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Investigate imaging follow-up patterns and assessment of malignancy rate of BI-RADS 3 lesions in women younger than 30 years.We retrospectively reviewed consecutive studies between January 1, 2013 and January 1, 2015 with BI-RADS 3 assessment in women30 years. Lesion size, follow-up rate, and biopsy rate were recorded. Completion of 24-month imaging follow-up or biopsy determined the endpoint. Statistical analysis of follow-up rates and biopsy timing was performed.Of 2525 BI-RADS 3 lesions, 278 were identified in 215 women30 years. Fifty-two (24%) women underwent a biopsy which was more frequently done at patient request than for lesion growth [33 (63.4%) versus 19 (36.5%), P.01]. The odds of having biopsy upfront was significantly higher in lesions2 cm in diameter (OR: 4.4 [95% CI 2.1-9.4], P.01). The malignancy rate in our cohort was 0% (95% CI 0-1.7%). Of the 188 women expected for follow-up imaging, 58 (30%) were lost to follow-up, while 103 (55%) had 6-month follow-up, 74 (39%) 12-month follow-up, and 56 (30%) 24-month follow-up.BI-RADS 3 lesions identified in our cohort had high biopsy rates and low compliance with no cancers. Our findings suggest that probable fibroadenomas in young women may only warrant abbreviated short-term follow-up at 6-months.

Breast density is strongly associated with breast cancer risk; however, studies on the association between density changes and breast cancer risk have controversial results. The aim of our study was to determine the association between breast density changes and breast cancer risk in East-Asian women. We included 3 301 279 women aged ≥40 years screened for breast cancer twice during 2009 to 2010 and 2011 to 2012. Data were obtained from the National Health Insurance Service (NHIS) database. Breast density was evaluated using the Breast Imaging-Reporting and Data System (BI-RADS). Relative risk (RR) and 5-year risk of developing breast cancer according to density category changes were calculated. Overall, 23.0% of the women had a higher breast density and 22.2% of the women had a lower breast density in second screening compared to the first. An increase in the BI-RADS density category between two subsequent mammographic screenings was associated with an increase in breast cancer risk and vice versa in terms of RR. The 5-year breast cancer risk was affected by the initial BI-RADS density category, changes in density category and patients' characteristics such as age, menopausal status and family history of breast cancer. In patients with breast cancer family history, the 5-year breast cancer risk was prominent, at a maximum of 2.39% (95% CI = 1.23-3.55) in women with breast density category of 2 to 4. Changes in the BI-RADS density category were associated with breast cancer risk. Longitudinal measures of BI-RADS density may be helpful in identifying high-risk women, especially those with a breast cancer family history.

BACKGROUND Breast cancer is the most common malignant tumor in women and a quantitative contrast-free method is highly desirable for its diagnosis. PURPOSE To investigate the performance of quantitative MRI in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS). STUDY TYPE Retrospective. SUBJECTS Eighty patients (56 with malignant lesions and 24 with benign lesions). FIELD STRENGTH/SEQUENCE Diffusion-weighted imaging (DWI) with a single-shot echo planar sequence and synthetic MRI with magnetic resonance image compilation (MAGiC) were performed at 3T. ASSESSMENT T1 relaxation time (T1 ), T2 relaxation time (T2 ), and proton density (PD) from synthetic MRI and apparent diffusion coefficient (ADC) from DWI were analyzed by two radiologists (Reader A, Reader B). Univariable and multivariable models were developed to optimize differentiation between malignant and benign lesions and their performances compared to BI-RADS. STATISTICAL TESTS The diagnostic performance was evaluated using multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC). RESULTS T2 , PD, and ADC values for malignant lesions were significantly lower than those in benign breast lesions for both radiologists (all P

