Breast Imaging Reporting And Data System Lexicon Research Articles

Differential diagnosis of B-mode ultrasound Breast Imaging Reporting and Data System category 3-4a lesions in conjunction with shear-wave elastography using conservative and aggressive approaches.

The high false-positive rates of US Breast Imaging Reporting and Data System (BI-RADS) category 3-4a breast lesions leads to excessive biopsies of many benign lesions, and our aim was to investigate the diagnostic performance achieved by adding a maximum elasticity (Emax) of shear-wave elastography (SWE) to ultrasound (US) to evaluate US BI-RADS category 3-4a breast lesions using conservative and aggressive approaches. We explored the capacity of using this method to avoid unnecessary biopsies without increasing the probability of missing breast cancers. A total of 123 breast lesions of 120 patients classified as BI-RADS category 3 or 4a were enrolled from January 2019 to December 2019. The US features were evaluated according to the US BI-RADS lexicon. The maximum diameter measured on the US was defined as the size of the lesion. The Emax was assessed by SWE, and the average Emax of breast lesions on two images were calculated and recorded as the final maximum Young's modulus. The diagnostic performance of the combined B-mode US and SWE approach for BI-RADS category 3-4a breast lesions was tested using a conservative approach and an aggressive approach. In the conservative approach, the lesions were downgraded with Emax of 30 kPa or less and upgraded with Emax of 160 kPa or more. In the aggressive approach, the lesions were downgraded with Emax of 80 kPa or less and upgraded with Emax of 160 kPa or more. Pathologic results were defined as the reference standard. Among all 123 breast lesions, there were 60 lesions classified as BI-RADS category 3 and 63 lesions classified as BI-RADS category 4a. Compared to the B-mode US, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) of the combined B-mode US and SWE with a conservative approach changed from 88.9% to 94.4%, 55.2% to 60.0%, 25.4% to 28.8%, 96.7% to 98.4%, 60.2% to 65.0%, and 0.721 to 0.772, respectively. The specificity, PPV, and accuracy of combined B-mode US and SWE with an aggressive approach increased from 55.2% to 72.4%, 25.4% to 29.3%, and 60.2% to 71.5%, respectively, but this was accompanied with decreases in the sensitivity from 88.9% to 66.7%, the NPV from 96.7% to 92.7%, and the AUC from 0.721 to 0.695. The addition of SWE improves the diagnostic performance of breast US. Adding the diagnostic criteria of SWE to the BI-RADS assessment of B-mode US, downgrading the lesions with Emax 30 kPa or less, and upgrading the lesions with Emax 160 kPa or more helped discriminate low suspicion lesions from benign lesions in order to decrease false-positive findings and avoid missing cancer diagnosis.

Diagnostic performance of superb microvascular imaging combined with shear-wave elastography in distinguishing invasive ductal carcinoma molecular subtypes

IntroductionTo explore the diagnostic value of combining superb microvascular imaging (SMI), shear-wave elastography (SWE), and Breast Imaging Reporting and Data System (BI-RADS) to distinguish different molecular subtypes of invasive ductal carcinoma (IDC).Material and methodsA total of 239 surgically confirmed IDC masses in 201 patients underwent conventional ultrasound, SMI, and SWE examination, the information such as echo pattern, posterior features, margins, SMI pixels, and hardness of the masses was recorded. According to the St. Gallen standard, breast masses were classified as Luminal A, Luminal B, HER2 overexpression, and triple-negative subtype. We further explored the differences between different molecular subtypes of IDC.ResultsLuminal A subtype had the following characteristics: low histologic grade, posterior acoustic shadowing (p= 0.019), spiculated margins (p<0.001) , and relatively soft. Luminal B subtype was characterized by low histological grade (p <0.0001), posterior acoustic shadowing or indifference, and indistinct margins. HER2 overexpression breast cancers were characterized by high histological grade, enhanced posterior acoustics or indifference, calcifications (p= 0.005), spiculated or indistinct margins, vascularity (p=0.005), and relative stiffness. Triple-negative breast cancers had the characteristics of high histological grade, posterior echogenic enhancement, lack of calcifications, circumscribed or microlobulated margins, low blood flow signals, and stiff tissue (p=0.013).ConclusionsOur study demonstrated the significant differences and trends among the IDC four subtypes by the combined application of SMI, SWE, and BI-RADS lexicon, which are of great significance for early diagnosis, selection of treatment methods, and evaluation of prognosis of IDC.

