International Interobserver Variability of Breast Density Assessment.

PURPOSE: The aim of this study was to determine variability in visually assessed mammographic breast density categorization among radiologists practicing in Indonesia, the Netherlands, South Africa, and the United States. METHODS: Two hundred consecutive 2-D full-field digital screening mammograms obtained from September to December 2017 were selected and retrospectively reviewed from four global locations, for a total of 800 mammograms. Three breast radiologists in each location (team) provided consensus density assessments of all 800 mammograms using BI-RADS® density categorization. Interreader agreement was compared using Gwet's AC2 with quadratic weighting across all four density categories and Gwet's AC1 for binary comparison of combined not dense versus dense categories. Variability of distribution among teams was calculated using the Stuart-Maxwell test of marginal homogeneity across all four categories and using the McNemar test for not dense versus dense categories. To compare readers from a particular country on their own 200 mammograms versus the other three teams, density distribution was calculated using conditional logistic regression. RESULTS: For all 800 mammograms, interreader weighted agreement for distribution among four density categories was 0.86 (Gwet's AC2 with quadratic weighting; 95% confidence interval, 0.85-0.88), and for not dense versus dense categories, it was 0.66 (Gwet's AC1; 95% confidence interval, 0.63-0.70). Density distribution across four density categories was significantly different when teams were compared with one another and one team versus the other three teams combined (P < .001). Overall, all readers placed the largest number of mammograms in the scattered and heterogeneous categories. CONCLUSIONS: Although reader teams from four different global locations had almost perfect interreader agreement in BI-RADS density categorization, variability in density distribution across four categories remained statistically significant.

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI.

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types.

Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss' Kappa score between-observers ranged from 0.31-0.50 and 0.55-0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61-0.66 and 0.70-0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.

Responsive Web-based Breast Imaging Core Curriculum for International Radiology Residents with Self-Assessment: A Pilot Study.

RATIONALE AND OBJECTIVES: Lack of uniformity in radiology resident education is partially attributable to variable access to subspecialty education. Web-based courses improve standardization, but with growing emphasis on competency based education, more evaluation of their effectiveness is needed. We created a responsive web-based breast imaging curriculum for radiology residents including self-assessment and a satisfaction survey. MATERIALS AND METHODS: Two global academic institutions collaboratively developed a breast imaging curriculum to address radiology residents' educational needs. This virtual course comprised 11 video lectures, nine didactic (with attached pre-test and post-test assessments) and two case review sessions. In April 2020, this optional curriculum was made available to all 56 radiology residents in one residency program cluster in Singapore, to be accessed alongside the breast imaging rotation as a supplement. A voluntary anonymous satisfaction survey was provided upon completion. RESULTS: A total of 39 of the 56 radiology residents (70%) completed the course. For the average score of nine lectures (maximum score 5), there was a significant increase in mean pre and post - test scores (mean = 2.2, SD = 0.7), p < 0.001. The proportion of residents with improvement between the pre-test score and the post-test score ranged from 74% to 100% (mean, 84%). Thirty three of the 39 participants (85%) completed the satisfaction survey, and all agreed or strongly agreed that the curriculum increased their knowledge of breast imaging. CONCLUSION: This web based breast imaging curriculum supplement was viewed positively by participating residents and improved their self-assessed knowledge. Curriculum access could be expanded to improve global radiology education.

The first BGICC consensus and recommendations for breast cancer awareness, early detection and risk reduction in low- and middle-income countries and the MENA region.

