BACKGROUND: Although DBT is the standard initial imaging modality for women with focal breast symptoms, the importance of ultrasound has grown rapidly in the past decades. Therefore, the Breast UltraSound Trial (BUST) focused on assessing the diagnostic value of ultrasound and digital breast tomosynthesis (DBT) for the evaluation of breast symptoms by reversing the order of breast imaging; first performing ultrasound followed by DBT. This side-study of the BUST evaluates patients\' perceptions of ultrasound and DBT in a reversed setting.
METHODS: After imaging, 1181/1276 BUST participants completed a survey consisting of open and closed questions regarding both exams (mean age 47.2, ±11.74). Additionally, a different subset of BUST participants (n = 29) participated in six focus group interviews 18-24 months after imaging to analyze their imaging experiences in depth.
RESULTS: A total of 55.3% of women reported reluctance to undergoing DBT, primarily due of pain, while the vast majority also find bilateral DBT reassuring (87.3%). Thematic analysis identified themes related to 1) imaging reluctance (pain/burden, result, and breast harm) and 2) ultrasound and DBT perceptions. Regarding the latter, the theme comfort underscores DBT as burdensome and painful, while ultrasound is largely perceived as non-burdensome. Ultrasound is also particularly valued for its interactive nature, as highlighted in the theme interaction. Perceived effectiveness reflects women\'s interest in bilateral breast evaluation with DBT and the visibility of lesions, while they express more uncertainty about the reliability of ultrasound. Emotional impact portrays DBT as reassuring for many women, whereas opinions on the reassurance provided by ultrasound are more diverse. Additional themes include costs, protocols and privacy.
CONCLUSIONS: Ultrasound is highly tolerated, and particularly valued is the interaction with the radiologist. Nearly half of women express reluctance towards DBT; nevertheless, a large portion report feeling more confident after undergoing bilateral DBT, reassuring them of the absence of abnormalities. Understanding patients\' perceptions of breast imaging examinations is of great value when optimizing diagnostic pathways.

OBJECTIVE: The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. We explore whether artificial Intelligence (AI)-generated images can help in simulation education and result in measurable improvement in performance of residents in training.
METHODS: We developed a generative adversarial network (GAN) that was capable of generating mammography images with varying characteristics, such as size and density, and created a tool with which a user could control these characteristics. The tool allowed the user (a radiology resident) to realistically insert cancers within different regions of the mammogram. We then provided this tool to residents in training. Residents were randomized into a practice group and a non-practice group, and the difference in performance before and after practice with such a tool (in comparison to no intervention in the non-practice group) was assessed.
RESULTS: Fifty residents participated in the study, 27 underwent simulation training, and 23 did not. There was a significant improvement in the sensitivity (7.43 percent, significant at p-value = 0.03), negative predictive value (5.05 percent, significant at p-value = 0.008) and accuracy (6.49 percent, significant at p-value = 0.01) among residents in the detection of cancer on mammograms after simulation training.
CONCLUSIONS: Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training.
CONCLUSIONS: Using generative artificial intelligence, simulation training modules can be developed that can help residents in training by providing them with a visual impression of a variety of different cases.
CONCLUSIONS: Generative networks can produce diagnostic imaging with specific characteristics, potentially useful for training residents. Training with generating images improved residents\' mammographic diagnostic abilities. Development of a game-like interface that exploits these networks can result in improvement in performance over a short training period.

