One of the biggest and heaviest juvenile bilateral breast fibroadenoma: A case report.

INTRODUCTION: Giant juvenile fibroadenomas occurring at a mean age of 13 may be multiple and bilateral, accounting for approximately 0.5 % of all fibroadenomas. The pathogenesis of these tumors is closely linked to hormonal changes during puberty, characterized by increased estrogen stimulation, heightened estrogen receptor sensitivity, and reduced estrogen antagonists. These hormonal factors are pivotal in the rapid growth and substantial size observed in giant juvenile fibroadenomas. CASE PRESENTATION: An adolescent girl presented at the outpatient clinic with significant bilateral breast enlargement, causing redness and discomfort when sitting, leading to difficulty wearing age-appropriate clothing and chest wall pressure. Despite previous consultations attributing the condition to genetic causes, further investigation via radiological ultrasound indicated a probable diagnosis of bilateral breast mass fibroadenoma, occupying a substantial portion of the breast tissue. Consequently, the decision was made to perform bilateral breast surgery to remove the giant masses for histopathological analysis. DISCUSSION: The delayed diagnosis accentuated the case's complexity, highlighting the challenges in effectively identifying and managing giant fibroadenomas in adolescents. Despite the initial absence of alarming symptoms, these fibroadenomas' sheer size and impact underscored the importance of early detection and comprehensive evaluation in similar clinical presentations. CONCLUSION: The surgeon must emphasize meticulous planning when deciding on the surgical approach for removing a giant juvenile fibroadenoma. This planning is crucial for preserving breast functionality, achieving a satisfactory cosmetic outcome, and addressing the psychological distress of young patients. Early detection and excision are imperative to safeguard breast tissue.

Shear wave elastography for solid breast masses evaluation: Quantitative measurement of mean elasticity value and elasticity ratio.

Purpose: Shear wave elastography (SWE), an ultrasonographic technique to measure the elasticity of mass lesions to evaluate breast mass. This study aimed to find out the cutoff values identifying breast malignancy using the mean elasticity (E-mean) and elasticity ratio (E-ratio) of breast masses. Methods: This retrospective study included women underwent SWE and US-guided biopsy of breast masses. During conventional US, the SWE mode was also performed, determining elasticity measurements, E-mean and E-ratio. Histopathological reports were obtained to identify mass status. The optimal and alternative cutoff values for E-mean and E-ratio to determine malignancy were assessed by receiver operating characteristic (ROC) curve analysis and Youden's index score. Results: Among 147 benign and 93 malignant masses, the median of E-means were 26.20 (IQR 15.70-56.60) and 141.60 (IQR 119.80-154.60) kPa and the median E-ratios were 3.11 (IQR 1.83-5.23) and 9.24 (IQR 6.76-12.44), respectively. Using Youden's index, the optimal cutoff values for E-mean and E-ratio were 90.35 and 5.89, with sensitivity of 87.1 % and 82.8 %, specificity of 89.1 % and 83.7 %, positive predictive value (PPV) of 83.5 % and 76.2 %, negative predictive value (NPV) of 91.6 % and 88.5 %, positive likelihood ratio (LR+) of 8.00 and 5.07, and negative likelihood ratio (LR-) of 0.14 and 0.21, respectively. Conclusion: This study revealed that SWE is useful in predicting malignancy. With the optimal cutoff values of E-mean and E-ratio at 90.35 kPa and 5.89, the sensitivity was nearly 90 % with E-mean and slightly over 80 % with E-ratio, respectively. These findings could be used in conjunction with conventional US.

Benign Adenomyoepithelioma of the Breast: A Case Report and Review of Imaging Features.