In low-middle income countries (LMICs) and the Middle East and North Africa (MENA) region, there is an unmet need to establish and improve breast cancer (BC) awareness, early diagnosis and risk reduction programs. During the 12th Breast, Gynecological & Immuno-oncology International Cancer Conference - Egypt 2020, 26 experts from 7 countries worldwide voted to establish the first consensus for BC awareness, early detection and risk reduction in LMICs/MENA region. The panel advised that there is an extreme necessity for a well-developed BC data registries and prospective clinical studies that address alternative modalities/modified BC screening programs in areas of limited resources. The most important recommendations of the panel were: (a) BC awareness campaigns should be promoted to public and all adult age groups; (b) early detection programs should combine geographically distributed mammographic facilities with clinical breast examination (CBE); (c) breast awareness should be encouraged; and (d) intensive surveillance and chemoprevention strategies should be fostered for high-risk women. The panel defined some areas for future clinical research, which included the role of CBE and breast self-examination as an alternative to radiological screening in areas of limited resources, the interval and methodology of BC surveillance in women with increased risk of BC and the use of low dose tamoxifen in BC risk reduction. In LMICs/MENA region, BC awareness and early detection campaigns should take into consideration the specific disease criteria and the socioeconomic status of the target population. The statements with no consensus reached should serve as potential catalyst for future clinical research.

Concerns about the economics of mammography and how radiologists can respond.

Rapidly increasing U.S. healthcare spending has been a hot topic over the past few decades. Imaging studies, including screening mammography, are possible targets for cost savings. Radiologists need to be more proactive and take charge by actively participating in the cost reduction conversation, improving the quality of care, providing patients with accurate cost estimates and educating patients along with clinicians on the value we have provided and can provide in the future.

Digital Breast Tomosynthesis for Intraoperative Margin Assessment during Breast-Conserving Surgery.

BACKGROUND: Intraoperative margin assessment for breast cancer patients undergoing segmental mastectomy (SM) enables identification of positive margins, with immediate excision of additional tissue to obtain negative margins. OBJECTIVE: The aim of this study was to determine the ability of digital breast tomosynthesis (DBT) to detect positive margins compared with an institution's standard extensive processing (SEP). METHODS: SM specimens underwent intraoperative SEP with two-dimensional (2D) imaging of the intact and sliced specimen, with review by a breast radiologist and gross assessment by a breast pathologist. Findings guided the surgeon to excise additional tissue. DBT images of intact specimens were prospectively obtained and retrospectively reviewed by a breast radiologist. A positive margin was defined as tumor at ink. RESULTS: Ninety-eight patients underwent 99 SMs. With SEP, 14 (14%) SM specimens had 19 positive margins. SEP did not detect 3 of the 19 positive margins, for a sensitivity of 84%, specificity of 78%, positive predictive value (PPV) of 11%, and negative predictive value (NPV) of 99%. Moreover, DBT did not detect 5 of the 19 positive margins, for a sensitivity of 74% (p > 0.05), specificity of 91% (p < 0.05), PPV of 21.5%, and NPV of 99%. With SEP guidance to excise additional tissue, six cases had final positive margins, with SEP not identifying three of these cases and DBT not identifying two. Pathology from the second surgery of these patients showed either no additional malignancy or only focal ductal carcinoma in situ. CONCLUSIONS: DBT is an accurate method for detecting positive margins in breast cancer patients undergoing SM, performing similar to institutional labor-intensive, intraoperative standard processing.

Impact of Artificial Intelligence on Women's Imaging: Cost-Benefit Analysis.

OBJECTIVE. The purpose of this article is to identify and discuss four areas in which artificial intelligence (AI) must excel to become clinically viable: performance, time, work flow, and cost. CONCLUSION. AI holds tremendous potential for transforming the practice of radiology, but certain metrics are needed to objectively quantify its impact. As patients, physicians, hospitals, and insurance companies look for value, AI must earn a role in medical imaging.

Biopsy Feasibility Trial for Breast Cancer Pathologic Complete Response Detection after Neoadjuvant Chemotherapy: Imaging Assessment and Correlation Endpoints.

