Incidence-Based Breast Cancer Mortality Trends in Estonia Before and After the Introduction of Organized Mammography Screening: A Register-Based Study.

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.

Addressing Breast Cancer Equity Through Virtual Community Oncology Navigation and Engagement (vCONET).

BACKGROUND: Breast cancer remains a leading cause of cancer morbidity and mortality worldwide. In the United States, Black women face significant disparities in screening mammograms, experience higher rates of breast cancer at advanced stages, and are more likely to die from the disease. AIMS: This study aimed to develop and beta-test a virtual health navigation program to enhance breast cancer care within the Black community. We identified barriers to utilizing virtual patient navigators and factors impacting the adoption of virtual navigation for breast cancer information among Black women. METHODS: The vCONET (Virtual Community Oncology Navigation and Engagement) intervention was delivered through the Second Life virtual platform. The informational content was collaboratively developed with community members. Participants engaged in an informational session on risk factors, mammography information, and preventive behaviors. Surveys (n = 18) and focus groups (n = 9) assessed knowledge and insights into perceptions. RESULTS: Findings revealed a positive impact of the intervention, with participants expressing increased knowledge and willingness to seek further information about breast cancer prevention, and highlighted the engaging nature of the virtual environment, while acknowledging potential technological challenges. CONCLUSION: Virtual health navigation shows promise in addressing breast cancer disparities by promoting awareness among Black women. Future efforts should optimize virtual navigation approaches through collaborative engagement for lasting impact, enhancing breast cancer care and equity in communities of color.

The Chiraiya project: a retrospective analysis of breast cancer detection gaps addressed via mobile mammography in Jammu Province, India.

BACKGROUND: Breast cancer remains a pervasive threat to women worldwide, with increasing incidence rates necessitating effective screening strategies. Timely detection with mammography has emerged as the primary tool for mass screening. This retrospective study, which is part of the Chiraiya Project, aimed to evaluate breast lesion patients identified during opportunistic mammography screening camps in Jammu Province, India. METHODS: A total of 1505 women aged 40 years and older were screened using a mobile mammographic unit over a five-year period, excluding 2020 and 2021 due to the COVID-19 pandemic. The inclusion criterion was women in the specified age group, while the exclusion criterion was women with open breast wounds, history of breast cancer or a history of breast surgery. The screening process involved comprehensive data collection using a detailed Proforma, followed by mammographic assessments conducted within strategically stationed mobile units. Radiological interpretations utilizing the BI-RADS system were performed, accompanied by meticulous documentation of patient demographics, habits, literacy, medical history, and breastfeeding practices. Participants were recruited through collaborations with NGOs, army camps, village panchayats, and urban cooperatives. Screening camps were scheduled periodically, with each camp accommodating 90 patients or fewer. RESULTS: Among the 1505 patients, most were aged 45-50 years. The number of screenings increased yearly, peaking at 441 in 2022. The BI-RADS II was the most common finding (48.77%), indicating the presence of benign lesions, while the BI-RADS 0 (32.96%) required further evaluation. Higher-risk categories (BI-RADS III, IV, V) were less common, with BI-RADS V being the rarest. Follow-up adherence was highest in the BI-RADS III, IV, and V categories, with BI-RADS V achieving 100% follow-up. However, only 320 of 496 BI-RADS 0 patients were followed up, indicating a gap in continuity of care. The overall follow-up rate was 66.89%. Compared to urban areas, rural areas demonstrated greater screening uptake but lower follow-up rates, highlighting the need for tailored interventions to improve follow-up care access, especially in rural contexts. CONCLUSION: This study underscores the efficacy of a mobile mammographic unit in reaching marginalized populations. Adherence to screening protocols has emerged as a linchpin for early detection, improved prognosis, and holistic public health enhancement. Addressing misconceptions surrounding mammographic screenings, especially in rural settings, is crucial. These findings call for intensified efforts in advocacy and education to promote the benefits of breast cancer screening initiatives. Future interventions should prioritize improving access to follow-up care and addressing screening to enhance breast cancer management in Jammu Province.

Utility of Second-look Ultrasonography in Distinguishing BI-RADS 4 Calcifications Detected on Mammography: An observational study.