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound.

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions.

ObjectivesTo evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.MethodsA total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.ResultsΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.ConclusionsThe mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.

Model for making diagnostic decisions in multiparametric ultrasound of breast lesions

In order to standardize the description of the breast imaging, the BI-RADS (Breast Imaging Reporting And Data System) imaging system developed by the American College of Radiologists ACR is widely used in world practice. At the same time, numerous visual characteristics of breast lesions with different diagnostic methods complicate the adoption of diagnostic decisions while using the BI-RADS system. The greatest difficulties arise when assessing a variety of multiparametric ultrasound signs of diseases. In this regard, in order to increase the efficiency of these technologies and make fast diagnostic decisions, it becomes relevant to develop a system model based on algorithms using the BI-RADS lexicon.
 Materials and methods: from 2017 to 2019 on the basis of the Research Oncology Center named after N.N. Petrov 277 women with various complaints of breast disease were examined using multiparametric ultrasound with elastography and contrast enhancement (2.5 ml Sonovue) on a Hitachi Hi Vision Ascendus ultrasound scanner. The software implementation of the diagnostic decision-making model was carried out using the C # programming language using the Microsoft Visual integrated development environment.
 Results: The effectiveness of the developed diagnostic model using the optimal algorithm for the use of various ultrasound technologies in determining the malignancy of the formation showed Sensitivity (Se) = 90.8%, Specificity (Sp) = 95.5%, Positive Predictive Value (PPV) = 88.5%, Negative Predictive Value (NPV) = 96.4%, Accuracy (Ac) = 94.2%. The effectiveness of the developed model in grouping diseases showed Se = 84.2%, Sp = 81.1%, PPV = 62.7%, NPV = 93.1%, Ac = 81.9%.
 Conclusions: The proposed system model of the optimal algorithm for making a diagnostic decision based on statistically significant multiparametric ultrasound signs increases the diagnostic efficiency.

Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients.

ObjectiveTo develop a nomogram for predicting axillary lymph node (ALN) metastases using the breast imaging reporting and data system (BI-RADS) ultrasound lexicon.MethodsA total of 703 patients from July 2015 to January 2018 were included in this study as a primary cohort for model construction. Moreover, 109 patients including 51 pathologically confirmed N1 patients (TNM staging) and 58 non-metastatic patients were recruited as an external validation cohort from March 2018 to August 2019. Ultrasound images and clinical information of these patients were retrospectively reviewed. The ultrasonic features based on the BI-RADS lexicon were extracted by two radiologists. The features extracted from the primary cohort were used to develop a nomogram using multivariate analysis. Internal and external validations were performed to evaluate the predictive efficacy of the nomogram.ResultsThe nomogram was based on two features (size, lesion boundary) and showed an area under the curve of 0.75 (95% confidence interval [CI], 0.70–0.79) in the primary cohort and 0.91 (95% CI, 0.84–0.97) in the external validation cohort; it achieved an 88% sensitivity in N1 patients.ConclusionThe nomogram based on BI-RADS ultrasonic features can predict breast cancer ALN status with relatively high accuracy. It has potential clinical value in improving the sensitivity and accuracy of the preoperative diagnosis of ALN metastases, especially for N1 patients.

Reader characteristics and mammogram features associated with breast imaging reporting scores.

