Journal of Breast Imaging最新文献

For the breast imaging radiologist, developing a career as a clinician-educator can be accomplished in a number of ways. Whether it be a new graduate or perhaps a radiologist making a midcareer or late-career pivot to the academic world, there are several opportunities and resources that can support a faculty member at any stage in this journey. In this article, the breast imaging radiologist will learn a variety of methods to strengthen their professional identity and career path as a clinician-educator through the early-, mid-, and late-career professional journey.

We used focus groups of radiologists who led the implementation of contrast-enhanced mammography (CEM) in their practice to identify barriers and strategies for adoption. Members of the Society of Breast Imaging in the United States who served as lead on CEM implementation were invited to participate in 2 separate focus groups. Ten breast imaging radiologists with varied geographic and practice type (60% academic, 30% private, and 10% community practice) participated. There were 4 major themes identified: patient selection, workflow, contrast, and billing. Patient selection varied widely among practices, with some limiting CEM to patients unable to obtain MRI and others routinely using CEM for diagnostic workup. Lack of Food and Drug Administration approval limited screening applications in some practices. Workflow challenges were numerous, and site-specific solutions were developed for ordering, scheduling, staffing, and intravenous access. There were universal concerns regarding contrast, including safe administration, response to reactions, and biopsy planning for findings only visible on CEM. Contrast reaction training, including conducting mock codes at some practices, helped alleviate concerns of the radiologists and technologists. Finally, billing was an administrative hurdle that influenced patient selection. Ample preparation is needed to successfully start a CEM program with particular attention to patient selection, workflow, contrast administration/reactions, and billing.

Objective: Peri-implant enhancement can be seen on contrast-enhanced breast MRI, but its association with malignancy has not been described, leading to considerable variability in assessment and recommendations by radiologists. This study evaluated imaging features, management, and outcomes of implant-related enhancement.

Methods: This multisite IRB-approved retrospective review queried all breast MRI reports for keywords describing peri-implant enhancement, fluid, and/or masses (plus synonymous descriptions) and implant-associated malignancies, with subsequent imaging and chart review. Peri-implant enhancement and implant features were characterized. Assessments and outcomes were evaluated via clinical and imaging follow-up, aspiration/biopsy, and/or capsulectomy to evaluate for association of peri-implant enhancement with implant-related malignancy.

Results: A total of 100 patients had peri-implant enhancement. Uniform thin peripheral enhancement was most common (79/100, 79%). Capsulectomy was performed in 31/100 (31%), with benign capsular fibrosis/inflammation discovered in 26/31 (83.9%). Breast implant-associated anaplastic large cell lymphoma was present in 2/100 (2%), both with textured implants, while 98/100 (98%) had no implant-related malignancy. MRI recommendations varied: resume routine imaging (26/100, 26%), clinical management (18/100, 18%), follow-up MRI (17/100, 17%), MRI-directed US (17/100, 17%), aspiration/biopsy (11/100, 11%), and surgical consultation (10/100, 10%).

Conclusion: Peri-implant enhancement is a nonspecific imaging finding with a low malignant association, especially when seen in isolation (no associated effusion, mass, or adenopathy). Implant surface texture should be considered in management recommendations; diagnostic capsulectomy is not recommended in patients with smooth implants. Additional studies are encouraged to validate nonoperative management recommendations.

Radiologists face a range of challenges to maximize the life-saving benefits of screening mammography, including pressure to maintain accuracy, manage heavy workloads, and minimize the risk of fatigue and burnout. This review provides targeted strategies to address these challenges and, ultimately, to improve interpretive performance of screening mammography. Workflow optimizations, including offline vs online and batched vs nonbatched interpretation, interrupted vs uninterrupted reading, and the importance of comparing current mammograms with prior examinations will be explored. Each strategy has strengths, weaknesses, and logistical challenges that must be tailored to the individual practice environment. Moreover, as breast radiologists contend with increasingly busy and hectic working conditions, practical solutions to protect reading environments and minimize distractions, such as the "sterile cockpit" approach, will be described. Additionally, breast radiologists are at greater risk for fatigue and burnout due to rising clinic volumes and an inadequate workforce. Optimizing the approach to reading screens is critical to helping breast imaging radiologists maintain and maximize the benefits of screening mammography, ensure the best outcomes for our patients, and maintain radiologist job satisfaction.

Objective: To assess the positive predictive value-3 (PPV3) and negative predictive value (NPV) of contrast-enhanced mammography (CEM) when added to the diagnostic workup of suspicious breast findings.

