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.
OBJECTIVE: To document the mammographic findings of adult females in a private NGO and assess the benefits of mammography practice in our locality.
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.
RESULTS: 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.
CONCLUSIONS: 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.
BACKGROUND: 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.
OBJECTIVE: Documenter les résultats mammographiques des femmes adultes dans une ONG privée et évaluer les avantages de la pratique de la mammographie dans notre localité.
UNASSIGNED: 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.
UNASSIGNED: 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.
CONCLUSIONS: 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.
UNASSIGNED: Mammographie, Femmes, Nigeria, Soins du sein, Imagerie mammaire, Organisation non gouvernementale.

BACKGROUND: Digital breast tomosynthesis (DBT) may improve sensitivity in population screening. However, evidence is currently limited on the performance of DBT in patients at a higher risk of breast cancer. This systematic review compares the clinical effectiveness and cost-effectiveness of DBT, digital mammography (DM), and ultrasound, for breast cancer detection in women with dense breasts and additional risk factors.
METHODS: Medline, Embase, and Evidence-Based Medicine Reviews via OvidSP were searched to identify literature from 2010 to August 21, 2023. Selection of studies, data extraction, and quality assessment (using QUADAS-2 and CHEERS) were completed in duplicate. Findings were summarised descriptively and narratively.
RESULTS: Twenty-six studies met pre-specified inclusion criteria. In women with breast symptoms or recalled for investigation of screen-detected findings (19 studies), DBT may be more accurate than DM. For example, in symptomatic women, the sensitivity of DBT + DM ranged from 82.8 % to 92.5 % versus 56.8 %-81.3 % for mammography (DM/synthesised images). However, most studies had a high risk of bias due to participant selection. Evidence regarding DBT in women with a personal or family history of breast cancer, for DBT versus ultrasound alone, and cost-effectiveness of DBT was limited.
CONCLUSIONS: In women with dense breasts and additional risk factors for breast cancer, evidence is limited about the accuracy of DBT compared to other imaging modalities, particularly in those with personal or family history of breast cancer. Future research in this population should consider head-to-head comparisons of imaging modalities to determine the relative effectiveness of these imaging tests.
BACKGROUND: PROSPERO registration number CRD42021236470.

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.

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

UNASSIGNED: To evaluate the indications for and results of magnetic resonance imaging (MRI) examinations for breast cancer screening at a cancer center in Brazil.
UNASSIGNED: This was a retrospective observational study, based on electronic medical records, of patients undergoing MRI for breast cancer screening at a cancer center in Brazil.
UNASSIGNED: We included 597 patients between 19 and 82 years of age. The main indications for MRI screening were a personal history of breast cancer, in 354 patients (59.3%), a family history of breast cancer, in 102 (17.1%), and a confirmed genetic mutation, in 67 (11.2%). The MRI result was classified, in accordance with the categories defined in the Breast Imaging Reporting and Data System, as benign (category 1 or 2), in 425 patients (71.2%), probably benign (category 3), in 143 (24.0%), or suspicious (category 4 or 5), in 29 (4.9%). On MRI, 11 malignant tumors were identified, all of which were invasive carcinomas. Among those 11 carcinomas, six (54.5%) were categorized as minimal cancers (< 1 cm), and the axillary lymph nodes were negative in 10 (90.9%). The cancer detection rate was 18.4/1,000 examinations, and the positive predictive value for suspicious lesions submitted to biopsy was 37.9%.
UNASSIGNED: In our sample, the main indication for breast MRI screening was a personal history of breast cancer. The results indicate that MRI is a highly accurate method for the early detection of breast neoplasms in this population.
UNASSIGNED: Avaliar as indicações e resultados de exames de ressonância magnética (RM) para rastreamento de câncer de mama em um centro oncológico no Brasil.
UNASSIGNED: Estudo observacional, realizado mediante análise retrospectiva de pacientes submetidos a RM das mamas para rastreamento de câncer de mama, por meio de revisão do prontuário eletrônico em um centro oncológico.
UNASSIGNED: Foram incluídas 597 pacientes com idade variando de 19 a 82 anos. As principais indicações para rastreamento foram história pessoal de câncer de mama em 354 (59,3%) pacientes, história familiar em 102 (17,1%) e mutação genética confirmada em 67 (11,2%). O resultado da RM foi benigno (BI-RADS 1 ou 2) em 425 (71,2%) pacientes, provavelmente benigno (BI-RADS 3) em 143 (24,0%) e suspeito (BI-RADS 4 ou 5) em 29 (4,9%). Foram identificados 11 tumores malignos na RM, todos carcinomas invasivos, porcentagem de cânceres “mínimos” (< 1 cm) de 54,5% e porcentagem de axila negativa de 90,9%. A taxa de detecção de câncer na RM foi 18,4/1000 exames e o valor preditivo positivo para as lesões suspeitas submetidas a biópsia foi 37,9%.
UNASSIGNED: A principal indicação para RM de rastreamento na nossa população foi história pessoal de câncer de mama. Os resultados mostraram que a RM constitui um método com alta acurácia para detecção precoce de neoplasias da mama nessa população.

