Complex sclerosing lesion (CSL)/radial scar of breast is a benign entity that can pose a diagnostic challenge due to resemblance to breast carcinoma on imaging. Hamartoma are uncommon benign tumors, composed of disorganized mixture of glandular, fibrous, and adipose tissues, which can exhibit classical imaging characteristics. Here we describe a case of concomitant CSL and hamartoma in left beast, of which CSL presented as suspicious mass on imaging but was ultimately confirmed to be benign on histopathology with 4 years of documented stability.

OBJECTIVE: This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS).
METHODS: In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions.
RESULTS: Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements.
CONCLUSIONS: This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

UNASSIGNED: Breast cysts encompass a variety of pathologies, both benign and malignant. Therefore, classifying cysts into different categories is needful to develop a management algorithm. This study aimed to describe and distinguish between simple, complicated and complex cysts; and compare the final BIRADS assessment with pathologic findings.
UNASSIGNED: A 5-year retrospective review of our ultrasound database identified two hundred and seventy patients with cystic breast lesions. They were divided into simple, complicated and complex cysts according to ultrasound characteristics based on shape, orientation, margin, wall thickness, internal features (echogenicity, septa, mass) posterior acoustic features, surrounding tissue vascularity. The final BIRADS assessment was correlated with histological findings.
UNASSIGNED: There were two hundred and sixty-six (98.5%) females and four (1.5%) males with a mean age 34.9 ± 11.8 years. The commonest presentation was a palpable mass, in 70% of the patients. There were 89 (33.0%) simple cysts, 61 (22.6%) complicated cysts and 120 (44.4%) complex cysts.
UNASSIGNED: Majority of the breast cysts (83%) were benign with overall 17% incidence of malignancy. Complex cysts were the most frequent cyst type in our study, it is also the category most frequently associated with breast cancer, obviating the need for histology.

Dirofilaria, commonly known as heartworm, is a parasitic nematode that primarily infects canines. However, human infections have been reported and can present as subcutaneous nodules in different parts of the body. We present a case of a 43-year-old female who presented with a breast lump that was ultimately diagnosed as a Dirofilaria infection, a rare occurrence in humans. This case report shows that considering parasites in unusual presentations is of utmost importance, especially in regions known to have a high prevalence of such infections.

(1) Background: Breast core needle biopsy (CNB) is preferred over fine needle aspiration (FNA) as it has higher sensitivity and specificity and enables immunohistochemical evaluation. However, breast FNA remains widely used because of its low cost, minimally invasive nature, and quick results. Studies analyzing the effects of each test on the prognoses of patients with breast cancer are scarce and controversial, and the criteria for test selection remain unknown. (2) Methods: This study included adult female patients who underwent breast cancer surgery at 102 general hospitals. The trend of breast biopsies over time was analyzed, and the prognoses of patients with breast cancer who underwent CNB and FNA were compared. (3) Results: This study included 73,644 patients who underwent FNA (n = 8027) and CNB (n = 65,617). A multivariate Cox regression analysis showed that patients diagnosed using FNA had significantly worse overall survival (OS) and breast-cancer-specific survival (BCSS) than those diagnosed using CNB. In the subgroup analysis, patients with breast imaging reporting and data system (BI-RADS) 5 lesions, palpable tumors, or centrally located tumors had significantly worse OS and BCSS with FNA than with CNB. (4) Conclusions: CNB should be performed preferentially instead of FNA in patients with BI-RADS 5 lesions and nonpalpable or centrally located tumors.

UNASSIGNED: Ultrasound-detected breast lesions with probably benign features are a great challenge for clinicians, especially in breasts with dense composition. We aimed to investigate the finding of two radiologic modalities on these lesions.
UNASSIGNED: This retrospective cross-sectional study recruited patients including (1) candidates of assisted reproductive therapy (ART), (2) patients with prior high-risk lesions, and (3) the \"suspected\" BIRADS-3 masses referring to masses that US BIRADS-3 was not compatible with the clinical breast exam. The degree of agreement in diagnosing BIRADS-3 lesions between two modalities of magnetic resonance imaging (MRI) and ultrasonography (US), and comparison of the lesions in US and MRI were the study variables.
UNASSIGNED: A total number of 123 lesions in 67 patients with a median age of 38 (IQR: 11, range: 17-67). In the examination by MRI, 107 (87.0 %) lesions were BIRADS-3 indicating the agreement level between these two modalities. The median size of the lesions in US was 9 mm (IQR: 5, range: 3-43) and 9 mm (IQR: 10, range: 4-46) in MRI. The measured size of the lesions between the two modalities was highly correlated (Spearman correlation coefficient: 0.889, P-value < 0.001). MRI evaluation revealed two cases of deep lesions which were missed in the US imaging.
UNASSIGNED: This study found relatively high agreement values between US and MRI in detecting BIRADS-3 breast lesions in candidates for ART or patients with prior high-risk lesions. Also, MRI could downgrade about one-tenth of the cases to a lower BIRADS level and resolved the need for closer follow-up.