PURPOSE: This study was designed to present the secondary imaging endpoints of the trial for evaluating mammogram (MMG), ultrasound (US) and image guided biopsy (IGBx) assessment of pathologic complete response (pCR) in breast cancer (BC) patients undergoing neoadjuvant chemotherapy (NAC). METHODS: Patients with T1-3, N0-3, M0 triple-negative or HER2-positive BC who received NAC were enrolled in an Institutional Review Board-approved prospective, clinical trial. Patients underwent US and MMG at baseline and after NAC. Images were evaluated for residual abnormality and to determine modality for IGBx [US-guided (USG) or stereotactic guided (SG)]. Fine-needle aspiration and 9-G, vacuum-assisted core biopsy (VACBx) of tumor bed was performed after NAC and was compared with histopathology at surgery. RESULTS: Forty patients were enrolled. Median age was 50.5 (range 26-76) years; median baseline tumor size was 2.4 cm (range 0.8-6.3) and 1 cm (range 0-5.5) after NAC. Nineteen patients had pCR: 6 (32%) had residual Ca2+ presurgery, 5 (26%) residual mass, 1 (5%) mass with calcifications, and 7 (37%) no residual imaging abnormality. Sensitivity, specificity, and accuracy of US, MMG, and IGBx for pCR were 47/95/73%, 53/90/73%, and 100/95/98%, respectively. Twenty-five (63%) patients had SGBx and 15 (37%) had US-guided biopsy (USGBx). Median number of cores was higher with SGBx (12, range 6-14) than with USGBx (8, range 4-12), p < 0.002. Positive predictive value for pCR was significantly higher for SG VACBx than for USG VACBx (100 vs. 60%, p < 0.05). CONCLUSIONS: SG VACBx is the preferred IGBx modality for identifying patients with pCR for trials testing the safety of eliminating surgery.

The Economic and Social Value of an Image Exchange Network: A Case for the Cloud.

As the health care environment continually changes, radiologists look to the ACR's Imaging 3.0® initiative to guide the search for value. By leveraging new technology, a cloud-based image exchange network could provide secure universal access to prior images, which were previously siloed, to facilitate accurate interpretation, improved outcomes, and reduced costs. The breast imaging department represents a viable starting point given the robust data supporting the benefit of access to prior imaging studies, existing infrastructure for image sharing, and the current workflow reliance on prior images. This concept is scalable not only to the remainder of the radiology department but also to the broader medical record.

Impact of Breast Density Legislation on Breast Cancer Risk Assessment and Supplemental Screening: A Survey of 110 Radiology Facilities.

Breast density notification laws, passed in 19 states as of October 2014, mandate that patients be informed of their breast density. The purpose of this study is to assess the impact of this legislation on radiology practices, including performance of breast cancer risk assessment and supplemental screening studies. A 20-question anonymous web-based survey was emailed to radiologists in the Society of Breast Imaging between August 2013 and March 2014. Statistical analysis was performed using Fisher's exact test. Around 121 radiologists from 110 facilities in 34 USA states and 1 Canadian site responded. About 50% (55/110) of facilities had breast density legislation, 36% of facilities (39/109) performed breast cancer risk assessment (one facility did not respond). Risk assessment was performed as a new task in response to density legislation in 40% (6/15) of facilities in states with notification laws. However, there was no significant difference in performing risk assessment between facilities in states with a law and those without (p < 0.831). In anticipation of breast density legislation, 33% (16/48), 6% (3/48), and 6% (3/48) of facilities in states with laws implemented handheld whole breast ultrasound (WBUS), automated WBUS, and tomosynthesis, respectively. The ratio of facilities offering handheld WBUS was significantly higher in states with a law than in states without (p < 0.001). In response to breast density legislation, more than 33% of facilities are offering supplemental screening with WBUS and tomosynthesis, and many are performing formal risk assessment for determining patient management.

The probably benign assessment.

The probably benign assessment (category 3 in the Breast Imaging Reporting and Data System) is associated with a less than 2% probability of malignancy. Its use in mammography is well supported by robust data from various large-scale prospective studies. Use of the probably benign assessment for lesions visible only at ultrasound or MR imaging is much less well established. This article examines in depth the use of the probably benign assessment: which lesions should be assessed as probably benign, the published evidence supporting such use, pitfalls in misuse, and areas of potentially expanded use that currently are under investigation.