Adenomyoepithelioma (AME) of the breast is a rare tumor that can be benign or malignant and has varied morphological features. We report a case of a 62-year-old female with a history of right breast cancer who presented with abnormal screening mammography. The detection, presentation, and varied imaging characteristics of AMEs are discussed. The nonspecific imaging and histologic appearance of AME are highlighted, emphasizing the need for representative biopsy samples and histopathological review for diagnosis. Our case underlines the importance of wide surgical excision with negative margins in the presence of diagnostic uncertainty, which corresponds with the current recommended treatment for AME to prevent recurrence.

MFMSNet: A Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network for breast ultrasound image segmentation.

Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of breast cancer. Recently, existing methods have mainly focused on spatial domain implementations, with less attention to the frequency domain. In this paper, we propose a Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network (MFMSNet). Specifically, we utilize Octave convolutions instead of conventional convolutions to effectively separate high-frequency and low-frequency components while reducing computational complexity. Introducing the Multi-frequency Transformer block (MF-Trans) enables efficient interaction between high-frequency and low-frequency information, thereby capturing long-range dependencies. Additionally, we incorporate Multi-scale interactive fusion module (MSIF) to merge high-frequency feature maps of different sizes, enhancing the emphasis on tumor edges by integrating local contextual information. Experimental results demonstrate the superiority of our MFMSNet over seven state-of-the-art methods on two publicly available breast ultrasound datasets and one thyroid ultrasound dataset. In the evaluation of MFMSNet, tests were conducted on the BUSI, BUI, and DDTI datasets, comprising 130 images (BUSI), 47 images (BUI), and 128 images (DDTI) in the respective test sets. Employing a five-fold cross-validation approach, the obtained dice coefficients are as follows: 83.42 % (BUSI), 90.79 % (BUI), and 79.96 % (DDTI). The code is available at https://github.com/wrc990616/MFMSNet.

Breasts as a perceived barrier to physical activity in Mexican women: A cross-sectional study.

BACKGROUND: Scarce evidence exists on barriers to physical activity in Mexican women. Despite evidence from other countries, no research has investigated the influence of the breast on PA in this population. OBJECTIVE: To determine the association between the breast and physical activity in Mexican women. DESIGN: Cross-sectional observational study. METHODS: Volunteers were 279 Mexican women from Veracruz, Durango, and Baja California states, who completed a paper survey of their demographics, brassiere characteristics, breast pain, and frequency and amounts of weekly physical activity. RESULTS: The first barrier to physical activity was time constraints, followed by breast-related issues. Breast pain was reported by 47.1% of women, and the breast as a barrier to physical activity participation was reported by 30.6%. Responses, such as "I am embarrassed by excessive breast movement" and "My breasts are too big" were the most frequently reported breast-related barriers to physical activity. Breast pain was associated with the menstrual cycle and exercise. Breast health knowledge and pain intensity were unrelated to moderate- and vigorous-intensity physical activity. The 36.4% and 6.7% of women did not meet weekly moderate- and vigorous-intensity physical activity guidelines, respectively. Weekly moderate- and vigorous-intensity physical activity was similar between women reporting breast pain and those who did not. CONCLUSIONS: Because the breast was the second most significant barrier to physical activity, it is imperative to increase breast health knowledge in Mexican women to reduce impediments to physical activity.

Perception of Mexican women regarding their breasts as a barrier to physical activityPhysical activity provides numerous health benefits, sometimes associated with reversing or delaying several diseases. However, barriers to increasing physical activity in women remain, as the breast is an anatomical aspect that is unique to women. Breast pain has been reported in more than 50% of women who perform physical exercise. Therefore, the study aimed to determine the associations between breast characteristics and barriers to physical activity in Mexican women. Two hundred and seventy-nine women from three Mexican states voluntarily participated in the study. They answered survey questions on the history of bra use, barriers to physical activity, and essential demographic characteristics. The main findings of this study were that issues related to the breasts were reported as the second barrier to physical activity participation. In addition, time constraints were reported as the main reason impeding physical activity participation. Public health initiatives should support attempts to increase breast satisfaction among women of all breast sizes to stimulate engagement in physical activity throughout their lives.

