Journal of Breast Imaging最新文献

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.

Opportunities exist to provide high-quality breast imaging fellowship training in the community setting. Various challenges exist, however, including obtaining funding for a fellowship position, creating an educational curriculum in a potentially nonacademic environment, and developing an overall competitive program that will attract radiology trainees. Here, we explore factors that contribute to the establishment of an academic breast imaging fellowship program in the community setting based on experience, including (1) providing guidance on how to secure funding for a breast imaging fellowship position; (2) developing a training curriculum based on established guidelines from the Accreditation Council for Graduate Medical Education, American College of Radiology, and Society of Breast Imaging; and (3) navigating the landscape of the recruitment process, from program branding to matching applicants.

Objective: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented.

Methods: This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared.

Results: The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments.

Conclusion: Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women.

Objective: To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample.

Methods: In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics. Up to 5 years of follow-up examination results were evaluated in cases to establish benignity. The algorithm sorted cases into groups of "suspicious" and "low suspicion." A theoretical workload reduction was calculated by subtracting cases triaged as "low suspicion" from the sample.

Results: At the default 93% sensitivity setting, there was significant improvement (P <.05) in the following triage simulation mean performance measures compared with actual outcome: 45.5% improvement in recall rate (13.4% to 7.3%; 95% CI, 6.2-8.3), 119% improvement in positive predictive value (PPV) 1 (5.3% to 11.6%; 95% CI, 9.96-13.4), 28.5% improvement in PPV2 (24.6% to 31.6%; 95% CI, 24.8-39.1), 20% improvement in sensitivity (83.3% to 100%; 95% CI, 100-100), and 7.2% improvement in specificity (87.2% to 93.5%; 95% CI, 92.4-94.5). A theoretical 62.5% workload reduction was possible. At the ultrahigh 99% sensitivity setting, a theoretical 27% workload reduction was possible. No cancers were missed by the algorithm at either sensitivity.

Conclusion: Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.

Objective: There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone.

Methods: A commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets.

Results: A total of 26 694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7/13 false negative cancers (54%) in the FFDM cohort and 4/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist.

Conclusion: Artificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.