This study aimed to assess the utility of second-look ultrasonography (US) in differentiating breast imaging reporting and data system (BI-RADS) 4 calcifications initially detected on mammography (MG). BI-RADS 4 calcifications have a wide range of positive predictive values. We hypothesized that second-look US would help distinguish BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG. This study included 1622 pure BI-RADS 4 calcifications in 1510 women (112 patients with bilateral calcifications). The cases were randomly divided into training (85%) and testing (15%) datasets. Two nomograms were developed to differentiate BI-RADS 4 calcifications in the training dataset: the MG-US nomogram, based on multifactorial logistic regression and incorporated clinical information, MG, and second-look US characteristics, and the MG nomogram, based on clinical information and mammographic characteristics. Calibration of the MG-US nomogram was performed using calibration curves. The discriminative ability and clinical utility of both nomograms were compared using the area under the receiver operating characteristic curve (AUC) and the decision analysis curve (DCA) in the test dataset. The clinical information and imaging characteristics were comparable between the training and test datasets. The bias-corrected calibration curves of the MG-US nomogram closely approximate the ideal line for both datasets. In the test dataset, the MG-US nomogram exhibited a higher AUC than the MG nomogram (0.899 vs 0.852, P = .01). DCA demonstrated the superiority of the MG-US nomogram over the MG nomogram. Second-look US features, including ultrasonic calcifications, lesions, and moderate or marked color flow, were valuable for distinguishing BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG.

Ethnic inequalities in coverage and use of women's cancer screening in Peru.

OBJECTIVE: This study aimed to assess ethnic inequalities in the coverage and utilization of cancer screening services among women in Peru. METHODS: Data from the 2017-2023 Demographic and Family Health Survey in Peru were analyzed to evaluate ethnic disparities in screening coverage for breast and cervical cancer, including clinical breast examination (CBE), Pap smear test (PST), and mammography. Measures such as the GINI coefficient and Slope Index of Inequality (SII) were used to quantify coverage and utilization disparities among ethnic groups. RESULTS: The study included 70,454 women aged 30-69. Among women aged 40-69, 48.31% underwent CBE, 84.06% received PST, and 41.69% underwent mammography. It was found inequalities in coverage for any cancer screening (GINI: 0.10), mammography (GINI: 0.21), CBE (GINI: 0.19), and PST (GINI: 0.06), in 25 Peruvian regions. These inequalities were more pronounced in regions with larger populations of Quechua, Aymara, and Afro-Peruvian women. In rural areas, Quechua or Aymara women (SII: -0.83, -0.95, and - 0.69, respectively) and Afro-Peruvian women (SII: -0.80, -0.92, and - 0.58, respectively) experienced heightened inequalities in the uptake of CBE, mammography, and PST, respectively. Like Quechua or Aymara women (SII: -0.50, SII: -0.52, and SII: -0.50, respectively) and Afro-Peruvian women (SII: -0.50, SII: -0.58, and SII: -0.44, respectively) with only a primary education. CONCLUSION: Ethnic inequalities affect breast and cervical cancer screening coverage across regions in Peru. In Quechua, Aymara, and Afro-Peruvian women the uptake of mammography, CBE, and PST was less frequently than their white or mestizo counterparts. These inequalities are attributed to sociodemographic conditions such as lower education levels and residence in rural or non-capital areas.

A Retrospective Analysis of Diagnostic Breast Imaging Outcomes in Young Women at a Tertiary Care Center.

(1) Purpose: The purpose of this study was to describe the outcomes of diagnostic breast imaging and the incidence of delayed breast cancer diagnosis in the study population. (2) Methods: We collected the outcome data from diagnostic mammograms and/or breast ultrasounds (USs) performed on women between the ages of 30 and 50 with symptomatic breast clinical presentations between 2018 and 2019. (3) Results: Out of 171 eligible patients, 10 patients (5.8%) had BIRADS 0, 90 patients (52.6%) had benign findings (BIRADS 1 and 2), 41 (24.0%) patients had probable benign findings requiring short-term follow-up (BIRADS 3), while 30 (17.5%) patients had findings suspicious of malignancy (BIRADS 4 and 5). In the BIRADS 3 group, 92.7% had recommended follow-up, while in BIRADS 4 and 5, only 83.3% underwent recommended biopsy at a mean time of 1.7 weeks (range 0-22 wks) from their follow-up scan. Ten (6%) patients were diagnosed with breast cancer, all of whom had BIRADS 4 or 5, with a mean time of breast cancer diagnosis from initial diagnostic imaging of 2.2 weeks (range 1-22 wks). No patients had delayed breast cancer diagnosis in our cohort. (4) Conclusions: We conclude that diagnostic mammograms and breast US are appropriate investigations for clinical breast concerns in women aged 30-50 years.