IgG4-related mastitis managed without excision or steroid therapy.

IgG4-related mastitis is an extremely rare IgG4-related sclerosing disease involving the breast that must be differentiated from breast cancer. There is currently no consensus regarding the optimal treatment strategies. Here, we report a case of IgG4-related mastitis followed up without excision or steroid therapy. Although the association between IgG4-related mastitis and breast cancer remains unclear, regular follow-up imaging and measurement of serum concentrations of disease activity markers may allow for follow-up without excision or steroid therapy.

Emergency Resection of a Bleeding Ruptured Malignant Phyllodes Tumor of the Breast.

Ruptured phyllodes tumors, though extremely rare, can necessitate emergency surgery in certain cases, particularly those with active bleeding. A 51-year-old woman presented to our hospital with a newly identified mass in her right breast that developed over the past two months. The tumor had ruptured through the paramammary nipple. While initially diagnosed with a phyllodes tumor and scheduled for elective surgery, she experienced active bleeding from the ruptured tumor, leading to a drop in hemoglobin levels. An emergency right simple mastectomy was performed to control the bleeding. Postoperatively, no complications or recurrences were observed. Phyllodes tumors, which are characterized by rapid growth, may present with active bleeding following rupture and may require emergency surgery.

Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis.

Purpose:In the last two decades, computer-aided detection and diagnosis (CAD) systems have been created to help radiologists discover and diagnose lesions observed on breast imaging tests. These systems can serve as a second opinion tool for the radiologist. However, developing algorithms for identifying and diagnosing breast lesions relies heavily on mammographic datasets. Many existing databases do not consider all the needs necessary for research and study, such as mammographic masks, radiology reports, breast composition, etc. This paper aims to introduce and describe a new mammographic database. Methods:The proposed dataset comprises mammograms with several lesions, such as masses, calcifications, architectural distortions, and asymmetries. In addition, a radiologist report is provided, describing the details of the breast, such as breast density, description of abnormality present, condition of the skin, nipple and pectoral muscles, etc., for each mammogram. Results:We present results of commonly used segmentation framework trained on our proposed dataset. We used information regarding the class of abnormalities (benign or malignant) and breast tissue density provided with each mammogram to analyze the segmentation model's performance concerning these parameters. Conclusion:The presented dataset provides diverse mammogram images to develop and train models for breast cancer diagnosis applications.

The utility of quantifying the orientation of breast masses in ultrasound imaging.

The aim of this study was to quantify the orientation of breast masses and determine whether it can enhance the utility of a not parallel orientation in predicting breast mass malignancy. A total of 15,746 subjects who underwent breast ultrasound examinations were initially enrolled in the study. Further evaluation was performed on subjects with solid breast masses (≤ 5 cm) intended for surgical resection and/or biopsy. The orientation angle, defined as the acute angle between the align of the maximal longitudinal diameter of the breast mass and the surface of the breast skin, was measured. Receiver operating characteristic (ROC) curve analysis was conducted, and various performance measures including sensitivity, specificity, positive and negative predictive values, accuracy, odds ratio, and the area under the ROC curve (AUC) were calculated. Multivariate analysis was performed to determine if the orientation angle was an independent predictor of breast malignancy. Decision curve analysis (DCA) was also conducted to assess the net benefit of adopting the orientation angle for predicting breast mass malignancy. The final analysis included 83 subjects with breast cancer and 135 subjects with benign masses. The intra-group correlation coefficient for the measurement of the orientation angle of breast masses was 0.986 (P = 0.001), indicating high reproducibility. The orientation angles of malignant and benign breast masses were 36.51 ± 14.90 (range: 10.7-88.6) degrees and 15.28 ± 8.40 (range: 0.0-58.7) degrees, respectively, and there was a significant difference between them (P < 0.001). The cutoff value for the orientation angle was determined to be 22.9°. The sensitivity, specificity, positive and negative predictive values, accuracy, odds ratio, and AUC for the prediction of breast malignancy using the orientation angle were 88.0%, 87.4%, 81.1%, 92.2%, 87.6%, 50.67%, and 0.925%, respectively. Multivariate analysis revealed that the orientation angle (> 22.9°), not circumscribed margin, and calcifications of the breast mass were independent factors predicting breast malignancy. The net benefit of adopting the orientation angle for predicting breast malignancy was 0.303. Based on these findings, it can be concluded that quantifying the orientation angle of breast masses is useful in predicting breast malignancy, as it demonstrates high sensitivity, specificity, AUC, and standardized net benefit. It optimizes the utility of the not parallel orientation in assessing breast mass malignancy.