OBJECTIVE: There are several breast cancer (BC) risk factors-many related to body composition, hormonal status, and fertility patterns. However, it is not known if risk factors in healthy women are associated with specific mammographic features at the time of BC diagnosis. Our aim was to assess the potential association between pre-diagnostic body composition and mammographic features in the diagnostic BC image.
METHODS: The prospective Malmö Diet and Cancer Study includes women with invasive BC from 1991 to 2014 (n = 1116). BC risk factors at baseline were registered (anthropometric measures, menopausal status, and parity) along with mammography data from BC diagnosis (breast density, mammographic tumor appearance, and mode of detection). We investigated associations between anthropometric measures and mammographic features via logistic regression analyses, yielding odds ratios (OR) with 95% confidence intervals (CI).
RESULTS: There was an association between high body mass index (BMI) (≥ 30) at baseline and spiculated tumor appearance (OR 1.370 (95% CI: 0.941-2.010)), primarily in women with clinically detected cancers (OR 2.240 (95% CI: 1.280-3.940)), and in postmenopausal women (OR 1.580 (95% CI: 1.030-2.440)). Furthermore, an inverse association between high BMI (≥ 30) and high breast density (OR 0.270 (95% CI: 0.166-0.438)) was found.
CONCLUSIONS: This study demonstrated an association between obesity and a spiculated mass on mammography-especially in women with clinically detected cancers and in postmenopausal women. These findings offer insights on the relationship between risk factors in healthy women and related mammographic features in subsequent BC.
CONCLUSIONS: With increasing numbers of both BC incidence and women with obesity, it is important to highlight mammographic findings in women with an unhealthy weight.
CONCLUSIONS: Women with obesity and BC may present with certain mammographic features. Spiculated masses were more common in women with obesity, especially postmenopausal women, and those with clinically detected BCs. Insights on the relationship between obesity and related mammographic features will aid mammographic interpretation.

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.

Extramammary metastases are uncommon and usually related to a poor prognosis, but the radiologist can suspect the diagnosis based on the patient\'s clinical history and specific imaging findings. Several imaging procedures may be used to evaluate breast metastases from different extramammary malignancies, including mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography-CT (PET-CT). The clinical and imaging presentation of these metastases is contingent upon how the illness spreads, however, they have the potential to resemble either benign or malignant breast tumors. Metastases that disseminate hematologically tend to appear as a single round or oval mass with circumscribed margins. Sonographically, they are usually hypoechoic, and with CT or MRI, they usually enhance. Lymphatic dissemination, for example, frequently reveals significant asymmetry with skin thickening and diffuse breast edema, which is compatible with an inflammatory breast carcinoma. Knowing the many types of cancers that have the potential to spread to the breast as well as being able to accurately diagnose them is crucial to prevent a needless mastectomy and provide guidance for subsequent treatment. The purpose of this article is to provide a better understanding of the imaging features and immunohistochemistry (IHC) of secondary tumors of the breast by presenting eight distinctive cases, which will enable radiologists to recognize this entity.

OBJECTIVE: To determine the optimal angular range (AR) for digital breast tomosynthesis (DBT) systems that provides highest lesion visibility across various breast densities and thicknesses.
METHODS: A modular DBT phantom, consisting of tissue-equivalent adipose and glandular modules, along with a module embedded with test objects (speckles, masses, fibers), was used to create combinations simulating different breast thicknesses, densities, and lesion locations. A prototype DBT system operated at four ARs (AR±7.5°, AR±12.5°, AR±19°, and AR±25°) to acquire 11 projection images for each combination, with separate fixed doses for thin and thick combinations. Three blinded radiologists independently assessed lesion visibility in reconstructed images; assessments were averaged and compared using linear mixed models.
RESULTS: Speckle visibility was highest with AR±7.5° or AR±12.5°, decreasing with wider ARs in all density and thickness combinations. The difference between AR±7.5° and AR±12.5° was not statistically significant, except for the tube-side speckles in thin-fatty combinations (5.83 [AR±7.5°] vs. 5.39 [AR±12.5°], P = 0.019). Mass visibility was not affected by AR in thick combinations, while AR±12.5° exhibited the highest mass visibility for both thin-fatty and thin-dense combinations (P = 0.032 and 0.007, respectively). Different ARs provided highest fiber visibility for different combinations; however, AR±12.5° consistently provided highest or comparable visibility. AR±12.5° showed highest overall lesion visibility for all density and thickness combinations.
CONCLUSIONS: AR±12.5° exhibited the highest overall lesion visibility across various phantom thicknesses and densities using a projection number of 11.

Ipsilateral axillary adenopathy post-COVID mRNA vaccine has been widely reported and guidelines for management have been established. Isolated changes of axillary tail trabecular thickening without associated adenopathy in the breast present a diagnostic dilemma and no official guidelines have thus far been reported. This finding has been reported after COVID mRNA vaccine and has never been reported with any other vaccine. We report on a patient with such changes on screening mammography 1.5 months after the fifth dose of a COVID-mRNA vaccine and 1 week after RSV vaccine. This raises the possibility that such changes can be seen with vaccines other than the COVID mRNA series of vaccines. The main differential diagnosis includes mastitis and inflammatory breast cancer. The transient nature of this finding with spontaneous resolution at diagnostic mammography and the vaccination history helps to establish the diagnosis and exclude breast cancer.