OBJECTIVE: To identify biopsy rates and indications for BI-RADS 3 lesions in a large cohort of patients and compare with follow-up compliance and malignancy outcomes.
METHODS: We retrospectively reviewed all BI-RADS category-3 lesions seen on mammography and/or ultrasound between 2013 and 2015. Patient age, lesion size, follow-up rates at 6-, 12-, and 24-months were collected. Biopsy timing, indication, and outcomes (malignant vs benign) were recorded using at least 2-year follow-up or biopsy pathology as endpoint.
RESULTS: Of 2319 BI-RADS 3 lesions in 2075 women analyzed, biopsy was performed in 173 (7.5%). Most biopsies were performed upfront (99, 57.2%), followed by at 6 (44, 25.4%), 12 (21, 12.1%), and 24-month follow-up (9, 5.2%; P < .001). Palpable (P < .001) and larger (median 1.4 vs 1.0 cm, P < .001) lesions in women <40 years (15.2% vs 4.8%, P < .001) were more likely to undergo biopsy. Most biopsies were prompted by patient/physician desire (64.5%, P < .001). Of 783 lesions with available endpoint, 5 (0.6%) were cancer. All cancers were identified either at presentation (in 0-5 months, n = 1) or 6-month follow-up (in 5-9 months, n = 4) with biopsy prompted by either morphology change (n = 3) or lesion growth (n = 2). Of the 1855 lesions which were expected for follow up, only 310 (16.7%) underwent all follow-ups, while 482 (26.1%) had two, 489 (26.5%) one, and 565 (30.6%) had no follow-up.
CONCLUSIONS: In our cohort, BI-RADS category 3 lesions had significantly higher biopsy rates compared with the small malignancy rate, all of which were identified at baseline or first follow-up. Overall patient follow-up compliance low. Imaging follow-up, especially at first 6-month time point, should be encouraged in BI-RADS 3 lesions, instead of upfront biopsies.

OBJECTIVE: Automatic classification and segmentation of tumors in breast ultrasound images enables better diagnosis and planning treatment strategies for breast cancer patients.
METHODS: We collected 953 breast ultrasound images from two open-source datasets and classified them with help of an expert radiologist according to BI-RADS criteria. The data was split into normal, benign and malignant classes. We then used machine learning to develop classification and segmentation algorithms.
RESULTS: We found 3.92% of the images across the open-source datasets had erroneous classifications. Post-radiologist intervention, three algorithms were developed based on the classification categories. Classification algorithms distinguished images with healthy breast tissue from those with abnormal tissue with 96% accuracy, and distinguished benign from malignant images with 85% accuracy. Both algorithms generated robust F1 and AUROC metrics. Finally, the masses within images were segmented with an 80.31% DICE score.
CONCLUSIONS: Our work illustrates the potential of deep learning algorithms to improve the accuracy of breast ultrasound assessments and to facilitate automated assessments.

Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.

UNASSIGNED: Promoting breast cancer (BC) detection in women by means of mammography is a viable strategy to reduce the number of diagnoses at clinically advanced stages and mortality.
UNASSIGNED: To describe the results reported by mammography studies in women, carried out nationally during 2013-2017, and to analyze the spatiotemporal trend of Breast Imaging Reporting and Data System (BIRADS) categories suggestive of malignancy by State.
UNASSIGNED: Longitudinal, analytical design that included information on mammography studies of women according to age group (< 40 and ≥ 40), evaluated in units of the Ministry of Health of Mexico during 2013-2017. The frequency of BIRADS categories and a standardized rate suggestive of malignancy (categories 4 and 5) were estimated in women aged ≥ 40 years, and spatial statistics were used to analyze the trend by State.
UNASSIGNED: A total of 3,659,151 mammograms were analyzed, 98.5 % in women aged ≥ 40 years. The malignancy-suggestive rate decreased from 38.3 (2013) to 31 (2017) per 100,000 women aged ≥ 40 years; however, the risk of detection increased up to 13 times in ten States.
UNASSIGNED: Although the risk of detection in categories suggestive of malignancy decreased at the national level, some States need to reinforce the application of BC detection programs through mammography and increase the participation of the target population.
UNASSIGNED: Promover la detección de cáncer de mama (CaMa) en mujeres mediante mastografía es una estrategia viable para disminuir los diagnósticos en fases clínicamente avanzadas y la mortalidad.
UNASSIGNED: Describir los resultados reportados por estudios de mastografía en mujeres realizados a nivel nacional durante 2013-2017 y analizar la tendencia espaciotemporal de categorías BIRADS (Breast Imaging Reporting and Data System) sugestivas de malignidad por Estado.
UNASSIGNED: Diseño analítico longitudinal que incluyó información sobre estudios de mastografía de mujeres según grupo de edad (< 40 e ≥ 40), valoradas en unidades de la Secretaría de Salud, México, durante 2013-2017. Se estimó la frecuencia de categorías según BIRADS, tasa estandarizada sugestiva de malignidad (categorías 4 y 5) en mujeres ≥ 40 años y se utilizó estadística espacial para analizar la tendencia por Estado.
UNASSIGNED: Se analizaron 3,659,151 mastografías, el 98.5 % en mujeres ≥ 40 años. La tasa sugestiva de malignidad disminuyó de 38.3 (2013) a 31 (2017) por 100 mil mujeres ≥ 40 años; sin embargo, el riesgo de detección aumentó hasta 13 veces en diez Estados.
UNASSIGNED: Aunque el riesgo de detección en categorías sugestivas de malignidad disminuyó a nivel nacional, algunos Estados requieren reforzar la aplicación de programas de detección del CaMa mediante mastografía e incrementar la participación de la población blanco.