Predictive value of contrast-enhanced ultrasonography and ultrasound elastography for management of BI-RADS category 4 nonpalpable breast masses.

PURPOSE: The objective of this study was to investigate the independent risk factors and associated predictive values of contrast-enhanced ultrasound (CEUS), shear wave elastography (SWE), and strain elastography (SE) for high-risk lesions (HRL) and malignant tumors (MT) among nonpalpable breast masses classified as BI-RADS category 4 on conventional ultrasound. METHODS: This prospective study involved consecutively admitted patients with breast tumors from January 2018, aiming to explore the management of BI-RADS category 4 breast tumors using CEUS and elastography. We conducted a retrospective review of patient data, focusing on those with a history of a nonpalpable mass as the primary complaint. Pathologic findings after surgical resection served as the gold standard. The CEUS arterial-phase indices were analyzed using contrast agent arrival-time parametric imaging processing mode, while quantitative and qualitative indices were examined on ES images. Independent risk factors were identified through binary logistic regression multifactorial analysis. The predictive efficacy of different modalities was compared using a receiver operating characteristics curve. Subsequently, a nomogram for predicting the risk of HRL/MT was established based on a multifactorial logistic regression model. RESULTS: A total of 146 breast masses from 146 patients were included, comprising 80 benign tumors, 12 HRLs, and 54 MTs based on the final pathology. There was no significant difference in pathologic size between the benign and HRL/MT groups [8.00(6.25,10.00) vs. 9.00(6.00,10.00), P = 0.506]. The diagnostic efficacy of US plus CEUS exceeded that of US plus SWE/SE for BI-RADS 4 nonpalpable masses, with an AUC of 0.954 compared to 0.798/0.741 (P ï¼œ 0.001). Further stratified analysis revealed a more pronounced improvement for reclassification of BI-RADS 4a masses (AUC: 0.943 vs. 0.762/0.675, P ï¼œ 0.001) than BI-RADS 4b (AUC:0.950 vs. 0.885/0.796, P＞0.05) with the assistance of CEUS than SWE/SE. Employing downgrade CEUS strategies resulted in negative predictive values ranging from 95.2 % to 100.0 % for BI-RADS 4a and 4b masses. Conversely, using upgrade nomogram strategies, which included the independent predictive risk factors of irregular enhanced shape, poor defined enhanced margin, earlier enhanced time, increased surrounding vessels, and presence of contrast agent retention, the diagnostic performance achieved an AUC of 0.947 with good calibration. CONCLUSION: After investigating the potential of CEUS and ES in improving risk assessment and diagnostic accuracy for nonpalpable BI-RADS category 4 breast masses, it is evident that CEUS has a more significant impact on enhancing classification compared to ES, particularly for BI-RADS 4a subgroup masses. This finding suggests that CEUS may offer greater benefits in improving risk assessment and diagnostic accuracy for this specific subgroup of breast masses.

DF-dRVFL: A novel deep feature based classifier for breast mass classification.