BACKGROUND: Tuberculous mastitis (TBM), is an uncommon form of extra-pulmonary tuberculosis. Clinical and radiological overlap of tuberculous mastitis with malignancy and other granulomatous conditions, along with its paucibacillary nature, make it a diagnostic challenge. In our study, we aim to assess the radiological response of microbiologically negative granulomatous mastitis cases to anti-tuberculous treatment (ATT) in an endemic country.
METHODS: Eighty-seven cases demonstrating granulomatous lesions on breast biopsy were identified. Of these, 49 patients who were treated with ATT and had at least two serial ultrasound follow-ups were included in our study. Mammogram and ultrasound were used for initial imaging. Subsequently, ultrasound was used for serial follow-up. Mantoux skin test, acid fast staining and histological examination of tissue sample were the other investigations used.
RESULTS: Radiologically, on ultrasound, well-circumscribed hypoechoic masses were noted in 18 patients, followed by ill-defined collections with tubular extensions in 15 cases, abscesses in 8, and a focal heterogeneity in 8 patients. Following ATT, 17 patients showed radiological resolution in 4 weeks, 18 of them at 3 months, and nine of them in 6 months.
CONCLUSIONS: Excellent and prompt radiological response to ATT, indicates the need for a high degree of suspicion for tuberculous mastitis (TBM), in endemic countries, even though microbiological tests may turn out negative.

BACKGROUND: Despite the relatively low breast cancer incidence in Estonia, mortality remains high, and participation in mammography screening is below the recommended 70%. The objective of this register-based study was to evaluate incidence-based (IB) breast cancer mortality before and after the introduction of organized mammography screening in 2004.
METHODS: Breast cancer deaths individually linked to breast cancer diagnosis were obtained from the Estonian Cancer Registry and used for calculating IB mortality. We compared age-specific IB mortality rates across 5-year birth cohorts and 5-year periods. Poisson regression was used to compare IB mortality for one age group invited to screening (50-63) and three age groups not invited to screening (30-49, 65-69, and 70+) during two periods before and after screening initiation (1993-2003 and 2004-2014). Joinpoint regression was used for age-standardized incidence and IB mortality trends.
RESULTS: Age-standardized IB mortality has been decreasing since 1997. Age-specific IB mortality for birth cohorts never exposed to screening showed a continuous increase with age, while in cohorts exposed to organized screening the mortality curve flattened or declined after the age of first invitation. Significant decreases in mortality from 1993-2003 to 2004-2014 were seen in the 30-49 (age-adjusted rate ratio 0.51, 95% CI 90.42-0.63) and 50-63 (0.65, 95% CI 0.56-0.74) age groups, while no decline was seen in the 65-69 and 70+ age groups.
CONCLUSIONS: The age specific IB mortality curves in birth cohorts exposed to screening and the significant mortality decline in the target age group after the initiation of the organized program suggest a beneficial effect of screening. Improved treatment without screening has not reduced mortality in older age groups. Our results support raising the upper screening age limit to 74 years.

UNASSIGNED: Women recalled from breast cancer screening receive post-screening work-up in the hospital with conventional breast imaging. The RACER trial aimed to study whether contrast-enhanced mammography (CEM) as primary imaging instead of conventional imaging resulted in more accurate and efficient diagnostic work-up in recalled women.
UNASSIGNED: In this randomised, controlled trial (registered under NL6413/NTR6589) participants were allocated using deterministic minimisation to CEM or conventional imaging as a primary work-up tool in two general and two academic hospitals. Predefined patients\' factors were reason for recall, BI-RADS score, and study centre. Primary outcomes were sensitivity and specificity. Secondary outcomes were the proportion of women needing supplemental examinations, and number of days until diagnosis.
UNASSIGNED: Between April, 2018, and September, 2021, 529 patients recalled from the Dutch screening program were randomised, 265 to conventional imaging and 264 to CEM. Three patients in the control arm had to be excluded from analysis due to a protocol breach. After the entire work-up, sensitivity was 98.0% (95% CI; 92.2-99.7%) in the intervention arm and 97.7% (91.8-99.6%) in the control arm (p = 1.0), and specificity was 75.6% (72.5-76.6%) and 75.4% (72.5-76.4%, p = 1.0), respectively. Based on only primary full-field digital mammography/digital breast tomosynthesis or CEM, final diagnosis was reached in 27.7% (73/264) in the intervention arm and 1.1% (3/262) in the control arm. The frequency of supplemental imaging was significantly higher in the control arm (p < 0.0001). Median time needed to reach final diagnosis was comparable: 1 day (control arm: IQR 0-4; intervention arm: IQR 0-3). Thirteen malignant occult lesions were detected using CEM, versus three using conventional imaging. No serious adverse events occurred.
UNASSIGNED: Diagnostic accuracy of CEM in the work-up of recalled women is comparable with conventional imaging. However, work-up with CEM as primary imaging is a more efficient pathway.
UNASSIGNED: ZonMw (grant number 843001801) and GE Healthcare.

Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear. Purpose To compare the performance of mammography alone, mammography with AI, and mammography plus supplemental US for screening women with dense breasts, and to investigate the characteristics of the detected cancers. Materials and Methods A retrospective database search identified consecutive asymptomatic women (≥40 years of age) with dense breasts who underwent mammography plus supplemental whole-breast handheld US from January 2017 to December 2018 at a primary health care center. Sequential reading for mammography alone and mammography with the aid of an AI system was conducted by five breast radiologists, and their recall decisions were recorded. Results of the combined mammography and US examinations were collected from the database. A dedicated breast radiologist reviewed marks for mammography alone or with AI to confirm lesion identification. The reference standard was histologic examination and 1-year follow-up data. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate (AIR) of mammography alone, mammography with AI, and mammography plus US were compared. Results Among 5707 asymptomatic women (mean age, 52.4 years ± 7.9 [SD]), 33 (0.6%) had cancer (median lesion size, 0.7 cm). Mammography with AI had a higher specificity (95.3% [95% CI: 94.7, 95.8], P = .003) and lower AIR (5.0% [95% CI: 4.5, 5.6], P = .004) than mammography alone (94.3% [95% CI: 93.6, 94.8] and 6.0% [95% CI: 5.4, 6.7], respectively). Mammography plus US had a higher CDR (5.6 vs 3.5 per 1000 examinations, P = .002) and sensitivity (97.0% vs 60.6%, P = .002) but lower specificity (77.6% vs 95.3%, P < .001) and higher AIR (22.9% vs 5.0%, P < .001) than mammography with AI. Supplemental US alone helped detect 12 cancers, mostly stage 0 and I (92%, 11 of 12). Conclusion Although AI improved the specificity of mammography interpretation, mammography plus supplemental US helped detect more node-negative early breast cancers that were undetected using mammography with AI. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Whitman and Destounis in this issue.

UNASSIGNED: Triple-negative breast cancer (TNBC) has an aggressive clinical behaviour, with advanced stages at initial diagnostic evaluation, early recurrences and poor survival, so the purpose was to determine the clinical and radiological manifestations associated with TNBC.
UNASSIGNED: A case-control study in women diagnosed with breast cancer from January 2015 to August 2022 at the \'Instituto Regional de Enfermedades Neoplásicas del Norte\'. We classified cases (Triple Negative subtype) and controls (Luminal A, Luminal B and HER2) according to immunohistochemistry ical analysis. Bivariate and multivariate logistic regression models were used to calculate the odds ratio (OR) with their respective 95% confidence intervals (CIs).
UNASSIGNED: The medical reports of 88 cases and 236 controls were reviewed. Cases were more likely to report pain (p = 0.001), nodules on ultrasound (p = 0.01) and mammography (p = 0.003), superior median size (p < 0.05), posterior enhancement (p = 0.001) and moderate density (p = 0.003). Multivariate analysis identified that TNBC was more likely to have a nodular type lesion by ultrasound (OR: 9.73, 95% CI: 1.10-86.16; p = 0.04), ultrasound lesion larger than 36 mm (OR: 4.99, 95% CI: 1.75-14.17; p = 0.003) and moderate density (OR: 3.83, 95% CI: 1.44-10.14; p = 0.007).
UNASSIGNED: There are particular clinical and imaging manifestations of TNBC, showing that radiological lesions that presented characteristics in ultrasound as nodular type lesions larger than 36 mm and in mammography moderate grade density, were associated with this subtype of breast tumours in a Peruvian population.