This study aims to explore the reading performances of radiologists in detecting cancers on mammograms using Tabar Breast Imaging Reporting and Data System (BIRADS) classification and identify factors related to breast imaging reporting scores. 117 readings of five different mammogram test sets with each set containing 20 cancer and 40 normal cases were performed by Australian radiologists. Each radiologist evaluated the mammograms using the BIRADS lexicon with category 1 - negative, category 2 - benign findings, category 3 - equivocal findings (Recall), category 4 - suspicious findings (Recall), and category 5 - highly suggestive of malignant findings (Recall). Performance metrics (true positive, false positive, true negative, and false negative) were calculated for each radiologist and the distribution of reporting categories was analyzed in reader-based and case-based groups. The association of reader characteristics and case features among categories was examined using Mann-Whitney U and Kruskal-Wallis tests. 38% of cancer-containing mammograms were reported with category 3 which decreased to 32.3% with category 4 and 16.2% with category 5 while 16.6 and 10.3% of cancer cases were marked with categories 1 and 2. Female readers had less false-negative rates when using categories 1 and 2 for cancer cases than male readers (p < 0.01). A similar pattern as gender category was also found in Breast Screen readers and readers completed breast reading fellowships compared with non-Breast Screen and non-fellowship readers (p < 0.05). Radiologists with low number of cases read per week were more likely to record the cancer cases with category 4 while the ones with high number of cases were with category 3 (p < 0.01). Discrete mass and asymmetric density were the two types of abnormalities reported mostly as equivocal findings with category 3 (47-50%; p = 0.005) while spiculated mass or stellate lesions were mostly selected as highly suggestive of malignancy with category 5 (26%, p = 0.001). Most radiologists used category 3 when reporting cancer mammograms. Gender, working for BreastScreen, fellowship completion, and number of cases read per week were factors associated with scoring selection. Radiologists reported higher Tabar BIRADS category for specific types of abnormalities on mammograms than others. The study identified factors associated with the decision of radiologists in assigning a BIRADS Tabar score for mammograms with abnormality. These findings will be useful for individual training programs to improve the confidence of radiologists in recognizing abnormal lesions on screening mammograms.

Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images

BackgroundThe purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images.MethodsThe database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI).ResultsThe classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667–0.9762), with 71.4% sensitivity (95% CI 0.6479–0.8616) and 76.9% specificity (95% CI 0.6148–0.8228). The best AUC for each method was 0.744 (95% CI 0.677–0.774) for DT, 0.818 (95% CI 0.6667–0.9444) for LDA, 0.811 (95% CI 0.710–0.892) for RF, and 0.806 (95% CI 0.677–0.839) for MLP.Lesion margin and orientation were the optimal features for all the machine learning methods.ConclusionsML can aid the distinction between benign and malignant breast lesion on ultrasound images using quantified BI-RADS descriptors. SVM provided the highest ROC-AUC (0.840).

Breast lesion shape and margin evaluation: BI-RADS based metrics understate radiologists' actual levels of agreement

BackgroundWhile there is much literature describing the radiologic detection of breast cancer, there are limited data available on the agreement between experts when delineating and classifying breast lesions. The aim of this work is to measure the level of agreement between expert radiologists when delineating and classifying breast lesions as demonstrated through Breast Imaging Reporting and Data System (BI-RADS) and quantitative shape metrics. MethodsForty mammographic images, each containing a single lesion, were presented to nine expert breast radiologists using a high specification interactive digital drawing tablet with stylus. Each reader was asked to manually delineate the breast masses using the tablet and stylus and then visually classify the lesion according to the American College of Radiology (ACR) BI-RADS lexicon. The delineated lesion compactness and elongation were computed using Matlab software. Intraclass Correlation Coefficient (ICC) and Cohen's kappa were used to assess inter-observer agreement for delineation and classification outcomes, respectively. ResultsInter-observer agreement was fair for BI-RADS shape (kappa = 0.37) and moderate for margin (kappa = 0.58) assessments. Agreement for quantitative shape metrics was good for lesion elongation (ICC = 0.82) and excellent for compactness (ICC = 0.93). ConclusionsFair to moderate levels of agreement was shown by radiologists for shape and margin classifications of cancers using the BI-RADS lexicon. When quantitative shape metrics were used to evaluate radiologists' delineation of lesions, good to excellent inter-observer agreement was found. The results suggest that qualitative descriptors such as BI-RADS lesion shape and margin understate the actual level of expert radiologist agreement.

Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience.