Amongst all types of cancer, breast cancer has become one of the most common cancers in the UK threatening millions of people's health. Early detection of breast cancer plays a key role in timely treatment for morbidity reduction. Compared to biopsy, which takes tissues from the lesion for further analysis, image-based methods are less time-consuming and pain-free though they are hampered by lower accuracy due to high false positivity rates. Nevertheless, mammography has become a standard screening method due to its high efficiency and low cost with promising performance. Breast mass, as the most palpable symptom of breast cancer, has received wide attention from the community. As a result, the past decades have witnessed the speeding development of computer-aided systems that are aimed at providing radiologists with useful tools for breast mass analysis based on mammograms. However, the main issues of these systems include low accuracy and require enough computational power on a large scale of datasets. To solve these issues, we developed a novel breast mass classification system called DF-dRVFL. On the public dataset DDSM with more than 3500 images, our best model based on deep random vector functional link network showed promising results through five-cross validation with an averaged AUC of 0.93 and an average accuracy of 81.71%. Compared to sole deep learning based methods, average accuracy has increased by 0.38. Compared with the state-of-the-art methods, our method showed better performance considering the number of images for evaluation and the overall accuracy.

Angiolipoma an Uncommon Breast Mass in Men: A Case Series.

Angiolipomas are uncommon benign masses of the breast which are rarely described in the male breast. They do not have a typical mammographic appearance and can present with concerning features such as microcalcifications or irregular borders. Ultrasound is helpful in evaluating these masses most commonly appearing as oval, circumscribed, and hyperechoic. Clinical, radiological, and pathological information needs to be carefully evaluated as angiolipomas can be confused with malignant pathology. Three cases of angiolipomas of the male breast are reported in this study with mammographic, sonographic, and pathologic correlation.

Rare Primary Malignant Melanoma of the Breast Presenting as a Solitary Breast Mass in a 43-Year-Old Male.

Primary malignant melanoma of the breast (PMMB) is an extremely rare lesion that carries a poor prognosis. Therefore, it is crucial to examine the patient's medical history, clinical presentation, and histopathology considering this diagnosis. The rarity of this lesion has made it difficult to identify classic presentations or specific treatment guidelines. Staining for specific biomarkers can be helpful for diagnosis in the absence of melanin pigment on histology. Additional molecular studies to determine gene status can also be useful for targeted immunotherapy and increased survival time for patients. In this paper, we introduce a rare case of PMMB without skin involvement presenting as a solitary breast mass in a male and explore the radiology, histology, evaluation, and treatment options.

Spatially localized sparse approximations of deep features for breast mass characterization.

We propose a deep feature-based sparse approximation classification technique for classification of breast masses into benign and malignant categories in film screen mammographs. This is a significant application as breast cancer is a leading cause of death in the modern world and improvements in diagnosis may help to decrease rates of mortality for large populations. While deep learning techniques have produced remarkable results in the field of computer-aided diagnosis of breast cancer, there are several aspects of this field that remain under-studied. In this work, we investigate the applicability of deep-feature-generated dictionaries to sparse approximation-based classification. To this end we construct dictionaries from deep features and compute sparse approximations of Regions Of Interest (ROIs) of breast masses for classification. Furthermore, we propose block and patch decomposition methods to construct overcomplete dictionaries suitable for sparse coding. The effectiveness of our deep feature spatially localized ensemble sparse analysis (DF-SLESA) technique is evaluated on a merged dataset of mass ROIs from the CBIS-DDSM and MIAS datasets. Experimental results indicate that dictionaries of deep features yield more discriminative sparse approximations of mass characteristics than dictionaries of imaging patterns and dictionaries learned by unsupervised machine learning techniques such as K-SVD. Of note is that the proposed block and patch decomposition strategies may help to simplify the sparse coding problem and to find tractable solutions. The proposed technique achieves competitive performances with state-of-the-art techniques for benign/malignant breast mass classification, using 10-fold cross-validation in merged datasets of film screen mammograms.

Solitary Metastasis to the Breast From Thigh Myxoid Liposarcoma.