Introduction Women with increased breast density are at increased risk of breast cancer. The aim of this research is to evidence for the first time the mammographic breast findings of Papua New Guinean (PNG) women and the relationship between Breast Imaging‐Reporting and Data System (BI‐RADS) assessment, mammographic parenchymal patterns (MPPs) and age. Methods A retrospective analysis of 1357 mammograms of women imaged at the Pacific International Hospital (PIH) from August 2006 to July 2010 was undertaken. Mammographic findings were categorised using the BI‐RADS Atlas® 5th Edition. MPPs were recorded for each woman using the Tabár Pattern I‐V classification system. Age was recorded in years. Statistical analysis was by descriptive analysis and Kruskal–Wallis with Dunn’s post‐test and Spearman’s rho correlation for inferential analysis. Results True pathological findings (benign and malignant); BI‐RADS 2–5 were noted in 111 women (8.2%); 1242 (91.5%) were negative. BI‐RADS categories for malignancy were reported in 16 (88.9%) of women aged 30 to 60 years. The lower risk Tabár Type I, II and III MPPs were associated with 94.4% (n = 17) of malignancies. Linear correlations between variables were weak and not statistically significant: age and Tabár pattern r = 0.031, P = 0.0261; age and BI‐RADS r = 0.018, P = 0.517; Tabár pattern and BI‐RADS r = 0.020, P = 0.459 (n = 1357). Conclusion There was no correlation demonstrated between BI‐RADS category, age and MPP. Importantly, there was no correlation demonstrated between BI‐RADS categories 4 and 5 for breast malignancy and high‐risk Tabár Type IV and V MPPs. The results of this study again reflect that the incidence of breast cancer in PNG cannot be explained by breast density and suggest that any formalised screening program in PNG has a target age group aimed at women younger than that of Western screening programs., Women with increased breast density are at increased risk of breast cancer. The results of this study again reflect that the incidence of breast cancer in Papua New Guinea cannot be explained by breast density. There was no correlation demonstrated between BI‐RADS categories 4 and 5 for breast malignancy and high‐risk Tabár Type IV and V mammographic parenchymal patterns. The lower risk Tabár Type I, II and III mammographic parenchymal patterns were associated with 94.4% (n = 17) of malignancies.

Background Breast cancer is the leading cause of death among women worldwide. Studies have identified breast density as a controversial risk factor of breast cancer. Moreover, studies found that breast density reduction through Tamoxifen could reduce risk of breast cancer significantly. To date, no study on the association between breast density and breast cancer has been carried out in Malaysia. If breast density is proven to be a risk factor of breast cancer, intervention could be carried out to reduce breast cancer risk through breast density reduction. Purpose To determine if density of breast is an independent risk factor which will contribute to development of breast cancer. Materials and Methods A prospective cohort study is carried out in two hospitals targeting adult female patients who presented to the Breast Clinic with symptoms suspicious of breast cancer. Participants recruited were investigated for breast cancer based on their symptoms. Breast density assessed from mammogram was correlated with tissue biopsy results and final diagnosis of benign or malignant breast disease. Results Participants with dense breasts showed 29% increased risk of breast cancer when compared to those with almost entirely fatty breasts (odds ratio [OR] 1.29, 95% CI 0.38‐4.44, P = .683). Among the postmenopausal women, those with dense breasts were 3.1 times more likely to develop breast cancer compared with those with fatty breasts (OR 3.125, 95% CI 0.72‐13.64, P = .13). Moreover, the chance of developing breast cancer increases with age (OR 1.046, 95% CI 1.003‐1.090, P, There are many established risk factors of breast cancer, and density of breast is a controversial risk factor which we wanted to evaluate. Our study shows that postmenopausal women with dense breasts are 3 times more likely to develop breast cancer compared to those with low breast density.

Background The MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon recommends that a breast MRI protocol contain T2 -weighted and dynamic contrast-enhanced (DCE) MRI sequences. The addition of diffusion-weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE-MRI, DWI, and T2 -weighted imaging are most strongly associated with a breast cancer diagnosis. Purpose/hypothesis To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI-RADS recommended descriptors for breast MRI with DCE, T2 -weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping. Study type Retrospective. Subjects In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014. Field strength/sequence IR inversion recovert DCE-MRI dynamic contrast-enhanced magnetic resonance imaging VIBE Volume-Interpolated-Breathhold-Examination FLASH turbo fast-low-angle-shot TWIST Time-resolved angiography with stochastic Trajectories. Assessment Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T2 -weighted imaging according to BI-RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE-MRI BI-RADS descriptors, T2 -weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10-3 mm2 /sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference. Statistical tests χ2 test, Fisher's exact test, Kruskal-Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer-Lemeshow test of goodness-of-fit, receiver operating characteristics analysis. Results In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T2 -weighted imaging variables were not included in the final models. DATA CONCLUSION: mpMRI with DCE-MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE-MRI and DWI identifies breast cancer with a high diagnostic accuracy. T2 -weighted imaging does not significantly contribute to breast cancer diagnosis. Level of evidence 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:864-874.

