Background Parenchymal Enhancement: A Comprehensive Update.

Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.

Contrast-enhanced Mammography versus MR Imaging of the Breast.

Breast MR imaging and contrast-enhanced mammography (CEM) are both techniques that employ intravenously injected contrast agent to assess breast lesions. This approach is associated with a very high sensitivity for malignant lesions that typically exhibit rapid enhancement due to the leakiness of neovasculature. CEM may be readily available at the breast imaging department and can be performed on the spot. Breast MR imaging provides stronger enhancement than the x-ray-based techniques and offers higher sensitivity. From a patient perspective, both modalities have their benefits and downsides; thus, patient preference could also play a role in the selection of the imaging technique.

Auditing Abbreviated Breast MR Imaging: Clinical Considerations and Implications.

Abbreviated breast MR (AB-MR) imaging is a relatively new breast imaging tool, which maintains diagnostic accuracy while reducing image times compared with full-protocol breast MR (FP-MR) imaging. Breast imaging audits involve calculating individual and organizational metrics, which can be compared with established benchmarks, providing a standard against which performance can be measured. Unlike FP-MR imaging, there are no established benchmarks for AB-MR imaging but studies demonstrate comparable performance for cancer detection rate, positive predictive value 3, sensitivity, and specificity with T2. We review the basics of performing an audit, including strategies to implement if benchmarks are not being met.

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was -2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.

The Frequency and Causes of Not-Detected Breast Malignancy in Dynamic Contrast-Enhanced MRI.

Breast MR is the most sensitive imaging modality, but there are cases of malignant tumors that are not detected in MR. This study evaluated the frequency and main causes of malignant breast lesions not detected in dynamic contrast-enhanced (DCE) MR. A total of 1707 cases of preoperative breast MR performed between 2020 and 2021 were included. Three radiologists individually reviewed the DCE MRs and found not-detected malignancy cases in the MRs. The final cases were decided through consensus. For the selected cases, images other than DCE MRIs, such as mammography, ultrasounds, diffusion-weighted MRs, and, if possible, contrast-enhanced chest CTs, were analyzed. In the final sample, 12 cases were not detected in DCE MR, and the frequency was 0.7% (12/1707). Six cases were not detected due to known non-enhancing histologic features. In four cases, tumors were located in the breast periphery and showed no enhancement in MR. In the remaining two cases, malignant lesions were not identified due to underlying marked levels of BPE. The frequency of not-detected malignancy in DCE MR is rare. Knowing the causes of each case and correlating it with other imaging modalities could be helpful in the diagnosis of breast malignancy in DCE MR.

Contrast Enhanced Mammography in Routine Clinical Practice: Frequency and Malignancy Rates of Enhancing Otherwise Occult Findings.

BACKGROUND: In routine clinical practice, contrast-enhanced mammography (CEM) examinations identify enhancing findings seen only on subtraction images that have no low-energy mammographic or sonographic correlate. The purpose of this study is to report the frequency and malignancy rates of enhancing findings seen only on subtraction images in a tertiary care breast imaging practice. MATERIALS AND METHODS: Consecutive review of CEM exams from December 2015 to May 2020. Chi square tests were used to determine associations between cancer diagnosis and clinical characteristics of enhancing findings seen only on subtraction images, P < .05 indicating a statistically significant difference. RESULTS: Four percent (100/2464) of CEM examinations identified 108 enhancing findings seen only on subtraction images. Twenty of those CEM enhancing findings were directly managed as a multifocal disease. Of those further evaluated with MR, 23% (19/78) with associated MR correlates were treated surgically as presumed multicentric or multifocal disease following multidisciplinary review. The remaining 76% (59/78) of enhancing findings were seen only on subtraction images, these included: 20% (12/59) and downgraded to benign on MR 80% (47/59) with suspicious findings which underwent MR vacuum assisted breast biopsy yielding: 26% (12/47) malignancy, 9% (4/47) high risk, and 66% (31/47) benign diagnoses. CONCLUSION: Enhancing findings seen on subtraction only CEM images are seen in 4% of cases in clinical practice. MR correlation can help characterize CEM findings to: (1) avoid unnecessary biopsy for benign findings, and (2) guide tissue sampling or empiric surgical planning for suspicious findings.

