Journal of Breast Imaging最新文献

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.

Objective: To determine the upgrade rate of exclusively MRI-detected benign papillomas in asymptomatic high-risk patients, patients with a history of cancer, or patients with known malignancy.

Methods: This IRB-approved retrospective study reviewed all breast MRI-guided biopsies yielding papilloma on pathology for all asymptomatic patients undergoing breast MRI for high-risk screening, newly diagnosed breast cancer, or a personal history of breast cancer. All cases were followed by excision or 2-year imaging follow-up. The upgrade rate was determined. Fisher's exact test was used to determine the significance of associated factors, including lesion type, lesion size, and ipsilateral malignancy.

Results: Of the 258 MRI-guided biopsies, 117 met the inclusion criteria. A 4% (5/117) upgrade rate was found with a 3% (4/117) upgrade rate to ductal carcinoma in situ (DCIS) and a 1% (1/117) upgrade rate to invasive malignancy for all identified papillomas. When evaluating all papillomas, the only associated feature identified to be statically significant for risk of upgrade was ipsilateral malignancy with a 60% (3/5) upgrade rate with a P-value of .0057. When separately evaluating benign papillomas only by excluding those with atypia or additional high-risk lesion at biopsy, a 4% (3/80) upgrade rate to DCIS was found. There was no upgrade to invasive malignancy.

Conclusion: Upgrade of MRI-detected papillomas in asymptomatic high-risk patients, patients with a history of cancer, or patients with known malignancy is 4% in this population, which suggests these lesions may warrant surgical excision.

Cystic breast masses are a common entity encountered by breast radiologists. The imaging features of benign and malignant cystic masses may overlap, causing confusion and miscategorization with the potential to produce diagnostic delay and harm. This article provides a review of key differentiating imaging features that help guide appropriate mass characterization and treatment.

Objective: Artificial intelligence (AI) has been shown to hold promise for improving breast cancer screening, offering advanced capabilities to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the impact of a multimodal multi-instant AI-based system on the diagnostic performance of radiologists in interpreting mammograms.

Methods: We designed a multireader multicase study taking into account the evaluation of both interpretive and noninterpretive tasks. The study was approved by an institutional review board and is compliant with HIPAA. The dataset included 90 cancer-proven and 150 negative cases. The overall diagnostic performance was compared between the unaided vs aided reading condition. Intraclass correlation coefficient (ICC), Fleiss's kappa, and accuracy were used to quantify the agreement and performance on noninterpretive tasks. Reading time and perceived fatigue were used as comprehensive metrics to assess the efficiency of readers.

Results: The average area under the receiver operating characteristic curve increased by 7.4% (95% CI, 4.5%-10%) with the concurrent assistance of the AI system (P <.001). On average, readers found 8% more cancers in the assisted reading condition. The ICC went from 0.6 (95% CI, 0.55-0.65) in the unassisted condition to 0.74 (95% CI, 0.70-0.78) for readings done with AI (P <.001). An overall decrease of 24% in reading time and a reduction in perceived fatigue was also found.

Conclusion: The incorporation of this AI system, capable of handling multiple image type, prior mammograms, and multiple outputs, improved the diagnostic proficiency of radiologists in identifying breast cancer while also reducing the time required for combined interpretive and noninterpretive tasks.

Breast radiologists have high rates of burnout. Some contributing factors include the sedentary nature of the occupation, reading room design and isolation associated with higher volumes, and increased remote interpretation. Reading rooms can also be filled with numerous distractions and produce conditions that do not support optimal workflow. Identifying and addressing these issues may help prolong physician careers and increase overall productivity. This article presents approaches to improve wellness for breast imaging radiologists and reduce the overall rate of burnout.

Introduction: The development of abbreviated breast MRI (AB-MRI) protocols reduce scan times. This paper reports the performance of AB-MRI at a tertiary care public academic medical center in comparison with established literature.

Methods: This HIPAA-compliant IRB-approved retrospective study reviewed 413 AB-MRI screenings in high-risk patients from June 2020 to March 2023. Data were collected from 3 databases (MagView, Cerner PowerChart, and Prism Primordial). Demographics and overall BI-RADS assessment were recorded. For all positive (BI-RADS 0, 3, 4, 5) examinations, manual review of each case was performed. Performance metrics (sensitivity, specificity, cancer detection rate [CDR], recall rate, positive predictive value [PPV] 3 and negative predictive value [NPV]) were calculated. PubMed and Google Scholar were used to review similar AB-MRI studies to compare performance metrics.

Results: There were 413 AB-MRI examinations from 413 unique patients. The majority of cases were audit-negative BI-RADS 1 or 2 (83.8%, 346/413). There were 67 (16.2%, 67/413) audit-positive cases with 3.6% (15/413) BI-RADS 3, 10.9% (45/413) BI-RADS 4, 0.7% (3/413) BI-RADS 5, and 1.0% (4/413) BI-RADS 0. Performance metrics showed a sensitivity of 100.0% (95% CI, 63.1%-100.0%) and a specificity of 85.7% (95% CI, 81.9%-88.9%). The PPV3 was 14.3% (95% CI, 5.1%-23.5%), and the NPV was 100.0% (95% CI, 99.0%-100.0%). The CDR was 19.4 per 1000 screenings. The results are comparable to prior literature and benchmark data.

Conclusion: This study demonstrates high sensitivity (100.0%) and NPV (100.0%) of AB-MRI with comparable specificity (85.7%) and CDR (19.4/1000) to the literature, adding support to the use of AB-MRI. Further research is needed to optimize AB-MRI protocols.