Purpose Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., “scattered density” and “heterogeneously dense”. The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. Methods In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. Results The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P

PURPOSE Digital Breast Imaging Reporting and Data System (BI-RADS) features extracted from ultrasound images are essential in computer-aided diagnosis, prediction, and prognosis of breast cancer. This study focuses on the reproducibility of quantitative high-throughput BI-RADS features in the presence of variations due to different segmentation results, various ultrasound machine models, and multiple ultrasound machine settings. METHODS Dataset 1 consists of 399 patients with invasive breast cancer and is used as the training set to measure the reproducibility of features, while dataset 2 consists of 138 other patients and is a validation set used to evaluate the diagnosis performances of the final reproducible features. Four hundred and sixty high-throughput BI-RADS features are designed and quantized according to BI-RADS lexicon. Concordance Correlation Coefficient (CCC) and Deviation (Dev) are used to assess the effect of the segmentation methods and Between-class Distance (BD) is used to study the influences of the machine models. In addition, the features jointly shared by two methodologies are further investigated on their effects with multiple machine settings. Subsequently, the absolute value of Pearson Correlation Coefficient (Rabs ) is applied for redundancy elimination. Finally, the features that are reproducible and not redundant are preserved as the stable feature set. A 10-fold Support Vector Machine (SVM) classifier is employed to verify the diagnostic ability. RESULTS One hundred and fifty-three features were found to have high reproducibility (CCC > 0.9 & Dev < 0.1) within the manual and automatic segmentation. Three hundred and thirty-nine features were stable (BD < 0.2) at different machine models. Two feature sets shared the same 102 features, in which nine features were highly sensitive to the machine settings. Forty-six features were finally preserved after redundancy elimination. For the validation in dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.915. CONCLUSIONS Three factors, segmentation results, machine models, and machine settings may affect the reproducibility of high-throughput BI-RADS features to various degrees. Our 46 reproducible features were robust to these factors and were capable of distinguishing benign and malignant breast tumors.

PURPOSE To analyze the malignancy rate of ultrasound-guided core needle biopsy (US-CNB) for screen-detected breast lesions and to evaluate the results according to the mode of detection of the target lesions. METHODS This is a retrospective review of 703 consecutive women who underwent US-CNB for screen-detected breast lesions at a single tertiary hospital. Breast Imaging Reporting and Data System (BI-RADS) categories and histopathological results of the target lesions were assessed. The cases were divided into two groups: the mammography-based detected lesions (M-group) and the US-based detected lesions (U-group). The biopsy performances of the two groups were compared using the χ2 test. RESULTS The malignancy rate in the entire population was 22.0%: 0.8%, 12.3%, 65.2%, 89.3%, and 94.6% in BI-RADS categories 3, 4A, 4B, 4C, and 5, respectively. The malignancy rate was significantly higher in the M-group (54.1% [79/146]) than in the U-group (13.6% [76/557]) (p < 0.001). BI-RADS category 3 was the most common assessment overall that led to a US-CNB (362/703, 51.5%) and the rate of BI-RADS category 3 diagnoses that led to US-CNB was significantly higher in the U-group (p < 0.001). CONCLUSIONS The malignancy rate associated with US-CNB for screen-detected breast lesions was 22.0%. The breast lesions detected by mammography have a higher malignancy rate than those detected by US. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 45:261-266, 2017.