Intelligent breast cancer diagnosis with two-stage using mammogram images.

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Associations between quantitative measures of mammographic density and terminal ductal lobular unit involution in Chinese breast cancer patients.

BACKGROUND: Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD. METHODS: In this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype. RESULTS: We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend = < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend = < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors. CONCLUSIONS: Our findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

A case of bilateral intracystic papillary carcinoma breast.

ABSTRACT: Intracystic papillary carcinoma breast is an uncommon breast cancer consisting of 0.5-1.0% of all breast cancers. Papillary carcinoma is further subdivided into intraductal and intracystic papillary carcinoma. Intracystic papillary carcinoma is further divided into pure intracystic papillary carcinoma or associated with in situ carcinoma. The clinical and radiological features of intracystic papillary carcinoma are not specific, hence a high chance of misdiagnosis. Here we report a case of intracystic papillary carcinoma of both breasts which created a diagnostic dilemma.

Giant Myoid mammary hamartoma: A case report.

ABSTRACT: Mammary hamartoma are rare neoplasms of the breast. Myoid mammary hamartoma are a subtype comprising of prominent smooth muscle component along with normal breast tissue components including fibrous, adipose, and glandular tissue. We report the case of a 38-year-old lady who presented with a large 21 × 15 cm, firm, mobile lump in right breast, clinically mimicking as phyllodes tumor. The lesion was reported as BIRADS 4a on mammography. Fine needle aspiration cytology suggested benign breast disease. Wide local excision was performed. The excised lump was solid, gray-white with fatty yellowish areas. Histological features were of myoid mammary hamartoma. To the best of our knowledge, this is the largest myoid hamartoma reported till date. Fine needle aspiration, needle biopsy, and immunohistochemistry are of limited value as diagnostic modalities in these lesions. Complete surgical excision, proper identification, and follow-up is essential, as these lesions, more commonly those which are incompletely excised, can recur.

Practice of Mammography in a Private Nigerian Cancer Foundation: Our Experience.

BACKGROUND: Despite the proven effectiveness of mammography in screening and early breast cancer detection, there is still a huge disparity in both access to breast care and the quality of services provided in Nigeria. Non-governmental organizations (NGOs) have attempted to bridge this gap through awareness campaigns and subsidized breast imaging services. OBJECTIVES: To document the mammographic findings of adult females in a private NGO and assess the benefits of mammography practice in our locality. MATERIAL AND METHODS: This was a retrospective evaluation of mammographic examinations carried out over a two-year period (January 2020- December 2021) in a private cancer foundation in Abuja, Nor t h Ce nt r al Nigeria. Demographic details, clinical and mammographic features were analyzed with a statistical level of significance set at p≤0.05. RESULT: The age range of 565 women evaluated in this study was 31-84 years with the majority (55.7%) of them in the 40-49 year range. More than half (52.7%) of the women had had at least one previous mammogram. Screening was the predominant indication for mammograms in 361 women (63.9%) while 204(36.1%) were symptomatic. Breast pain (59.6%) and breast lump (26.3%) were the most common clinical indications. The predominant breast density pattern was the American College of Radiologists Breast Imaging and Reporting Data System (ACR BIRADS) type B (Scattered fibroglandular densities) in 241 women (42.7%). Mammogram was normal in 206 women (34.7%) while 52 (8.8%) had intraparenchymal findings. The final assessment showed that most of the mammograms were BIRADS category 1(69.6%) and 2(13.8%) signifying normal and benign findings. Body mass index, parity, age at first pregnancy, menopausal status, and breast density had significant relationships with the final BIRADS category. CONCLUSION: Mammography is an invaluable part of breast care in our locality. Evaluation of mammographic services in our private NGO showed a predominance of screening mammography while a majority of the women with symptomatic breast diseases had normal and benign findings.