Metastasis to the breast from non-mammary malignancies are rare and suggestive of advanced disease. Accurate and prompt diagnosis of breast metastasis can provide important prognostic information and guide treatment planning. Interestingly, in contrast to primary breast malignancies, non-mammary metastatic breast lesions often have benign-appearing imaging characteristics. Knowing a patient's clinical history and having prior breast imaging studies for comparison is important for making accurate assessments and appropriate recommendations. Imaging-guided biopsy is often indicated for definitive tissue diagnosis. We report a rare case of solitary metastasis to the breast from thigh myxoid liposarcoma.

Intelligent Breast Mass Classification Approach Using Archimedes Optimization Algorithm with Deep Learning on Digital Mammograms.

Breast cancer (BC) has affected many women around the world. To accomplish the classification and detection of BC, several computer-aided diagnosis (CAD) systems have been introduced for the analysis of mammogram images. This is because analysis by the human radiologist is a complex and time-consuming task. Although CAD systems are used to primarily analyze the disease and offer the best therapy, it is still essential to enhance present CAD systems by integrating novel approaches and technologies in order to provide explicit performances. Presently, deep learning (DL) systems are outperforming promising outcomes in the early detection of BC by creating CAD systems executing convolutional neural networks (CNNs). This article presents an Intelligent Breast Mass Classification Approach using the Archimedes Optimization Algorithm with Deep Learning (BMCA-AOADL) technique on Digital Mammograms. The major aim of the BMCA-AOADL technique is to exploit the DL model with a bio-inspired algorithm for breast mass classification. In the BMCA-AOADL approach, median filtering (MF)-based noise removal and U-Net segmentation take place as a pre-processing step. For feature extraction, the BMCA-AOADL technique utilizes the SqueezeNet model with AOA as a hyperparameter tuning approach. To detect and classify the breast mass, the BMCA-AOADL technique applies a deep belief network (DBN) approach. The simulation value of the BMCA-AOADL system has been studied on the MIAS dataset from the Kaggle repository. The experimental values showcase the significant outcomes of the BMCA-AOADL technique compared to other DL algorithms with a maximum accuracy of 96.48%.

Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer.

Purpose: This study aims to examine the performance of breast ultrasound with a computer-aided diagnostic (CAD) system in detecting malignant breast cancer compared to conventional ultrasound and investigate the effects on smaller tumor sizes (≤20 mm). Methods: This retrospective analysis included 123 patients with breast masses between March 2021 and July 2023. By using pathology results from biopsies or surgeries as the gold standard, we calculated and compared the diagnostic performances of conventional ultrasound and CAD, including sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve (AUC). A subgroup analysis of masses ≤20 mm in size was performed. Results: Twenty-seven patients were pathologically diagnosed with malignant breast cancer. CAD had a higher specificity (92.71 % vs. 62.5 %) and accuracy (93.5 % vs. 69.92 %) than conventional ultrasound. The AUC of CAD was significantly greater than that of conventional ultrasonography (0.9450 vs. 0.7940, p < 0.0001). The agreement between the CAD and pathology results was almost perfect (kappa = 0.82, p < 0.0001). In patients with masses ≤20 mm, the effect was consistent: CAD had higher specificity (91.43 % vs. 51.43 %), higher accuracy (90.70 % vs. 58.14 %), and a higher AUC (0.8946 vs. 0.6946, p < 0.0001) than conventional ultrasound. Thirty-one downgrades were observed in BI-RADS 4A and 4B based on CAD, all of which were proven to be benign. Conclusion: Compared to conventional breast ultrasound, CAD had better diagnostic performance, with higher specificity, accuracy, and AUC. CAD can help recognize benign lesions, especially in patients with BI-RADS 4A, and avoid unnecessary invasive procedures.

Phyllodes Tumor of the Breast Complicated With Mastitis.

