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Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined.To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics.Prospective.Eighty-four patients with breast lesions.A 3.0 T/single-shot fast spin echo APT imaging.APTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki-67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER-2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER-2 enriched) were determined.Student t-test, one-way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P 0.05 as statistical significance.APTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2- (1.57% ± 0.64%) and T3-stage lesions (1.54% ± 0.63%) than in T1-stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki-67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER-2 (P = 0.961), and among molecular subtypes (P = 0.073).APT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis-related tumor grade, T stage, and proliferative activity.2 TECHNICAL EFFICACY: Stage 2.

BACKGROUND Metaplastic breast cancer (MBC) is a rare type of breast cancer, characterized histologically by the presence of two or more malignant cell types (epithelial and mesenchymal). This retrospective study aimed to review the imaging and histological features of MBC, with a review of the literature. MATERIAL AND METHODS Nineteen patients with MBC (age range, 28-75 years; mean, 55 years) underwent review of their clinical records, histopathology, immunohistochemistry, and imaging findings, which included mammography, sonography, and magnetic resonance imaging (MRI) with T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and diffusion restriction determined by the apparent diffusion coefficient (ADC) and a time-intensity curve (TIC) for signal intensity. RESULTS The mammographic features of MBC were oval shaped (54.5%), with indistinct margin (45.5%), and high tumor density (72.7%), and on sonography, they were oval shaped (57.1%), with hypo-echogenic areas (85.8%). On MRI, MBC showed moderate hyper-intensity with a high signal intensity in the center of the tumor on T2WI (100%), an indistinct margin (75.0%), and rim enhancement (58.3%). Using a TIC, the early phase showed rapid enhancement, and the delay phase showed a signal plateau (91.7%). DWI showed diffusion restriction in all cases determined by the ADC. Immunohistochemistry showed negative expression of estrogen receptor (ER) (91.0%), progesterone receptor (PR) (81%), and HER2 (erbB-2) (80.0%). CONCLUSIONS Imaging features of MBC on mammography and ultrasound were benign. The use of T2WI MRI showed characteristic features of signal intensity using TIC curve and ADC analysis, which may support biopsy and histological analysis for definitive diagnosis.

To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram. The radiomic signature based on 12 LN status–related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79). We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients. • ALNM is an important factor affecting breast cancer patients’ treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.