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

Purpose: Lesions detected at mammography are described with a highly standardized terminology: the breast imaging-reporting and data system (BI-RADS) lexicon. Up to now, no validated semantic computer assisted classification algorithm exists to interactively link combinations of morphological descriptors from the lexicon to a probabilistic risk estimate of malignancy. The authors therefore aim at the external validation of the mammographic mass diagnosis (MMassDx) algorithm. A classification algorithm like MMassDx must perform well in a variety of clinical circumstances and in datasets that were not used to generate the algorithm in order to ultimately become accepted in clinical routine. Methods: The MMassDx algorithm uses a naive Bayes network and calculates post-test probabilities of malignancy based on two distinct sets of variables, (a) BI-RADS descriptors and age (“descriptor model”) and (b) BI-RADS descriptors, age, and BI-RADS assessment categories (“inclusive model”). The authors evaluate both the MMassDx (descriptor) and MMassDx (inclusive) models using two large publicly available datasets of mammographic mass lesions: the digital database for screening mammography (DDSM) dataset, which contains two subsets from the same examinations—a medio–lateral oblique (MLO) view and cranio–caudal (CC) view dataset—and the mammographic mass (MM) dataset. The DDSM contains 1220 mass lesions and the MM dataset contains 961 mass lesions. The authors evaluate discriminative performance using area under the receiver-operating-characteristic curve (AUC) and compare this to the BI-RADS assessment categories alone (i.e., the clinical performance) using the DeLong method. The authors also evaluate whether assigned probabilistic risk estimates reflect the lesions’ true risk of malignancy using calibration curves. Results: The authors demonstrate that the MMassDx algorithms show good discriminatory performance. AUC for the MMassDx (descriptor) model in the DDSM data is 0.876/0.895 (MLO/CC view) and AUC for the MMassDx (inclusive) model in the DDSM data is 0.891/0.900 (MLO/CC view). AUC for the MMassDx (descriptor) model in the MM data is 0.862 and AUC for the MMassDx (inclusive) model in the MM data is 0.900. In all scenarios, MMassDx performs significantly better than clinical performance, P < 0.05 each. The authors furthermore demonstrate that the MMassDx algorithm systematically underestimates the risk of malignancy in the DDSM and MM datasets, especially when low probabilities of malignancy are assigned. Conclusions: The authors’ results reveal that the MMassDx algorithms have good discriminatory performance but less accurate calibration when tested on two independent validation datasets. Improvement in calibration and testing in a prospective clinical population will be important steps in the pursuit of translation of these algorithms to the clinic.

Purpose To investigate gray-scale and color Doppler sonographic (US) features of complex fibroadenoma (FA), according to the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. Methods From 2010 through 2013, 586 women with FAs were seen; 101 (17%) of those lesions were diagnosed as complex FAs on percutaneous needle biopsy or surgery. Among the patients with complex FAs, 67 who had US examination results available were included in this study. In addition, the results from 98 women who had simple FAs were included as controls. US features were retrospectively analyzed by two breast radiologists in consensus for shape, margin, echogenicity, posterior acoustic pattern, boundary, orientation, and associated findings. They also reassessed the BI-RADS category for FAs. Color Doppler US examination results were classified according to the amount of vascularity as absent, moderate, or marked. Results Complex FA were larger than simple FAs were (14.5 cm versus 12.1 cm, p > 0.05). On univariate analysis, a round to irregular shape, an uncircumscribed margin, the presence of associated findings, and BI-RADS categorization as 4a and 4b were more frequently revealed in complex than in simple FAs (p < 0.05). Multivariate analysis revealed that the tumor margin was a predictive factor for complex FA (odds ratio: 6.08; 95% confidence interval: 1.14–32.49, p < 0.05). On color Doppler US, the complex FAs had higher degrees of vascularity than simple FA (p < 0.05). Conclusions Complex FAs, in comparison with simple FAs, tend to have more aggressive features and to be in higher BI-RADS categories on US examination. © 2015 Wiley Periodicals, Inc. J Clin Ultrasound 43:556–562, 2015

