ACR Appropriateness Criteria® Female Breast Cancer Screening: 2023 Update.

Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer. Numerous factors contribute to breast cancer risk, which is frequently divided into three major categories: average, intermediate, and high risk. For patients assigned female at birth with native breast tissue, mammography and digital breast tomosynthesis are the recommended method for breast cancer screening in all risk categories. In addition to the recommendation of mammography and digital breast tomosynthesis in high-risk patients, screening with breast MRI is recommended. 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 process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation.

Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape, and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success in biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting small breast tumors. In this paper, we propose a novel deep neural network architecture, namely the Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. The Enhanced Small Tumor-Aware Network introduces two encoders to extract and fuse image context information at different scales, and utilizes row-column-wise kernels to adapt to the breast anatomy. We compare ESTAN and nine state-of-the-art approaches using seven quantitative metrics on three public breast ultrasound datasets, i.e., BUSIS, Dataset B, and BUSI. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on small tumor segmentation. Specifically, the Dice similarity coefficient (DSC) of ESTAN on the three datasets is 0.92, 0.82, and 0.78, respectively; and the DSC of ESTAN on the three datasets of small tumors is 0.89, 0.80, and 0.81, respectively.

Autologous Fat Grafting to the Breast: An Educational Review.

Autologous fat grafting (AFG) is a technique that is increasingly utilized in breast cosmetic and reconstructive surgery. In this procedure, fat is aspirated by liposuction from one area of the body and injected into the breast. The procedure and process of AFG has evolved over the last few decades, leading to more widespread use, though there is no standard method. Autologous fat grafting is generally considered a safe procedure but may result in higher utilization of diagnostic imaging due to development of palpable lumps related to fat necrosis. Imaging findings depend on surgical technique but typically include bilateral, symmetric, retromammary oil cysts and scattered dystrophic and/or coarse calcifications when AFG is used for primary breast augmentation. More focal findings occur when AFG is used to improve specific areas of cosmetic deformity, scarring, or pain following breast cancer surgery. As with any cause of fat necrosis, imaging features tend to appear more benign over time, with development of rim calcifications associated with oil cysts and a shift in echogenicity of oil cyst contents on ultrasound towards anechoic in some cases. This article reviews the AFG procedure, uses, complications, and imaging findings.

The Impact of the COVID-19 Pandemic on Breast Imaging.

"Starting in Wuhan, China, followed quickly in the United States in January 2020, an outbreak of a novel coronavirus, or COVID-19, escalated to a global pandemic by March. Significant disruptions occurred to breast imaging, including deferred screening mammography, triaging diagnostic breast imaging, and changes in breast cancer care algorithms. This article summarizes the effect of the global pandemic-and efforts to curtail its spread-on both breast cancer care and on breast imaging practices including effects on patients, clinical workflow, education, and research."

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach to integrating visual saliency into a deep learning model for breast tumor segmentation in ultrasound images. Visual saliency refers to image maps containing regions that are more likely to attract radiologists' visual attention. The proposed approach introduces attention blocks into a U-Net architecture and learns feature representations that prioritize spatial regions with high saliency levels. The validation results indicate increased accuracy for tumor segmentation relative to models without salient attention layers. The approach achieved a Dice similarity coefficient (DSC) of 90.5% on a data set of 510 images. The salient attention model has the potential to enhance accuracy and robustness in processing medical images of other organs, by providing a means to incorporate task-specific knowledge into deep learning architectures.

Summary measures of agreement and association between many raters' ordinal classifications.

PURPOSE: Interpretation of screening tests such as mammograms usually require a radiologist's subjective visual assessment of images, often resulting in substantial discrepancies between radiologists' classifications of subjects' test results. In clinical screening studies to assess the strength of agreement between experts, multiple raters are often recruited to assess subjects' test results using an ordinal classification scale. However, using traditional measures of agreement in some studies is challenging because of the presence of many raters, the use of an ordinal classification scale, and unbalanced data. METHODS: We assess and compare the performances of existing measures of agreement and association as well as a newly developed model-based measure of agreement to three large-scale clinical screening studies involving many raters' ordinal classifications. We also conduct a simulation study to demonstrate the key properties of the summary measures. RESULTS: The assessment of agreement and association varied according to the choice of summary measure. Some measures were influenced by the underlying prevalence of disease and raters' marginal distributions and/or were limited in use to balanced data sets where every rater classifies every subject. Our simulation study indicated that popular measures of agreement and association are prone to underlying disease prevalence. CONCLUSIONS: Model-based measures provide a flexible approach for calculating agreement and association and are robust to missing and unbalanced data as well as the underlying disease prevalence.

