Faculty Bibliography

MRI of the breast is the most sensitive test for breast cancer detection and outperforms conventional imaging with mammography, digital breast tomosynthesis, or ultrasound. However, the long scan time and relatively high costs limit its widespread use. Hence, it is currently only routinely implemented in the screening of women at an increased risk of breast cancer. To overcome these limitations, abbreviated dynamic contrast-enhanced (DCE)-MRI protocols have been introduced that substantially shorten image acquisition and interpretation time while maintaining a high diagnostic accuracy. Efforts to develop abbreviated MRI protocols reflect the increasing scrutiny of the disproportionate contribution of radiology to the rising overall healthcare expenditures. Healthcare policy makers are now focusing on curbing the use of advanced imaging examinations such as MRI while continuing to promote the quality and appropriateness of imaging. An important cornerstone of value-based healthcare defines value as the patient's outcome over costs. Therefore, the concept of a fast, abbreviated MRI exam is very appealing, given its high diagnostic accuracy coupled with the possibility of a marked reduction in the cost of an MRI examination. Given recent concerns about gadolinium-based contrast agents, unenhanced MRI techniques such as diffusion-weighted imaging (DWI) are also being investigated for breast cancer diagnosis. Although further larger prospective studies, standardized imaging protocol, and reproducibility studies are necessary, initial results with abbreviated MRI protocols suggest that it seems feasible to offer screening breast DCE-MRI to a broader population. This article aims to give an overview of abbreviated and fast breast MRI protocols, their utility for breast cancer detection, and their emerging role in the new value-based healthcare paradigm that has replaced the fee-for-service model.

Although the majority of male breast problems are benign with gynecomastia as the most common etiology, men with breast symptoms and their referring providers are typically concerned about whether or not it is due to breast cancer. If the differentiation between benign disease and breast cancer cannot be made on the basis of clinical findings, or if the clinical presentation is suspicious, imaging is indicated. The panel recommends the following approach to breast imaging in symptomatic men. In men with clinical findings consistent with gynecomastia or pseudogynecomastia, no imaging is routinely recommended. If an indeterminate breast mass is identified, the initial recommended imaging study is ultrasound in men younger than age 25, and mammography or digital breast tomosynthesis in men age 25 and older. If physical examination is suspicious for a male breast cancer, mammography or digital breast tomosynthesis is recommended irrespective of patient age. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Breast pain is a common complaint. However, in the absence any accompanying suspicious clinical finding (eg, lump or nipple discharge), the association with malignancy is very low (0%-3.0%). When malignancy-related, breast pain tends to be focal (less than one quadrant) and persistent. Pain that is clinically insignificant (nonfocal [greater than one quadrant], diffuse, or cyclical) requires no imaging beyond what is recommended for screening. In cases of pain that is clinically significant (focal and noncyclical), imaging with mammography, digital breast tomosynthesis (DBT), and ultrasound are appropriate, depending on the patient's age. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

In detection, treatment, and follow-up, male breast cancer has historically lagged behind female breast cancer. On the whole, breast cancer is less common among men than among women, limiting utility of screening, yet the incidence of male breast cancer is rising, and there are men at high risk for breast cancer. While women at high risk for breast cancer are well characterized, with clearly established guidelines for screening, supplemental screening, risk prevention, counseling, and advocacy, men at high risk for breast cancer are poorly identified and represent a blind spot in public health. Today, more standardized genetic counseling and wider availability of genetic testing are allowing identification of high-risk male relatives of women with breast cancer, as well as men with genetic mutations predisposing to breast cancer. This could provide a new opportunity to update our approach to male breast cancer. This article reviews male breast cancer demographics, risk factors, tumor biology, and oncogenetics; recognizes how male breast cancer differs from its female counterpart; highlights its diagnostic challenges; discusses the implications of the widening clinical use of multigene panel testing; outlines current National Comprehensive Cancer Network guidelines (version 1, 2018) for high-risk men; and explores the possible utility of targeted screening and surveillance. Understanding the current state of male breast cancer management and its challenges is important to shape future considerations for care. Shifting the paradigm of male breast cancer detection toward targeted precision medicine may be the answer to improving clinical outcomes of this uncommon disease. ^©RSNA, 2018.

OBJECTIVE:The purpose of this study was to investigate county-level geographic patterns of mammographic screening uptake throughout the United States and to determine the impact of rural versus urban settings on breast cancer screening uptake. MATERIALS AND METHODS/METHODS:This descriptive study used County Health Rankings (CHR) data to identify the percentage of Medicare enrollees 67-69 years old per county who had at least one mammogram in 2013 or 2012 (uptake). Uptake was matched with U.S. Department of Agriculture (USDA) Atlas of Rural and Small Town America categorizations along a rural-urban continuum scale from 1 to 9 based on county population size (large urban, population ≥ 20,000 people; small urban, < 20,000 people) and proximity to a metropolitan area. Univariable and multivariable analyses were performed. RESULTS:In all, 2,243,294 Medicare beneficiaries were eligible for mammograms. National mean uptake per county was 60.5% (range, 26.0-86.0%). Uptake was significantly higher in metropolitan and large urban counties in 25 states and lower in only one. County-level mammographic uptake was moderately positively correlated with percentage of residents with some college education (r = 0.40, p < 0.001) and moderately negatively correlated with age-adjusted mortality (r = -0.41, p < 0.001). Multivariable analysis showed that percentage of white and black residents and age-adjusted mortality rate were the strongest significant independent predictors of uptake. CONCLUSION/CONCLUSIONS:Uptake of mammographic screening services in a Medicare population varies widely at the county level and is generally lowest in rural counties and urban counties with fewer than 20,000 people.

