under the receiver operating characteristic curve in the test and validation sets was 0.84 (95% confidence interval: 0.79, 0.88) and 0.78 (95% confidence interval: 0.70, 0.87), respectively. When the patients had a simplified risk score of 1, the false-negative rates ranged between 5%-10%. Conclusion: A prediction model incorporating nodal status stage, US finding, MRI response, and molecular receptor status shows good diagnostic performance for residual axillary lymph node metastasis after neoadjuvant chemotherapy in patients with clinically node-positive breast cancer.

• Analysis of enhancement characteristics helped accurate discrimination of IMCCs from HCCs. • Wash-out should be determined on the PVP of gadoxetic acid-enhanced MRI. • A hypointense rim on the HBP was a significant finding of IMCCs.

Background: There is increasing interest in noncontrast-enhanced MRI due to safety concerns for gadolinium contrast agents. Purpose: To investigate the clinical feasibility of MR-based conductivity imaging for breast cancer detection and lesion differentiation. Study Type: Prospective. Subjects: One hundred and ten women, with 112 known cancers and 17 benign lesions (biopsy-proven), scheduled for preoperative MRI. Field Strength/Sequence: Non-fat-suppressed T2-weighted turbo spin-echo sequence (T2WI), dynamic contrast-enhanced MRI and diffusion-weighted imaging (DWI) at 3T. Assessment: Cancer detectability on each imaging modality was qualitatively evaluated on a per-breast basis: the conductivity maps derived from T2WI were independently reviewed by three radiologists (R1-R3). T2WI, DWI, and preoperative digital mammography were independently reviewed by three other radiologists (R4-R6). Conductivity and apparent diffusion coefficient (ADC) measurements (mean, minimum, and maximum) were performed for 112 cancers and 17 benign lesions independently by two radiologists (R1 and R2). Tumor size was measured from surgical specimens. Statistical Tests: Cancer detection rates were compared using generalized estimating equations. Multivariable logistic regression analysis was performed to identify factors associated with cancer detectability. Discriminating ability of conductivity and ADC was evaluated by using the areas under the receiver operating characteristic curve (AUC). Results: Conductivity imaging showed lower cancer detection rates (20%-32%) compared to T2WI (62%-71%), DWI (85%-90%), and mammography (79%-88%) (all P < 0.05). Fatty breast on MRI (odds ratio = 11.8, P < 0.05) and invasive tumor size (odds ratio = 1.7, P < 0.05) were associated with cancer detectability of conductivity imaging. The maximum conductivity showed comparable ability to the mean ADC in discriminating between cancers and benign lesions (AUC = 0.