Journal of Breast Imaging最新文献

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.

Breast cancer is the most prevalent cancer in women in Europe, and while all European countries have some form of screening for breast cancer, disparities in organization and implementation exist. Breast density is a well-established risk factor for breast cancer; however, most countries in Europe do not have recommendations in place for notification of breast density or additional supplementary imaging for women with dense breasts. Various supplemental screening modalities have been investigated in Europe, and when comparing modalities, MRI has been shown to be superior in cancer detection rate and in detecting small invasive disease that may impact long-term survival, as demonstrated in the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial in the Netherlands. Based on convincing evidence, the European Society of Breast Imaging issued recommendations that women with category D density undergo breast MRI from ages 50 to 70 years at least every 4 years and preferably every 2 to 3 years. However, currently no countries in Europe routinely offer women with BI-RADS category D density breasts MRI as supplemental imaging. The reasons for lack of implementation of MRI screening are multifactorial. Concerns regarding increased recalls have been cited, as have cost and lack of resources. However, studies have demonstrated breast MRI in women with BI-RADS category D density breasts to be cost-effective compared with the current breast cancer screening standard of biannual mammography. Furthermore, abbreviated MRI protocols could facilitate more widespread use of affordable MRI screening. Women's perception on breast density notification and supplemental imaging is key to successful implementation.

Cystic neutrophilic granulomatous mastitis (CNGM) is a rare type of granulomatous lobular mastitis (GLM) with a distinct histologic pattern characterized on histopathology by clear lipid vacuoles lined by peripheral neutrophils ("suppurative lipogranulomas"), often containing gram-positive bacilli and strongly associated with Corynebacterial infection (in particular, Corynebacterium kroppenstedtii). Cystic neutrophilic granulomatous mastitis has a distinct histopathologic appearance, but the imaging appearance is less well described and has been limited to case reports and small case series published primarily in pathology literature. Mammographic findings of CNGM include focal asymmetry, skin thickening, and irregular or oval masses. Sonographic findings of CNGM include irregular mass, complex collection/abscess, dilated ducts with intraductal debris, axillary lymphadenopathy, and skin thickening with subcutaneous edema. The imaging features of CNGM are nonspecific, and biopsy is required. Identifying a causative organism, when possible, requires a Gram stain, microbiological culture, and, potentially, molecular analysis. Although therapeutic options exist for CNGM, including antibiotics, steroids, and surgical intervention, there is no current consensus on optimal treatment.

The National Cancer Institute-funded Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer mathematical models have been increasingly utilized by policymakers to address breast cancer screening policy decisions and influence clinical practice. These well-established and validated models have a successful track record of use in collaborations spanning over 2 decades. While mathematical modeling is a valuable approach to translate short-term screening performance data into long-term breast cancer outcomes, it is inherently complex and requires numerous inputs to approximate the impacts of breast cancer screening. This review article describes the 6 independently developed CISNET breast cancer models, with a particular focus on how they represent breast cancer screening and estimate the contribution of screening to breast cancer mortality reduction and improvements in life expectancy. We also describe differences in structures and assumptions across the models and how variation in model results can highlight areas of uncertainty. Finally, we offer insight into how the results generated by the models can be used to aid decision-making regarding breast cancer screening policy.

Objective: To evaluate the T2 signal intensity (SI) of axillary lymph nodes as a potential functional imaging marker for metastasis in patients with mucinous breast cancer.

Methods: A retrospective review of breast MRIs performed from April 2008 to March 2024 was conducted to identify patients with mucinous breast cancer and adenopathy. Two independent, masked readers qualitatively assessed the T2 SI of tumors and lymph nodes. The T2 SI ratio for adenopathy and contralateral normal lymph nodes was quantitatively measured using the ipsilateral pectoralis muscle as a reference. Comparisons between malignant and nonmalignant lymph nodes were made using the chi-square test for qualitative assessments and the Mann-Whitney U test for quantitative assessments.

Results: Of 17 patients (all female; mean age, 48.4 ± 10.7 years; range: 29-80 years), 12 had malignant nodes, while 5 had benign nodes. Qualitative assessment revealed that the primary mucinous breast cancer was T2 hyperintense in most cases (88.2%-94.1%). No significant difference in qualitative T2 hyperintensity was observed between malignant and nonmalignant nodes (P = .51-.84). Quantitative T2 SI ratio parameters, including the ratio of mean and minimal node T2 SI to mean ipsilateral pectoralis muscle T2 SI, were higher in malignant nodes vs benign and contralateral normal nodes (P <.05).

Conclusion: Metastatic axillary lymph nodes exhibit high T2 SI, which could serve as a functional biomarker beyond traditional morphological assessment. Future studies should prioritize investigating more precise measurements, such as T2 mapping, and confirm these results in larger groups and across mucinous neoplasms in other organs.