Purpose To determine whether readout-segmented echo-planar diffusion imaging (RESOLVE) improves separation of malignant versus benign lesions compared to standard single-shot echo-planar imaging (ss-EPI) on BI-RADS 4/5 lesions detected on breast magnetic resonance imaging (MRI). Materials and Methods Consecutive 3T breast MRI studies with BI-RADS 4/5 designation and subsequent biopsy or benign mastectomy were retrospectively identified. Freehand regions of interest (ROIs) were drawn on lesions and also on normal background fibroglandular tissue for comparison. Lesion-to-background contrast was evaluated by normalizing signal intensity of the lesion ROI by the normal background tissue ROI at b = 800. Statistical analysis used the Mann–Whitney/Wilcoxon rank-sum test for unpaired and Wilcoxon signed-rank for paired comparisons. Results Of 38 lesions in 32 patients, 10 were malignant. Lesion-to-background contrast was higher on RESOLVE than ss-EPI (1.80 ± 0.71 vs. 1.62 ± 0.63, P = 0.03). Mean apparent diffusion coefficient (ADC) was the same or lower on RESOLVE than ss-EPI, and this effect was largest in malignant lesions (RESOLVE 0.90 ± 0.13; ss-EPI 1.00 ± 0.13; median difference −0.10 (95% confidence interval [CI]: −0.17, −0.02) × 10−3mm2/sec; P = 0.014). By either diffusion method, there was a statistically significant difference between benign and malignant mean ADC (P < 0.001). Conclusion Increased lesion-to-background contrast and improved separation of benign from malignant lesions by RESOLVE compared to standard diffusion suggests that RESOLVE may show promise as an adjunct to clinical breast MRI. J. Magn. Reson. Imaging 2014;40:674–681. © 2013 Wiley Periodicals, Inc.

BACKGROUND. Breast cancer is the leading cause of cancer-related deaths in Latinas, chiefly because of later diagnosis. The time from screening to diagnosis is critical to optimizing cancer care, yet the efficacy of navigation in reducing it is insufficiently documented. Here, the authors evaluate a culturally sensitive patient navigation program to reduce the time to diagnosis and increase the proportions of women diagnosed within 30 days and 60 days. METHODS. The authors analyzed 425 Latinas who had Breast Imaging Reporting and Data System (BI-RADS) radiologic abnormalities categorized as BI-RADS-3, BI-RADS-4, or BI-RADS-5 from July 2008 to January 2011. There were 217 women in the navigated group and 208 women in the control group. Women were navigated by locally trained navigators or were not navigated (data for this group were abstracted from charts). The Kaplan-Meier method, Cox proportional hazards regression, and logistic regression were used to determine differences between groups. RESULTS. The time to diagnosis was shorter in the navigated group (mean, 32.5 days vs 44.6 days in the control group; hazard ratio, 1.32; P = .007). Stratified analysis revealed that navigation significantly shortened the time to diagnosis among women who had BI-RADS-3 radiologic abnormalities (mean, 21.3 days vs 63.0 days; hazard ratio, 2.42; P < .001) but not among those who had BI-RADS-4 or BI-RADS-5 radiologic abnormalities (mean, 37.6 days vs 36.9 days; hazard ratio, 0.98; P = .989). Timely diagnosis occurred more frequently among navigated Latinas (within 30 days: 67.3% vs 57.7%; P = .045; within 60 days: 86.2% vs 78.4%; P = .023). This was driven by the BI-RADS-3 strata (within 30 days: 83.6% vs 50%; P < .001; within 60 days: 94.5% vs 67.2%; P < .001). A lack of missed appointments was associated with timely diagnosis. CONCLUSIONS. Patient-centered navigation to assist Latina women with abnormal screening mammograms appeared to reduced the time to diagnosis and increase rates of timely diagnosis overall. However, in stratified analyses, only navigated Latinas with an initial BI-RADS-3 screen benefited, probably because of a reduction in missed diagnostic appointments. Cancer 2013. © 2012 American Cancer Society.