Association of breast cancer risk, density, and stiffness: global tissue stiffness on breast MR elastography (MRE).

PURPOSE: Quantify in vivo biomechanical tissue properties in various breast densities and in average risk and high-risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk based on patient demographics and clinical data. METHODS: Patients with average risk or high-risk of breast cancer underwent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (average versus high). Separately for stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors. RESULTS: 50 average risk and 86 high-risk patients were included. Risk groups were similar in age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high-risk patients (N = 55) compared to average risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR = 4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity. CONCLUSION: A structurally based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high-risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.

Incidental Breast Findings on Computed Tomography and MR Imaging.

Computed tomography (CT) and magnetic resonance (MR) imaging may demonstrate a wide variety of incidental findings in the breast, including primary breast carcinoma, the second most common cancer in women. It important to recognize the spectrum of pathologic conditions in order to properly assess the need for further workup. Some findings may be diagnosed as benign on the basis of CT/ MR imaging and clinical history alone, whereas others will require evaluation with dedicated breast imaging and possibly biopsy. This article serves to guide radiologists' management of the wide spectrum of incidental breast findings encountered on cross-sectional imaging.

Abbreviated MR Imaging for Breast Cancer.

Breast MR imaging is the most sensitive imaging method for the detection of breast cancer and detects more aggressive malignancies than mammography and ultrasound examination. Despite these advantages, breast MR imaging has low use rates for breast cancer screening. Abbreviated breast MR imaging, in which a limited number of breast imaging sequences are obtained, has been proposed as a way to solve cost and patient tolerance issues while preserving the high cancer detection rate of breast MR imaging. This review discusses abbreviated breast MR imaging, including protocols, multicenter clinical trial results, clinical workflow implementation challenges, and future directions.

MR Elastography of the Breast: Evolution of Technique, Case Examples, and Future Directions.

Recognizing that breast cancers present as firm, stiff lesions, the foundation of breast magnetic resonance elastography (MRE) is to combine tissue stiffness parameters with sensitive breast MR contrast-enhanced imaging. Breast MRE is a non-ionizing, cross-sectional MR imaging technique that provides for quantitative viscoelastic properties, including tissue stiffness, elasticity, and viscosity, of breast tissues. Currently, the technique continues to evolve as research surrounding the use of MRE in breast tissue is still developing. In the setting of a newly diagnosed cancer, associated desmoplasia, stiffening of the surrounding stroma, and necrosis are known to be prognostic factors that can add diagnostic information to patient treatment algorithms. In fact, mechanical properties of the tissue might also influence breast cancer risk. For these reasons, exploration of breast MRE has great clinical value. In this review, we will: (1) address the evolution of the various MRE techniques; (2) provide a brief overview of the current clinical studies in breast MRE with interspersed case examples; and (3) suggest directions for future research.

Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations.

PURPOSE: To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm2 in benign and malignant breast lesions. METHODS: Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm2 ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated. RESULTS: The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at bmax = 600 s/mm2 already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction. CONCLUSION: Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.

Overview of Breast Cancer Screening and Diagnosis.

Screening mammography saves lives. The mainstay of screening has been mammography. Multiple alternative options, however, for supplemental imaging are now available. Some are just improved anatomic delineation whereas others include physiology added to anatomy. A third group (molecular imaging) is purely physiologic. This article describes and compares the available options and for which patient populations they should be used.

Improving Breast MR Wait Times: A Model for Transitioning Newly Implemented Diagnostic Imaging Procedures into Routine Clinical Operation.

