Objective: The purpose of this study is to assess the “real-world” impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center. Methods: Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations. Results: Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9–1.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8–1.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97–1.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8–1.5). Conclusion: Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.

Background: Health disparities among minority groups, especially African Americans, can limit their access to quality medical care and lead to disproportionate medical management and disease outcomes. The aim of this study was to compare the COVID-19-related change in mammogram volumes and cancer detection at two affiliated academic breast centers, one that serves a predominantly African American patient population and one that serves a predominantly non-African American patient population. Materials and methods: For the purpose of anonymity, racial demographics were collected and the center with a higher African American patient population was designated as institution A, while the center with a higher non-African American patient population was designated as institution B. Careful selection of the two breast centers was instituted in order to limit the impact of potential confounders other than race. An Institutional Review Board (IRB) exemption was obtained and two Mammography Quality Standards Act (MQSA) reports were generated; one for March 2020 through September 2020, during the height of the COVID-19 pandemic, and one for March 2019 through September 2019 to serve as the pre-pandemic control group. The i2b2 Query Analysis Tool® was used to obtain racial demographic data and compare the percent change in screening and diagnostic mammograms, image-guided biopsies, total cancers diagnosed by imaging, and percent of minimal cancers for both institutions. Results: Screening mammograms and breast cancer detection decreased in 2020 compared to 2019 at both institutions. However, the percent change from 2019 to 2020 was greater at institution A than at institution B. Percent minimal cancers, an indicator of early-stage breast cancer also decreased more drastically at institution A than at institution B. Interestingly, the total number of diagnostic exams and image-guided biopsies increased in 2020 at institution B, whereas both decreased at institution A. Conclusion: The COVID-19 pandemic may lead to worsening racial disparities in breast cancer screening. In an effort to narrow future disparity, it is crucial for radiologists and other health care providers to be aware of this inequality and educate all women on the importance of obtaining routine screening mammography. More studies are needed.

Gender-affirming hormone therapy may be utilized by transgender women hoping to induce feminizing changes and suppress the effects of endogenous testosterone. It is well established that exogenous hormone use increases risk of breast cancer in cisgender women; however, a link in the transgender population is less clear. Our case presents a transgender woman undergoing gender-affirming exogenous hormone therapy who subsequently developed breast cancer. We review the available literature regarding risks of exogenous hormone therapy with regards to breast cancer development. Additionally, we present current societal guidelines and recommendations for screening in this population.

Background: Image-based artificial intelligence (AI) risk models can estimate short-term breast cancer risk directly from mammograms and may outperform traditional questionnaire-based tools. However, risk stratification remains particularly challenging in women with dense breasts who do not otherwise meet high-risk criteria. At our institutions, molecular breast imaging (MBI) is used as supplemental screening for this population. This study evaluated the performance and clinical utility of a mammography-based AI risk model (iCAD ProFound AI^® Risk) in predicting short-term breast cancer risk among women with dense breasts undergoing MBI. Methods: This retrospective IRB-approved study included 416 non-actionable (BI-RADS category 1 or 2) screening digital breast tomosynthesis mammograms (BI-RADS C–D density) obtained from 2018 to 2023, all followed by MBI within one year. The cohort comprised 70 cancer cases (16.8%) and 346 (83.2%) non-cancer controls. Mammograms were retrospectively processed using the ProFound AI^® Risk model to generate 1-year risk and density scores. Tyrer-Cuzick and Gail model scores were computed for comparison. Group differences were assessed using t-tests and effect sizes, and model discrimination was evaluated with ROC analysis using area under the curve (AUC), sensitivity, specificity, and 95% confidence intervals (CIs). Results: Across the full cohort, mean AI risk scores were higher in cancer cases than controls (0.41±0.35 vs. 0.37±0.21), although this difference was not statistically significant (P=0.239; Cohen’s d=0.23). Subgroup analyses demonstrated progressively stronger discriminatory performance with increasing breast density. The greatest separation was observed in women with extremely dense breasts (category D), where the AI model achieved an AUC of 0.75 (95% CI: 0.61–0.89; P=0.049), with 69.3% sensitivity and 61.1% specificity at a threshold of 0.14. Effect size in this group was the largest (d=0.41). In contrast, traditional models showed limited and non-significant discrimination across all density categories, with AUC values ranging from 0.54 to 0.63. When stratified by cancer subtype, the AI model produced significantly higher risk scores in invasive lobular carcinoma (ILC) compared with controls (0.69±0.46 vs. 0.41±0.32; P=0.048; d=0.56). Although differences in ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) were not significant, risk scores trended higher for cancer cases. A similar pattern of increasing AI-estimated risk was observed with higher tumor grade, with the strongest separation seen in grade 2 cancers (P=0.089). Conclusions: Although overall differences between cancer and non-cancer groups were not statistically significant, the mammography-based AI risk model demonstrated meaningful and statistically significant discrimination in women with extremely dense breasts, outperforming both Tyrer-Cuzick and Gail models. The AI model also showed better separation in ILC and in higher-grade tumors. These findings support the role of image-based AI tools in refining risk assessment in women for whom mammography is least effective and in guiding more targeted use of supplemental MBI screening.

Radiation-induced rib fractures (RIRF) are long-term complications associated with irradiation of the chest/chest wall. RIRFs are commonly seen in lung- or breast-cancer patients treated with stereotactic body radiotherapy (SBRT) or conventional external beam radiation therapy (EBRT), respectively. We report a case of a 31-year-old female presenting with pathological fractures of the third, fourth, and fifth ribs discovered on magnetic resonance imaging (MRI) as a complication from pencil beam scanning (PBS) proton therapy (PT), of the whole left breast and regional lymph nodes. To our knowledge, this presentation is the first to be initially reported on MRI in radiological literature.

Cholesterol granuloma of the breast is a rare entity that can be indistinguishable from cancer on physical exam and imaging. We present the case of a 58-year-old woman who developed a new enhancing mass on high-risk screening magnetic resonance imaging (MRI). Interestingly, this mass developed in the same area as a previously noted benign-appearing inflammatory cyst that had been decreasing in size on MRI and mammogram over many years. Ultrasound-guided biopsy was performed which revealed cholesterol granuloma. We will discuss the nonspecific imaging findings and the characteristic histopathological features that confirm diagnosis.