PURPOSE: To describe the sonographic (US) features associated with ultrasonography BI-RADS category 4 lesions that have a benign histopathological outcome. METHODS: One hundred seventy-two histopathologically proven benign lesions in 169 patients, which had been classified as BI-RADS category 4 with ultrasonography, were retrospectively evaluated. Ultrasonography and histopathology findings were analyzed. The frequency of sonographic findings according to the histopathological diagnosis was determined. RESULTS: Among the 172 lesions, there were 66 (38%) fibroadenomas, 31 (18%) sclerosing adenoses, 24 (14%) fibrocystic changes, 16 (9%) mastitis/inflammations, 9 (5.5%) intraductal papillomas, 8 (5%) focal fibroses, 4 (2.5%) atypical ductal hyperplasias, 4 (2.5%) fat necroses, 2 (1%) phyllodes tumors, 1 (0.5%) tubular adenomas, 1 (0.5%) epidermal inclusion cysts, and 6 (3.5%) "other benign lesions." The most frequent sonographic findings were heterogeneity, indistinct margin, microlobulation in fibroadenomas; heterogeneity, irregular-indefinite margin, and antiparallel orientation in sclerosing adenosis; heterogeneity, microlobulation, and acoustic shadowing in fibrocystic changes. CONCLUSIONS: BI-RADS category 4 lesions demonstrate more than one suspicious ultrasonography feature, and biopsy is necessary to diagnose malignancy captured in 33% of lesions in this study. At this time, any lesion with more than one suspicious BI-RADS US feature cannot avoid a diagnostic biopsy.

Summary Mammography is the primary imaging modality for the early detection of breast cancer. Because of the low predictive value of mammography, a large majority of patient referred for biopsy have benign disease. The question is whether magnetic resonance imaging (MRI) is a diagnostic alternative to biopsy for women with inconclusive findings at mammography or mammographic (Breast Imaging Reporting And Data System (BIRADS) 3 lesions. In this article the breast MRI and indications will be described. An overview will be given of MRI as a problem-solving modality in mammographic BIRADS 3 lesions and inconclusive mammographic findings with and without microcalcifications. The negative predictive value of breast MRI must be sufficiently high to definitively indicate a lack of need for biopsy and thus to be an effective addition to the work-up of mammographic BIRADS 3 lesions or inconclusive findings on mammography. Therefore, breast MRI should only be used for cases with proven diagnostic value.

Objective To assess the accuracy of categorization of breast ultrasound findings based on scoring for malignancy using the sonographic breast imaging-reporting and data system (BI-RADS). Methods Breast ultrasound was performed in 2462 patients between 2001 and 2004 at our unit. Sonographic findings were scored using analog criteria as in BI-RADS for breast ultrasound (mass shape, margin, orientation, posterior acoustic features, lesion boundary, echo pattern). Each lesion was described using these features and classified into categories 1 to 5 according to the BI-RADS for breast ultrasound. Categorization and biopsy results were compared. Results In twenty-two (0.9%) patients breast ultrasound could not be evaluated because of extreme density of tissue. Normal breast ultrasound belonging to Category 1 was found in 871 (35.4%) patients. Simple cysts classified as Category 2 were observed in 712 (28.9%) women. In 491 (19.9%) patients, apparently benign solid masses (Category 3) were found. Suspicious masses were observed in 225 (9.1%) women and masses highly suggestive of malignancy were found in 141 (5.7%) patients (Categories 4 and 5, respectively). Histological examinations were available from 84 (17.1%) masses that had been classified by BI-RADS as Category 3, in 97 (43.1%) from Category 4 and 106 (75.2%) from Category 5. Accordingly, the rate of malignant findings was 1.2% (n = 1) in Category 3, 17% (n = 16) in Category 4 and 94% (n = 100) in Category 5. Conclusion Scoring breast ultrasound findings for malignancy based on criteria used for BI-RADS breast ultrasound has a high accuracy, comparable to that obtained by BI-RADS for mammography. Copyright © 2008 ISUOG. Published by John Wiley & Sons, Ltd.