PurposeThe purpose of this study was to evaluate the usefulness of applying computer-aided diagnosis (CAD) to breast ultrasound (US), depending on the reader's experience with breast imaging.MethodsBetween October 2015 and January 2016, two experienced readers obtained and analyzed the grayscale US images of 200 cases according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. They additionally applied CAD (S-Detect) to analyze the lesions and made a diagnostic decision subjectively, based on grayscale US with CAD. For the same cases, two inexperienced readers analyzed the grayscale US images using the BI-RADS lexicon and categories, added CAD, and came to a subjective diagnostic conclusion. We then compared the diagnostic performance depending on the reader's experience with breast imaging.ResultsThe sensitivity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 91.7%, 75.0%, 75.0%, and 66.7%, respectively. The specificity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 76.6%, 71.8%, 78.2%, and 76.1%, respectively. When diagnoses were made subjectively in combination with CAD, the specificity significantly improved (76.6% to 80.3%) without a change in the sensitivity (91.7%) in the experienced readers. After subjective combination with CAD, both of the sensitivity and specificity improved in the inexperienced readers (75.0% to 83.3% and 71.8% to 77.1%). In addition, the area under the curve improved for both the experienced and inexperienced readers (0.84 to 0.86 and 0.73 to 0.80) after the addition of CAD.ConclusionCAD is more useful for less experienced readers. Combining CAD with breast US led to improved specificity for both experienced and inexperienced readers.

Characterization of Breast Masses in Digital Breast Tomosynthesis and Digital Mammograms: An Observer Performance Study

This study aimed to compare Breast Imaging Reporting and Data System (BI-RADS) assessment of lesions in two-view digital mammogram (DM) to two-view wide-angle digital breast tomosynthesis (DBT) without DM. With Institutional Review Board approval and written informed consent, two-view DBTs were acquired from 134 subjects and the corresponding DMs were collected retrospectively. The study included 125 subjects with 61 malignant (size: 3.9-36.9 mm, median: 13.4 mm) and 81 benign lesions (size: 4.8-43.8 mm, median: 12.0 mm), and 9 normal subjects. The cases in the two modalities were read independently by six experienced Mammography Quality Standards Act radiologists in a fully crossed counterbalanced manner. The readers were blinded to the prevalence of malignant, benign, or normal cases and were asked to assess the lesions based on the BI-RADS lexicon. The ratings were analyzed by the receiver operating characteristic methodology. Lesion conspicuity was significantly higher (P << .0001) and fewer lesion margins were considered obscured in DBT. The mean area under the receiver operating characteristic curve for the six readers increased significantly (P = .0001) from 0.783 (range: 0.723-0.886) for DM to 0.911 (range: 0.884-0.936) for DBT. Of the 366 ratings for malignant lesions, 343 on DBT and 278 on DM were rated as BI-RADS 4a and above. Of the 486 ratings for benign lesions, 220 on DBT and 206 on DM were rated as BI-RADS 4a and above. On average, 17.8% (65 of 366) more malignant lesions and 2.9% (14 of 486) more benign lesions would be recommended for biopsy using DBT. The inter-radiologist variability was reduced significantly. With DBT alone, the BI-RADS assessment of breast lesions and inter-radiologist reliability were significantly improved compared to DM.

Nonpalpable BI-RADS 4 breast lesions: sonographic findings and pathology correlation.

We aimed to evaluate ultrasonography (US) findings for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions using BI-RADS US lexicon and determine the positive and negative predictive values (PPV and NPV) of US with respect to biopsy results. Sonograms of 186 BI-RADS 4 nonpalpable breast lesions with a known diagnosis were reviewed retrospectively. The morphologic features of all lesions were described using BI-RADS lexicon and the lesions were subcategorized into 4A, 4B, and 4C on the basis of the physician's level of suspicion. Lesion descriptors and biopsy results were correlated. Pathologic results were compared with US features. PPVs of BI-RADS subcategories 4A, 4B, and 4C were calculated. Of 186 lesions, 38.7% were malignant and 61.2% were benign. PPVs according to subcategories 4A, 4B, and 4C were 19.5%, 41.5%, and 74.3%, respectively. Microlobulated, indistinct, and angular margins, posterior acoustic features, and echo pattern were nonspecific signs for nonpalpable BI-RADS 4 lesions. Typical signs of malignancy were irregular shape (PPV, 66%), spiculated margin (PPV, 80%) and nonparallel orientation (PPV, 58.9%). Typical signs of benign lesions were oval shape (NPV, 77.1%), circumscribed margin (NPV, 67.5%), parallel orientation (NPV, 70%), and abrupt interface (NPV, 67.6%). BI-RADS criteria are not sufficient for discriminating between malignant and benign lesions, and biopsy is required. Subcategories 4A, 4B, and 4C are useful in predicting the likelihood of malignancy. However, objective and clear subclassification rules are needed.