CONTEXTE: Malgré l'efficacité avérée de la mammographie dans le dépistage et la détection précoce du cancer du sein, il existe encore une énorme disparité tant dans l'accès aux soins du sein que dans la qualité des services fournis au Nigeria. Les organisations non gouvernementales (ONG) ont tenté de combler cette lacune grâce à des campagnes de sensibilisation et à des services d'imagerie mammaire subventionnés. OBJECTIFS: Documenter les résultats mammographiques des femmes adultes dans une ONG privée et évaluer les avantages de la pratique de la mammographie dans notre localité. MATÉRIEL ET MÉTHODES: Il s'agissait d'une évaluation rétrospective des examens mammographiques réalisés sur une période de deux ans (janvier 2020 - décembre 2021) dans une fondation de lutte contre le cancer privée à Abuja, au Nigeria. Les détails démographiques, les caractéristiques cliniques et mammographiques ont été analysés avec un niveau de signification statistique fixé à p ≤ 0,05. RÉSULTAT: La tranche d'âge des 565 femmes évaluées dans cette étude était de 31 à 84 ans, la majorité (55,7 %) d'entre elles se situant dans la tranche d'âge de 40 à 49 ans. Plus de la moitié (52,7 %) des femmes avaient déjà subi au moins une mammographie précédente. Le dépistage était l'indication prédominante pour les mammographies chez 361 femmes (63,9 %), tandis que 204 (36,1 %) étaient symptomatiques. Les douleurs mammaires (59,6 %) et les masses mammaires (26,3 %) étaient les indications cliniques les plus courantes. Le motif de densité mammaire prédominant était de type B du système de notation et de rapport d'imagerie mammaire du Collège Américain des Radiologues (ACR BIRADS) chez 241 femmes (42,7 %). La mammographie était normale chez 206 femmes ( 34, 7 %) , t andi s que 52 ( 8, 8 %) présent ai ent des anomal i es intraparenchymateuses. L'évaluation finale a montré que la plupart des mammographies étaient classées BIRADS catégorie 1 (69,6 %) et 2 (13,8 %), ce qui signifie des résultats normaux et bénins. L'indice de masse corporelle, la parité, l'âge à la première grossesse, le statut ménopausique et la densité mammaire avaient des relations significatives avec la catégorie BIRADS finale. CONCLUSION: La mammographie est un élément inestimable des soins du sein dans notre localité. L'évaluation des services mammographiques dans notre ONG privée a montré une prédominance de la mammographie de dépistage, tandis que la majorité des femmes atteintes de maladies mammaires symptomatiques présentaient des résultats normaux et bénins. MOTS-CLÉS: Mammographie, Femmes, Nigeria, Soins du sein, Imagerie mammaire, Organisation non gouvernementale.

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach.

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100µm, a thickness of 500µm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.

INTRODUCTION: 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. MATERIAL AND 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. CONCLUSION: 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. RELEVANCE STATEMENT: 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. KEY POINTS: • 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.

Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US.

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.

A nonparametric measure of contrast in x-ray images.

Objective.We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images.Approach.A relative brightness distanceδis calculated by making use of the order statistic of the pixel values. By multiplyingδwith the grey value rangeR, the mean brightness distance MBD is obtained. From the MBD, the CED and the distance-to-noise ratio DNR can be derived. The latter is the ratio of the MBD and a previously suggested nonparametric measureτfor the noise. Since the order statistic is independent of the spatial arrangement of the pixel values, the measures can be obtained directly from clinical images. We apply the new measures to mammography images of an anthropomorphic phantom and of a phantom with a step wedge as well as to CT images of a head phantom.Main results.For low-noise images of a step wedge, the MBD is equivalent to the conventional grey value distance. While this measure permits the evaluation of clinical images, it is sensitive to noise. Therefore, noise has to be quantified at the same time. When the ratioσ/τof the noise standard deviationσtoτis available, validity limits for the CED as a measure of contrast can be established. The new figures of merit can be calculated for entire images as well as on regions of interest (ROI) with an edge length not smaller than 32 px.Significance.The new figures of merit are suited to quantify the quality of clinical images without relying on the assumption of a linear, shift-invariant system. They can be used for any kind of greyscale image, provided the ratioσ/τcan be estimated. This will hopefully help to achieve the optimisation of image quality vs dose required by radioprotection laws.

Evaluating the impact of delayed-phase imaging in Contrast-Enhanced Mammography on breast cancer staging: A comparative study of abbreviated versus complete protocol.