Women with mammographic percent density >50% have a approximately three-fold increased risk of developing breast cancer, potentially making them screening candidates for breast MRI scanning. The purpose of this work is to introduce a new method to quantify mammographic percent density (MPD), and to compare the results with the current standard of care for breast density assessment. Craniocaudal (CC) and mediolateral oblique (MLO) mammograms for 104 patients were digitized and analyzed using an interactive computer-assisted segmentation routine implemented for two purposes: (1) to segment the breast area from background and radiographic markers, and (2) to segment dense from fatty portions of the breast. Our technique was evaluated by comparing the results to qualitative estimates determined by a certified breast radiologist using the BI-RADS Categorical Assessment (1 (fatty) to 4 (dense) scale). Statistically significant correlations (two-tailed, p < 0.01) were observed between calculated MPD and BI-RADS for both CC (Spearman rho = 0.67) and MLO views (Spearman rho = 0.71). For the CC view, statistically significant differences were revealed between the mean MPD for each BI-RADS category except between fatty (BI-RADS 1) and scattered (BI-RADS 2). Finally, for the MLO views, statistically significant differences in the mean MPD between all BI-RADS categories were observed. Comparing the CC and MLO views revealed a strong positive correlation (Pearson r = 0.8) in calculated MPD. In addition, an evaluation of the reproducibility of our segmentation demonstrated the average standard deviation of MPD for a subsample of eight patients, measured five times, was 1.9% (range: 0.03%-9.9%). Eliminating one misassignment reduced the average standard deviation to 0.75% (range: 0.03%-3.16%). Further analysis of approximately 10% of the patient sample revealed strong agreement (ICC = 0.80-0.85) in the reliability of MPD estimates for both mammographic views. Overall, these results demonstrate the feasibility of utilizing our approach for quantitative breast density segmentation, which may be useful for detecting small changes in MPD introduced through chemoprevention, diet, or other interventions.

Purpose To evaluate the cancer yield, frequency of use, and follow-up compliance of breast MR examinations assessed as “probably benign.” Materials and Methods We retrospectively reviewed our MR database of 809 consecutive examinations between January 2003 and June 2004. We identified all examinations that ultimately received an MR breast imaging reporting and data system (BI-RADS) 3 assessment and recommendation for a short-interval follow-up MR with or without an antecedent targeted ultrasound. The clinical indication for breast MR, date of the follow-up examination, follow-up assessment, and any pathology findings through June 2005 were collected from clinical records and radiology and pathology reports. Frequency of BI-RADS 3 use, follow-up compliance, and cancer yield were calculated. Results A total of 160 out of 809 (20%) examinations comprise the study population. A total of 100 out of 160 (63%) women returned for the recommended follow-up MR, in which three out of 100 (3%) were upgraded to BI-RADS 4 with biopsy recommended. One patient underwent biopsy and the result was benign. A total of seven out of 160 (4%) underwent mastectomy in lieu of MR for known cancer elsewhere in the breast. One out of seven mastectomy specimens contained malignancy that corresponded to the probably benign finding, resulting in a cancer yield of one out of 160 (0.6%). Conclusion Our data suggest that follow-up MR is a valid alternative to biopsy for MR BI-RADS 3 lesions. J. Magn. Reson. Imaging 2007;26:950–955. © 2007 Wiley-Liss, Inc.

In this study we compared the performance of computer aided diagnosis (CAD{sub x}) algorithms based on Breast Imaging Reporting And Data System (BI-RADS trade mark sign ) descriptors from one or two views. To select cases for the study with different mediolateral (MLO) and craniocaudal (CC) view descriptors, we assessed the agreement in BI-RADS trade mark sign lesion descriptors, BI-RADS trade mark sign assessment, and subtlety ratings for 1626 cases from the Digital Database for Screening Mammogrpahy (DDSM) using kappa statistics. We used 115 mass cases with different descriptors for the two views to design linear discriminant analysis (LDA) based CAD{sub x} algorithms. The CAD{sub x} algorithms used BI-RADS trade mark sign descriptors and patient age as features. The algorithms based on BI-RADS trade mark sign descriptors from both the views performed marginally better than algorithms based on BI-RADS trade mark sign descriptors from a single view. A system that averaged the results of two classifiers trained separately on the MLO and CC views displayed the best performance (A{sub z}=0.920{+-}0.027). Thus, some improvement in performance of BI-RADS trade mark sign based CAD{sub x} algorithms may be achieved by combining information from two mammographic views.

Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

Purpose: The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data. Methods: A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BRpg measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BRvc and BRvr measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals. Results: The three BI-RADS measures generated by method-1 had κ between 0.25–0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BRpg; (b) OR = 1.93 (1.36, 2.74) for BRvc; and (c) OR = 1.37 (1.05, 1.80) for BRvr. The measures generated by method-2 had κ between 0.42–0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BRpg; (b) OR = 1.42 (0.87, 2.32) for BRvc; and (c) OR = 2.13 (1.22, 3.72) for BRvr. The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant. Conclusions: A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.