To determine feasibility of performing bilateral dual-energy (DE) contrast agent-enhanced (CE) digital mammography and to evaluate its performance compared with conventional digital mammography and breast magnetic resonance (MR) imaging in women with known breast cancer. This study was approved by the institutional review board and was HIPAA compliant. Written informed consent was obtained. Patient accrual began in March 2010 and ended in August 2011. Mean patient age was 49.6 years (range, 25-74 years). Feasibility was evaluated in 10 women with newly diagnosed breast cancer who were injected with 1.5 mL per kilogram of body weight of iohexol and imaged between 2.5 and 10 minutes after injection. Once feasibility was confirmed, 52 women with newly diagnosed cancer who had undergone breast MR imaging gave consent to undergo DE CE digital mammography. Positive findings were confirmed with pathologic findings. Feasibility was confirmed with no adverse events. Visualization of tumor enhancement was independent of timing after contrast agent injection for up to 10 minutes. MR imaging and DE CE digital mammography both depicted 50 (96%) of 52 index tumors; conventional mammography depicted 42 (81%). Lesions depicted by using DE CE digital mammography ranged from 4 to 67 mm in size (median, 17 mm). DE CE digital mammography depicted 14 (56%) of 25 additional ipsilateral cancers compared with 22 (88%) of 25 for MR imaging. There were two false-positive findings with DE CE digital mammography and 13 false-positive findings with MR imaging. There was one contralateral cancer, which was not evident with either modality. Bilateral DE CE digital mammography was feasible and easily accomplished. It was used to detect known primary tumors at a rate comparable to that of MR imaging and higher than that of conventional digital mammography. DE CE digital mammography had a lower sensitivity for detecting additional ipsilateral cancers than did MR imaging, but the specificity was higher. © RSNA, 2012.

Contrast-enhanced mammography is an emerging breast imaging modality that helps improve diagnostic accuracy when routine breast imaging produces inconclusive findings and that may serve as an alter... Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost. Intravenous ...

For screening women at increased risk for breast cancer, the sensitivity of contrast agent–enhanced mammography was 87.5%, compared with 50.0% for digital mammography (P = .03), with a specificity ... BackgroundContrast agent–enhanced digital mammography (CEDM) has been shown to be more sensitive and specific than two-dimensional full-field digital mammography in the diagnostic setting. Few stud...

To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.