PURPOSE: The purpose of this quality improvement (QI) initiative was to increase patient access to breast MR while maintaining diagnostic image quality. METHODS: Institutional review board approval was waived for this HIPAA-compliant QI initiative, which was conducted from December 2014 through March 2016. Breast MR wait times, scheduling grids, and staffing models were reviewed to identify root causes of elevated wait times. Breast MR wait times were tracked on a biweekly basis as root causes were identified and action plans were implemented. Patient recall rates for repeat MR imaging were tracked. A retrospective analysis of image quality was performed in a randomly selected sample (20 per month; total: 320 examinations). Wait time and image quality data were analyzed with statistical process control charts and logistic regression. RESULTS: In all, 798 breast MR examinations were performed during the study period. Monthly volume increased from 23 in December 2014 to 50 in March 2016 (range: 23-64). Wait time for a routine breast MRI fell from 101 days before implementation to 5 days at study completion. The technical recall rate was 0.5% (4 of 798); no recall was performed for a technologist-related error or scan quality concern. The proportion of examinations with minor (31% [99 of 320]) or major (3% [9 of 320]) image quality impairments did not significantly change during the study period (P = .69-.70). CONCLUSION: A specialized MR examination was transitioned into routine clinical operation while maintaining image quality. This model may be useful for transitioning other specialized diagnostic imaging examinations into routine clinical practice.

Breast MR Imaging: Atlas of Anatomy, Physiology, Pathophysiology, and Breast Imaging Reporting and Data Systems Lexicon.

The latest edition of the Breast Imaging Reporting and Data Systems lexicon, copyrighted in 2013, contains several changes to the breast MR imaging section. Most changes were implemented to standardize descriptors across breast imaging modalities. New sections on special topics and implant evaluation are included. We review basic MR imaging breast anatomy and a detailed pictorial review of the Breast Imaging Reporting and Data Systems lexicon, including these new sections. In each section we discuss which descriptors are more concerning for malignancy and information radiologists can use to better categorize findings as lower risk or benign.

Role of MR Imaging for the Locoregional Staging of Breast Cancer.

Breast MR imaging has been shown to identify unsuspected sites of cancer in the ipsilateral breast in 16% of women with newly diagnosed breast cancer. Breast MR imaging identifies occult cancer in the contralateral breast in 3% to 5% of women. Early evidence suggests that the added value of MR imaging for staging may be attenuated in women who also undergo tomosynthesis, particularly those with nondense breasts. Breast MR imaging is complementary to ultrasound imaging in evaluating regional nodal basins. Ongoing prospective randomized clinical trials should clarify the impact of preoperative breast MR imaging on clinical outcomes.

Problem-Solving MR Imaging for Equivocal Imaging Findings and Indeterminate Clinical Symptoms of the Breast.

Breast MR imaging is commonly used for high-risk screening and for assessing the extent of disease in patients with newly diagnosed breast cancer, but its utility for assessing suspicious symptoms and equivocal imaging findings is less widely accepted. The authors review current literature and guidelines regarding the use of breast MR imaging for these indications. Overall, problem-solving breast MR imaging is best reserved for pathologic nipple discharge and sonographically occult architectural distortion with limited biopsy options. Further study is necessary to define the role of problem-solving MR imaging for calcifications, mammographic asymmetries, and surgical scarring.

MR Imaging-Guided Breast Interventions: Indications, Key Principles, and Imaging-Pathology Correlation.

MR imaging is now routinely performed for breast cancer screening and staging. For suspicious MR imaging-detected lesions that are mammographically and sonographically occult, MR imaging-guided breast interventions, including biopsy, clip placement, and preoperative needle localization, have been developed to permit accurate tissue diagnosis and aid in surgical planning. These procedures are safe, accurate, and effective when performed according to key principles, including proper patient selection, use of appropriate technique, adequate preprocedure preparation and postprocedure patient care, and postprocedure imaging-pathology correlation. Imaging-pathology correlation after MR imaging-guided biopsy is essential to confirm accurate sampling and guide development of a comprehensive management plan.

Comparison of Contrast-Enhanced Mammography and Contrast-Enhanced Breast MR Imaging.

Contrast-enhanced mammography (CEM) is a contrast-enhanced modality for breast cancer detection that utilizes iodinated contrast and dual-energy imaging performed on a digital mammography unit with only slight modifications. It is approved by the US Food and Drug Administration, commercially available, and in routine clinical use at centers around the world. It has similar sensitivity and specificity to MR Imaging and has advantages in terms of cost, patient acceptability, and examination time. MR Imaging has some advantages compared with CEM, especially in its ability to image the complete axilla and the chest wall.