Screening for Breast Cancer.

The goal of screening is to detect breast cancers when still curable to decrease breast cancer-specific mortality. Breast cancer screening in the United States is routinely performed with mammography, supplemental digital breast tomosynthesis, ultrasound, and/or MR imaging. This article aims to review the most commonly used breast imaging modalities for screening, discuss how often and when to begin screening with specific imaging modalities, and examine the pros and cons of screening. By the article's end, the reader will be better equipped to have informed discussions with patients and medical professionals regarding the benefits and disadvantages of breast cancer screening.

Clinical implementation of synthesized mammography with digital breast tomosynthesis in a routine clinical practice.

BACKGROUND: Most published studies evaluating digital breast tomosynthesis (DBT) included a separate 2-dimensional full-field digital mammogram (FFDM) for DBT screening protocols, increasing radiation from screening mammography. Synthesized mammography (SM) creates a 2-dimensional image from the DBT source data, and if used in place of FFDM, it reduces radiation of DBT screening. This study evaluated the implementation of SM + DBT in routine screening practice in terms of recall rates, cancer detection rates (CDR), % of minimal cancers, % of node-positive cancers, and positive predictive values (PPV). MATERIALS AND METHODS: A multivariate retrospective institutional analysis was performed on 31,979 women who obtained screening mammography (10/2013-12/2015) with cohorts divided by modality (SM + DBT, FFDM + DBT, and FFDM). We adjusted for comparison mammograms, age, breast density, and the interpreting radiologist. Recall type was analyzed for differences (focal asymmetry, asymmetry, masses, calcifications, architectural distortion). RESULTS: SM + DBT significantly decreased the recall rate compared to FFDM (5.52 vs. 7.83%, p < 0.001) with no differences in overall CDR (p = 0.66), invasive and/or in situ CDR, or percentages of minimal and node-negative cancers. PPV1 significantly increased with SM + DBT relative to FFDM (9.1 vs. 6.2%, p = 0.02). SM + DBT did not differ significantly in recall rate or overall CDR compared to FFDM + DBT. There were statistically significant differences in certain findings recalled by screening modality (e.g., focal asymmetries). CONCLUSIONS: SM + DBT reduces false positives compared to FFDM, while maintaining the CDR and other desirable audit outcome data. SM + DBT is more accurate than FFDM alone, and is a desirable alternative to FFDM + DBT, given the added benefit of radiation reduction.

Synthesized Digital Mammography Imaging.

Synthesized mammography (SM) is a new imaging technique similar to digital mammography constructed from an acquired digital breast tomosynthesis (DBT) examination. SM allows for widespread screening using DBT, maintaining the benefits of DBT while decreasing the radiation of DBT by nearly half. This article reviews studies evaluating SM, most of which suggest that SM may be appropriate to use clinically to replace an actual acquired conventional 2-dimensional full-field digital mammogram (FFDM) when using DBT for breast cancer screening. These results should be interpreted with caution because there are inherent differences between SM and FFDM image quality and lesion visibility and larger, more robust studies still need to be performed.

Spectrum of Extramammary Malignant Neoplasms in the Breast With Radiologic-Pathologic Correlation.

Although primary breast cancer is the most common malignancy identified by breast imaging, extramammary malignancies may also rarely be encountered. These uncommon lesions may reflect primary neoplasms of nonmammary origin as well as secondary metastatic lesions, and include lymphoma, melanoma, neuroendocrine tumors, gastrointestinal tract malignancies, and angiosarcoma among other entities. Malignant extramammary breast lesions may be encountered during routine mammographic screening, identified during the diagnostic evaluation of a palpable breast abnormality, or may be detected incidentally during imaging of other organs of interest. As such, the radiologist should have familiarity with the appearance of these lesions. This article focuses on a review of several of the most common extramammary metastases to the breast, as well as a few lesions that may develop as either primary or secondary lesions.

False-negative rate of combined mammography and ultrasound for women with palpable breast masses.