Methods: This prospective study was IRB approved. We recruited 99 women with abnormal findings on digital breast tomosynthesis (DBT) and/or US to undergo CEM prior to biopsy. Based on final pathology outcomes, PPV3 and NPV were calculated and compared using N-1 chi-squared tests with P-values and 95% CIs.

Results: Final pathologic outcome yielded 56.6% (56/99) benign, 5.1% (5/99) benign with upgrade potential (BWUP), and 38.4% (38/99) malignant lesions. Final pathologic outcomes for the 63 positive CEMs yielded 33.3% (21/63) benign, 6.3% (4/63) BWUP, and 60.3% (38/63) malignant lesions. Adding CEM to the diagnostic workup significantly increased PPV3 from 38.4% (38/99) to 60.3% (38/63) (P <.01; 95% CI, 6.1-36.2). Negative predictive value was 100% (36/36) for CEM, 92.9% (13/14; P = .1; 95% CI, -4.2 to 31.4) for DBT, and 75.9% (22/29; P <.05; 95% CI, 8.8-42.1) for US. The number of unnecessary biopsies could be reduced by 36.4% (from 100% [99/99] to 63.6% [63/99]).

Conclusion: Adding CEM to the diagnostic workup of suspicious breast findings could improve PPV3 to prevent unnecessary biopsies.

Triple-negative breast cancers (TNBCs) are invasive carcinomas that lack ER and PR expression and also lack amplification or overexpression of HER2. Triple-negative breast cancers are histopathologically diverse, with the majority classified as invasive breast carcinomas of no special type with a basal-like profile. Triple-negative breast cancer is the most aggressive molecular subtype of invasive breast carcinoma, with the highest rates of stage-matched mortality and regional recurrence. Triple-negative breast cancer has a younger median age of diagnosis than other molecular subtypes and is disproportionately diagnosed in Black women and BRCA1 germline pathogenic mutation carriers. On US and mammography, TNBCs are most often seen as a noncircumscribed mass without calcifications; TNBCs can have circumscribed margins and mimic a cyst or have probably benign features that may result in delayed diagnosis. MRI is the most sensitive modality for detecting TNBC, with rim enhancement being a common feature, and MRI is also the most accurate imaging for assessing neoadjuvant chemotherapy response. Understanding the radiologic and pathologic findings of TNBC can aid in diagnosis.

Objective: Assess current practices and plans regarding home workstations and remote diagnostic breast imaging in the United States.

Methods: A 43-question survey relating to remote breast imaging was distributed to Society of Breast Imaging members from July 6, 2023, through August 2, 2023. A descriptive summary of responses was performed. Pearson's chi-squared test was used to compare demographic variables of respondents and questions of interest.

Results: In total, 424 surveys were completed (response rate 13%, 424/3244). One-third of breast imaging radiologists (31%, 132/424) reported reading examinations from home or a personal remote site for a median of 25% of their clinical time. The most common types of examinations read from home were screening mammography (90%, 119/132), screening US (58%, 77/132), diagnostic mammography and MRI (both 53%, 70/132), and diagnostic US (49%, 65/132). Respondents from private practices were more likely than those from academic practices to read diagnostic imaging from home (67%, 35/52 vs 29%, 15/52; P <.001). Respondents practicing in the West were less likely to read breast imaging examinations from home compared with those in other geographic regions (18%, 12/67 vs 28%-43% for other regions; P = .023). No differences were found among respondents' overall use of home workstations based on age, gender, or having dependents. Most respondents (75%, 318/424) felt that remote breast reading would be a significant practice pattern in the future.

Conclusion: Home workstations for mammography and remote diagnostic breast imaging are a considerable U.S. practice pattern. Further research should explore radiologist preferences regarding remote breast imaging and its impact on clinical care and radiologist well-being.

Identifying systemic disease with medical imaging studies may improve population health outcomes. Although the pathogenesis of peripheral arterial calcification and coronary artery calcification differ, breast arterial calcification (BAC) on mammography is associated with cardiovascular disease (CVD), a leading cause of death in women. While professional society guidelines on the reporting or management of BAC have not yet been established, and assessment and quantification methods are not yet standardized, the value of reporting BAC is being considered internationally as a possible indicator of subclinical CVD. Furthermore, artificial intelligence (AI) models are being developed to identify and quantify BAC on mammography, as well as to predict the risk of CVD. This review outlines studies evaluating the association of BAC and CVD, introduces the role of preventative cardiology in clinical management, discusses reasons to consider reporting BAC, acknowledges current knowledge gaps and barriers to assessing and reporting calcifications, and provides examples of how AI can be utilized to measure BAC and contribute to cardiovascular risk assessment. Ultimately, reporting BAC on mammography might facilitate earlier mitigation of cardiovascular risk factors in asymptomatic women.