Purpose Our aim was to evaluate the accuracy of a new algorithm to automatically delineate the breast region from the chest on T1-weighted, non-fat-suppressed MR images. This process is also referred to as the chest wall detection. There is a general agreement that this step is very difficult to automate. At the same time it is crucially needed for clinically important processing workflows [1]. These workflows include 3D measurement of breast density and of the breast parenchymal enhancement. Both measures reveal patients at risk of breast cancer [2]. Manually traced chest wall was used as the ground truth when estimating the segmentation errors. Segmentation accuracy was evaluated using the Hausdorff distance and the volumetric error. We also estimated the inter-observer agreement in defining the chest wall surface. Methods The program starts by generating the mid-sagittal 2D section by averaging the signal across 20 mm thick mid-sagittal slab. We determine the chest wall boundary on this image by modeling the signal profiles along the antero-posterior direction as a sequence of three tissues: background air, skin and fat layer, muscle. Non-uniformity correction is then applied to the entire 3D volume. The mid-sagittal boundary, represented as a polyline P, is then propagated in two opposite (left and right) directions. At each sagittal section the algorithm adjusts the control points of the polyline received from an adjacent slice. The adjustment is estimated from the weighted sum of six measures that combine specific local and global signal statistics. These include: the local gradient, the signal uniformity, the gradient similarity, the contour-gradient consistency, the global contour uniformity and the normal vector consistency. At each iteration we form a candidate shift vector, we apply it to shift P to its new position, and then we smooth the resulting polyline. The process terminates when the magnitude of the shift becomes negligible or when the specified number of iterations is exceeded. Two metrics were used to estimate accuracy. The conventional volumetric error was obtained by dividing the volume DV of misclassified breast voxels over the true breast volume V. The Hausdorff distance, HD, is the distance between each voxel on the true breast/ chest wall border and the closest boundary voxel produced by the algorithm. HD is averaged over the entire chest wall surface. From a clinical database of screening breast MRIs acquired at our medical center we have randomly selected 16 test exams. The selection was constrained to enforce that there were four exams in each of the four breast density categories [3]. Bilateral breasts were imaged on Siemens 3T Magnetom Trio equipped with a 7-element surface breast coil. The parameters of the T1-weighted non-fat-suppressed sequence were: TR = 4.74 ms, TE = 1.79 ms, FOV = 320 mm2, matrix = 448 9 358 9*150, 0.7 9 0.7 9 1.1 mm voxels, TA = 2-3 min. Three experts in breast and chest anatomy drew contours to separate the chest wall from the breast (Fig. 1). The pectoralis fascia and pectoralis muscles were used as reference points for the anterolateral borders. The medial border of the axilla was the posterolateral boundary. The axillary tail was considered as the breast tissue. The ground truth references were constructed by a software designed to perform voxel-based ROI averaging [4]. (Figure Presented) Results The border distance error HD was 0.84 +/- 0.8 mm (average +/- standard deviation) and ranged from 0.57 to 2.45 mm. The volume error DV/V was 6.43 +/- 6.82%. There was no correlation between the HD and DV/V (R2 = 0.23, p = 0.12). The test cases covered a wide range 411-3439 ml of breast volumes. There was a significant positive correlation (R2 = 0.40, p = 0.02) between volumetric error and the true breast volume V, but there was no correlation between HD and V (R2 = 0.08, p = 0.44). The average execution time was under 1.5 min per case on a standard 8-core workstation. The inter-observer agreement measured in term of HD was 0.56 +/- 0.15 mm (average +/- standard deviation). The agreement expressed in terms of volumetric discrepancy (relative to breast volume) was 1.61% +/- 0.71%. Conclusion Breast density, defined as fraction of fibroglandular tissue, and postcontrast enhancement, are considered significant risk factors for breast cancer. These MRI measures are recommended for radiologic reports and are promising cancer biomarkers. Radiologists currently visually estimate these measure. Unfortunately, readers agreement for qualitative evaluation is only fair, requiring better standardization and reproducibility. Computer-assisted quantitative assessment is needed, but the task is challenging due to image nonuniformity (breast coils cause loss of MR signal in remote regions) and to the anatomical complexity of chest wall boundary (Fig. 2). (Figure Presented) Given its accuracy and speed, our breast segmentation method appears to be ready for clinical use as a part of larger workflow to generate routine diagnostic reports