Mammography and ultrasound are often used concurrently for patients with palpable breast masses. While mammography has a false-negative rate of approximately 15 %, the addition of breast ultrasound decreases this rate among patients with palpable breast masses. There are no recent outcome data regarding the use of combined reporting of ultrasound and mammography (CRUM) for palpable breast masses. In this study, female patients presenting with a palpable breast mass were retrospectively reviewed in a prospectively entered database at a single institution from June 2010 to July 2013. All cancer cases and false-negative cases using CRUM were identified. Cancer rates, false-negative rates, and negative predictive values were calculated based on CRUM breast imaging-reporting and data system (BI-RADS) categories. One thousand two hundreds and twelve female patients presenting with a palpable breast mass were identified; 77 % of patients had CRUM and 73 % (682/932) were BI-RADS 1-2. Despite negative or benign BI-RADS, 9.5 % of patients with BI-RADS 1-2 (65/682) underwent biopsy, compared to 96 % of patients with a BI-RADS 4-5 designation. Eighty-one patients were found to have cancers; 2 had BI-RADS 1-2 imaging. The false-negative rate of CRUM was 2.4 % (2/81). Since 69 % (428/617) of BI-RADS 1-2 patients without tissue diagnosis had follow-up imaging and/or clinical exam (median: 27 months, range: 2-62 months) and none developed cancers, the cancer rate and negative predictive value of a palpable breast mass of BI-RADS 1-2 were estimated to be 0.3 % (2/682) and 99.7 %, respectively. In the modern era of combined imaging for breast masses, a patient with a low suspicion exam can be reassured with a negative CRUM report.

Breast cancer screening in the era of density notification legislation: summary of 2014 Massachusetts experience and suggestion of an evidence-based management algorithm by multi-disciplinary expert panel.

Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review, the Cochrane review, National Comprehensive Cancer Network guidelines, American Cancer Society recommendations, and American College of Radiology appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (<15% lifetime risk), do not routinely require supplemental screening per the expert consensus. Women of high risk (>20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman.

Mammographic breast density: impact on breast cancer risk and implications for screening.

Mammographic breast density is rapidly becoming a hot topic in both the medical literature and the lay press. In the United States, recent legislative changes in 19 states now require radiologists to notify patients regarding breast density as well as the possible need for supplemental screening. Federal legislation regarding breast density notification has been introduced, and its passage is likely on the horizon. An understanding of the context, scientific evidence, and controversies surrounding the topic of breast density as a risk factor for breast cancer is critical for radiologists. The current state of evidence is presented regarding supplemental screening for women with dense breasts, including the use of digital breast tomosynthesis, whole-breast ultrasonography, and gadolinium-enhanced magnetic resonance imaging. A review of current practice guidelines and additional sources of information will improve radiologists' understanding of the relevant subject of breast density and enable them to respond appropriately to questions from patients, clinicians, and the media.

Preoperative Tomosynthesis-guided Needle Localization of Mammographically and Sonographically Occult Breast Lesions.

PURPOSE: To assess the feasibility and accuracy of digital breast tomosynthesis (DBT)-guided needle localization for DBT-detected suspicious abnormalities not visualized with other modalities and to analyze the imaging and pathologic characteristics of abnormalities detected only with DBT to determine the positive predictive value for malignancy. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board, and the requirement to obtain informed consent was waived. A retrospective query of the imaging database identified 34 consecutive women (average age, 55 years; age range, 28-84 years) with 36 lesions who underwent DBT-guided needle localization between April 2011 and January 2013 with use of commercially available equipment. Imaging findings and medical records were reviewed. Findings that were attributable to previous surgical changes were classified as benign or probably benign and excluded from analysis because the lesions did not proceed to localization. RESULTS: Architectural distortion was the imaging finding identified in all 36 abnormalities (100%). Findings from pathologic examination after the first attempt at localization were concordant with those from imaging in 35 of the 36 lesions (97%), which is suggestive of appropriate sampling. Histologic findings were malignant in 17 of the 36 lesions (47%; 95% confidence interval: 30.4%, 64.5%). Thirteen of the 17 lesions (76%; 95% confidence interval: 50.1%, 93.1%) were invasive malignancies. Twenty-two of the 36 abnormalities (61%) were either malignant or high-risk lesions (atypical ductal hyperplasia, lobular carcinoma in situ, atypical lobular hyperplasia). CONCLUSION: DBT-guided needle localization is an accurate and feasible method with which to biopsy DBT-detected suspicious architectural distortions not visualized at mammography or sonography. The high risk of malignancy in abnormalities detected only with DBT (47%) confirms that routine biopsy is required for histologic analysis.

To do or not to do: axillary nodal evaluation after ACOSOG Z0011 Trial.