UNASSIGNED: Mammographic breast density (MBD), a well-established factor linked to breast cancer, is the focus of this preliminary report among women across multiple centers in Riyadh. The study aims to identify risk factors associated with high breast density.
UNASSIGNED: MBD was assessed at three hospitals in Riyadh, Saudi Arabia, using the American College of Radiology (ACR) categories: A (almost entirely fatty), B (scattered areas of fibroglandular density), C (heterogeneously dense), and D (extremely dense). Breast density distributions were analyzed in relation to age, body mass index (BMI), family history, parity, and hormonal therapy usage.
UNASSIGNED: The study included 1,530 women, revealing an inverse association between dense breast proportion and age/BMI. Notably, 43.3% [95% confidence interval (CI): 43.2% to 43.5%] of women aged 40-79 years exhibited heterogeneously or highly dense breasts, with this proportion inversely correlated with age and BMI.
UNASSIGNED: Healthcare providers should consider breast density for appropriate screening and, if necessary, recommend supplemental methods. Policymakers and healthcare providers, when discussing breast density notification legislation, should be mindful of its high prevalence, ensuring women notified have opportunities to evaluate breast cancer risk and pursue supplemental screening options if deemed appropriate.

BACKGROUND: Early detection of cancer and provision of appropriate treatment can increase the cancer cure rate and reduce cancer-related deaths. Early detection requires improving the cancer screening quality of each medical institution and enhancing the capabilities of health professionals through tailored education in each field. However, during the COVID-19 pandemic, regional disparities in educational infrastructure emerged, and educational accessibility was restricted. The demand for remote cancer education services to address these issues has increased, and in this study, we considered medical metaverses as a potential means of meeting these needs. In 2022, we used Metaverse Educational Center, developed for the virtual training of health professionals, to train radiologic technologists remotely in mammography positioning.
OBJECTIVE: This study aims to investigate the user experience of the Metaverse Educational Center subplatform and the factors associated with the intention for continuous use by focusing on cases of using the subplatform in a remote mammography positioning training project.
METHODS: We conducted a multicenter, cross-sectional survey between July and December 2022. We performed a descriptive analysis to examine the Metaverse Educational Center user experience and a logistic regression analysis to clarify factors closely related to the intention to use the subplatform continuously. In addition, a supplementary open-ended question was used to obtain feedback from users to improve Metaverse Educational Center.
RESULTS: Responses from 192 Korean participants (male participants: n=16, 8.3%; female participants: n=176, 91.7%) were analyzed. Most participants were satisfied with Metaverse Educational Center (178/192, 92.7%) and wanted to continue using the subplatform in the future (157/192, 81.8%). Less than half of the participants (85/192, 44.3%) had no difficulty in wearing the device. Logistic regression analysis results showed that intention for continuous use was associated with satisfaction (adjusted odds ratio 3.542, 95% CI 1.037-12.097; P=.04), immersion (adjusted odds ratio 2.803, 95% CI 1.201-6.539; P=.02), and no difficulty in wearing the device (adjusted odds ratio 2.020, 95% CI 1.004-4.062; P=.049). However, intention for continuous use was not associated with interest (adjusted odds ratio 0.736, 95% CI 0.303-1.789; P=.50) or perceived ease of use (adjusted odds ratio 1.284, 95% CI 0.614-2.685; P=.51). According to the qualitative feedback, Metaverse Educational Center was useful in cancer education, but the experience of wearing the device and the types and qualities of the content still need to be improved.
CONCLUSIONS: Our results demonstrate the positive user experience of Metaverse Educational Center by focusing on cases of using the subplatform in a remote mammography positioning training project. Our results also suggest that improving users\' satisfaction and immersion and ensuring the lack of difficulty in wearing the device may enhance their intention for continuous use of the subplatform.

BACKGROUND: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.
METHODS: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.
RESULTS: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.
CONCLUSIONS: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.
CONCLUSIONS: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.
CONCLUSIONS: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16\'s superior performance in localizing BAC.