PURPOSE: Contrast-enhanced mammography (CEM) is an innovative imaging tool for breast cancer detection, involving intravenous injection of a contrast medium and the assessment of lesion enhancement in two phases: early and delayed. The aim of the study was to analyze the topographic concordance of lesions detected in the early- versus delayed phase acquisitions. MATERIALS AND METHODS: Approved by the Ethics Committee (No. 118/20), this prospective study included 100 women with histopathological confirmed breast neoplasia (B6) at the Radiodiagnostics Department of the Maggiore della Carità Hospital of Novara, Italy from May 1, 2021, to October 17, 2022. Participants underwent CEM examinations using a complete protocol, encompassing both early- and delayed image acquisitions. Three experienced radiologists blindly analyzed the CEM images for contrast enhancement to determine the topographic concordance of the identified lesions. Two readers assessed the complete study (protocol A), while one reader assessed the protocol without the delayed phase (protocol B). The average glandular dose (AGD) of the entire procedure was also evaluated. RESULTS: The analysis demonstrated high concordance among the three readers in the topographical identification of lesions within individual quadrants of both breasts, with a Cohen's κ > 0.75, except for the lower inner quadrant of the right breast and the retro-areolar region of the left breast. The mean whole AGD was 29.2 mGy. The mean AGD due to CEM amounted to 73% of the whole AGD (21.2 mGy). The AGD attributable to the delayed phase of CEM contributed to 36% of the whole AGD (10.5 mGy). CONCLUSIONS: As we found no significant discrepancy between the readings of the two protocols, we conclude that delayed-phase image acquisition in CEM does not provide essential diagnostic benefits for effective disease management. Instead, it contributes to unnecessary radiation exposure.

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals.

BACKGROUND: The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs). METHODS: Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted. RESULTS: Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(ß) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(ß) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(ß) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(ß) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(ß) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05). CONCLUSIONS: TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.

Breast cancer screening patterns and associated factors in Iranian women over 40 years.

Screening is a key component of breast cancer early detection programs that can considerably reduce relevant mortality rates. The purpose of this study was to determine the breast cancer screening behavioral patterns and associated factors in women over 40 years of age. In this descriptive­analytical cross­sectional study, 372 over 40 years of age women visiting health centers in Tabriz, Iran, in 2023 were enrolled using cluster sampling. The data were collected using the sociodemographic characteristics questionnaire, breast cancer perception scale, health literacy for Iranian adults scale, and the Breast Cancer Screening Behavior Checklist. The obtained data were analyzed in SPSS version 16 using descriptive statistics (frequency, percentage, mean, and standard deviation) and inferential statistics (univariate and multivariate logistic regression analyses). In total, 68.3% of all participants performed breast self­examination (BSE) (9.9% regularly, once per month), 60.2% underwent clinical breast examination (CBE) (8.9% regularly, twice per year), 51.3% underwent mammography (12.3% regularly, once per year), and 36.2% underwent sonography (3.8% regularly, twice per year). The findings also showed that women with benign breast diseases were more likely to undergo CBE (OR = 8.49; 95% CI 2.55 to 28.21; P < 0.001), mammography (OR = 8.84; 95% CI 2.98 to 10; P < 0.001), and sonography (OR = 18.84; 95% CI 6.40 to 53.33; P < 0.001) than others. Participants with low and moderate breast cancer perception scores were more likely to perform BSE than women with high breast cancer perception scores (OR = 2.20; 95% CI 1.21 to 4.00; P = 0.009) and women who had a history of benign breast disease were more likely to perform screening behaviors than others (OR = 2.47; 95% CI 1.27 to 4.80; P = 0.008). Women between the ages of 50 and 59 were more likely to undergo mammography (OR = 2.33; 95% CI 1.29 to 4.77; P = 0.008) and CBE (OR = 2.40; 95% CI 1.347 to 4.20; P = 0.003) than those ≥ 60 years. Given the low participation of women in regular breast cancer screening, it is suggested that health care providers highlight the need for screening at the specified intervals in their training programs. In addition, health authorities are recommended to use reminder systems to remind women, especially those over 40 years of age, of the best time for breast screening. Moreover, health care providers must seek to improve breast cancer knowledge, attitudes, and perceptions of women who visit health centers, which are the first level of contact with the healthcare system for the general population.