Methods of axillary evaluation in invasive breast cancer continue to evolve. The recent American College of Surgeons Oncology Group Z0011 Trial is a prospective, randomized, multicenter trial that compared the survival and locoregional recurrence rates after complete axillary lymph node dissection (ALND) versus sentinel node biopsy (SNB) alone in women with a positive sentinel node in an effort to avoid the complications associated with ALND. As the results of this trial are implemented clinically, affecting surgical management of axillary metastatic disease, radiologists may need to redefine their role in the preoperative assessment of the axilla. Before the Z0011 trial, breast imagers worked to identify axillary metastases preoperatively, allowing appropriate patients to proceed directly to ALND and avoiding the need for SNB. However, the Z0011 trial concluded that ALND may not be necessary in women with metastatic axillary disease who meet the trial criteria. In the Z0011 trial, after 6 years of median follow-up there was no difference in either locoregional recurrence or survival among the women who underwent SNB alone compared with those who underwent ALND, suggesting that ALND is unnecessary in a subset of women with a positive node at SNB. These results raise questions about how aggressively radiologists should pursue percutaneous sampling of axillary nodes, as some practitioners conclude that, in an otherwise eligible woman, positive results from imaging-guided percutaneous biopsy preclude a Z0011 trial-directed pathway. Debate about the best way to implement the results of the Z0011 trial into daily clinical practice exists. It is important for breast imagers to work closely with breast surgeons to provide the most appropriate treatment course for each patient.

Surgical excision of radial scars diagnosed by core biopsy may help predict future risk of breast cancer.

Radial scars (RS's) are benign breast lesions known to be associated with carcinomas and other high-risk lesions (HRL's). The upgrade rate to carcinoma after core biopsy revealing RS is 0-40 %. We sought to determine the outcomes of RS with and without HRL diagnosed by core biopsy. Patients who underwent core biopsy revealing RS without carcinoma at our institution between 1/1996 and 11/2012 were identified from a surgical pathology database. Retrospective chart review was utilized to classify patients as RS-no HRL or RS-HRL. HRL was defined as ADH, LCIS, and/or ALH. We determined upgrade rate to carcinoma at surgical excision, and upgrade to HRL for RS-no HRL patients. Univariate analysis was performed to identify risk factors for upgrade in RS-no HRL patients. 156 patients underwent core biopsy revealing RS, 131 RS-no HRL (84 %), and 25 RS-HRL (16 %). The overall rate of upgrade to invasive carcinoma was 0.8 % (1/124). 1.0 % (1/102) of RS-no HRL and 13.6 % (3/22) of RS-HRL patients were upgraded to DCIS (P = 0.0023). The upgrade of RS-no HRL to HRL at excision was 21.6 % (22/102). By univariate analysis, RS-no HRL with radiologic appearance of a mass/architectural distortion had a significantly higher rate of upgrade to HRL or carcinoma compared with calcifications (P = 0.03). Excision of RS to rule out associated invasive carcinoma is not warranted, given a <1 % rate of upgrade at excision. However, excision to evaluate for non-invasive cancer or HRL may be considered to help guide clinical decision-making about use of chemoprevention.

Digital breast tomosynthesis in the analysis of fat-containing lesions.

Digital breast tomosynthesis (DBT) is rapidly emerging as an important clinical tool for both screening and diagnosis. DBT improves upon mammography by depicting breast tissue on a dynamic sequence of cross-sectional images reconstructed in planes corresponding to their mammographic planes of acquisition. DBT results in markedly reduced summation of overlapping tissue and depicts the margins of masses in far greater detail than mammography. Fat is commonly recognized in both benign and malignant breast masses at DBT, even when no fat is appreciated at mammography. In cases of encapsulated fat-containing masses, the increased detail at DBT often allows the radiologist to definitively classify a mass as benign (eg, lipoma, hamartoma, galactocele, lipid cyst) when mammographic findings alone are equivocal, thereby avoiding unnecessary biopsy or workup. However, when learning to read DBT images, many radiologists misinterpret this rule, mistaking the presence of any fat within a mass for an indication of benignity or an artifact and falsely concluding that an otherwise suspicious mass is not worrisome. If fat seen in breast masses at DBT is not appropriately analyzed, malignant breast masses may be incorrectly classified as probably or even definitely benign. With use of radiologic-pathologic correlation, the authors illustrate cases in which the presence of fat can help correctly classify a mass as benign, and pitfalls in which the presence or absence of fat within a mass is irrelevant and should not influence analysis.