One of the rarest fibro-epithelial neoplasms of the breast during pregnancy is the phyllodes tumor (PT). It is typically a painless, bi-phasic, and rapidly growing neoplasm that resembles fibroadenomas. It is still unclear if the neoplasm is hormone-dependent during pregnancy. It is often challenging to diagnose and treat PT. Herein, we report a case of a 30-year-old female at 31 weeks gestation who was diagnosed with a benign phyllodes tumor of her breast with concurrent mastitis. She was first seen during her third trimester where the neoplasm was around 5 cm as reported by the ultrasound (US) examination. Her biopsy report was suggestive of a PT and she was advised surgery with excision of the tumor margin, but she refused. Ten days after her delivery she presented to the emergency department with a fever and a hard, engorged, erythematous, and tender left breast. She was diagnosed with mastitis of the left breast. She then underwent incision and drainage of the left breast that drained purulent milk; additionally, large necrotic grape-like tissues were removed and were confirmed by the histopathology report as a benign phyllodes tumor of the breast.

Real-World Diagnostic Accuracy of the On-Site Cytopathology Advance Report (OSCAR) Procedure Performed in a Multidisciplinary One-Stop Breast Clinic.

Fine-needle aspiration (FNA) cytology has been widely used for the diagnosis of breast cancer lesions with the objective of differentiating benign from malignant masses. However, the occurrence of unsatisfactory samples and false-negative rates remains a matter of concern. Major improvements have been made thanks to the implementation of rapid on-site evaluation (ROSE) in multidisciplinary and integrated medical settings such as one-stop clinics (OSCs). In these settings, clinical and radiological examinations are combined with a morphological study performed by interventional pathologists. The aim of our study was to assess the diagnostic accuracy of the on-site cytopathology advance report (OSCAR) procedure on breast FNA cytologic samples in our breast OSC during the first three years (April 2004 till March 2007) of its implementation. To this goal, we retrospectively analyzed a series of 1820 breast masses (1740 patients) radiologically classified according to the American College of Radiology (ACR) BI-RADS lexicon (67.6% being either BI-RADS 4 or 5), sampled by FNA and immediately diagnosed by cytomorphology. The clinicoradiological, cytomorphological, and histological characteristics of all consecutive patients were retrieved from the hospital computerized medical records prospectively registered in the central information system. Histopathological analysis and ultrasound (US) follow-up (FU) were the reference diagnostic tests of the study design. In brief, we carried out either a histopathological verification or an 18-month US evaluation when a benign cytology was concordant with the components of the triple test. Overall, histology was available for 1138 masses, whereas 491 masses were analyzed at the 18-month US-FU. FNA specimens were morphologically nondiagnostic in 3.1%, false negatives were observed in 1.5%, and there was only one false positive (0.06%). The breast cancer prevalence was 62%. Diagnostic accuracy measures of the OSCAR procedure with their 95% confidence intervals (95% CI) were the following: sensitivity (Se) = 97.4% (96.19-98.31); specificity (Sp) = 94.98% (92.94-96.56); positive predictive value (PPV) = 96.80% (95.48-97.81); negative predictive value (NPV) = 95.91% (94.02-97.33); positive likelihood ratio (LR+) = 19.39 (13.75-27.32); negative predictive ratio (LR-) = 0.03 (0.02-0.04), and; accuracy = 96.45% (95.42-97.31). The respective positive likelihood ratio (LR+) for each of the four categories of cytopathological diagnoses (with their 95% CI) which are malignant, suspicious, benign, and nondiagnostic were 540 (76-3827); 2.69 (1.8-3.96); 0.03 (0.02-0.04); and 0.37 (0.2-0.66), respectively. In conclusion, our study demonstrates that the OSCAR procedure is a highly reliable diagnostic approach and a perfect test to select patients requiring core-needle biopsy (CNB) when performed by interventional cytopathologists in a multidisciplinary and integrated OSC setting. Besides drastically limiting the rate of nondiagnostic specimens and diagnostic turn-around time, OSCAR is an efficient and powerful first-line diagnostic approach for patient-centered care.