P131 Targeted ultrasonography for MRI-detected lesions of preoperative breast cancer patients

Goals: Magnetic resonance imaging (MRI) is commonly performed to preoperatively evaluate the extent of breast cancer. However, proper management of secondary lesions detected by using MRI is sometimes challenging. Although MRI-guided biopsy is recommended to examine such lesions, the procedure is rarely performed. Therefore, these lesions are often investigated by using ultrasonography (US). Furthermore, the treating physicians might not always be familiar with the MRI lexicon by the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) for the management of MRI-detected lesions, possibly because MRI and US findings do not always correspond. The aim of this study was to examine the identification and malignancy rates of MRI-detected lesions by using US in preoperative breast cancer patients based on the BI-RADS lexicon. Methods:Of the 725 primary breast cancer patients who underwent preoperative breast MRI at Chiba University Hospital between November 2011 and June 2014, 102 were detected with secondary lesions and included in this study. We retrospectively analysed the identification rate of these lesions by using US and their malignancy frequency based on the BI-RADS classification. Results: MRI detected a total of 111 lesions, including 24 belonging to category 2, 40 of category 3, 45 of category 4, and 2 of category 5. The identification rates on US for each category were 50%, 60%, 89%, and 100%, respectively. Biopsy was performed to examine all lesions detected on US, and the malignancy rates for each category were 0%, 29%, 55%, and 100%, respectively. One patient who was diagnosed with category 4 lesions on MRI but undetectable on US eventually showed a relapse. Conclusion: The application of US for MRI-detected lesions is useful and convenient. However, approximately 10% of the cases with an MRI category of ≥4 were undetectable on US, especially non-mass enhancement lesions. Therefore, it is critical to improve detection capability, for example, by using real-time virtual sonography or repeating MRI within a short time period. Disclosure of Interest: No significant relationships.

Le rehaussement sans masse en IRM mammaire : comment j’interprète ?

The MR Breast Imaging Reporting and Data System (BI-RADS) lexicon of the American College of Radiology (ACR) includes a new lesion category defined as non-masslike enhancement. The purpose of this paper is to review the definition of this new entity, illustrate the main imaging features described in the BI-RADS lexicon and to propose a diagnostic approach based on data from the literature in order to achieve diagnosis and optimal patient management.

Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and Le Gal's classification

Introduction/objective: Our aim was to evaluate the positive predictive value (PPV) of the analysis of breast microcalcifications according to Breast Imaging Reporting and Data System (BI-RADS) and Le Gal's classification in identification of malignancy, and to assess the interobserver agreement using these criteria. Methods and material: Eighty-two patients with breast microcalcifications on their screening mammograms underwent surgical excision after a needle localization at our institution between July 1993 and June 2000. The mammograms were examined by two experienced mammographers retrospectively and independently. Each observer noted the morphology, distribution, associated findings, final assessment categories of microcalcifications according to BI-RADS criteria and the morphologic type of microcalcifications according to Le Gal's classification. The PPVs for each radiologist and the interobserver agreement were determined by using these data and histologic findings. Results: Histopathologic results yielded malignancy in 25 (30%) cases. The evaluation of microcalcifications according to BI-RADS criteria revealed PPVs of 17% and 25% for category 4 lesions, and 68% and 44% for categoy 5 lesions. In the assessment of microcalcifications according to Le Gal's classification, the PPV of type 4 lesions was 45% (for both observers), whereas the PPVs of type 5 lesions were 70% and 50%. The interobserver agreement was fair in evaluation of morphology of microcalcifications (κ: 0.31), distribution of microcalcifications (κ: 0.29), final assessment categories (κ: 0.27), and moderate in evaluation of associated findings (κ: 0.48) by using BI-RADS lexicon. It was higher for the assessment of milk of calcium and round microcalcifications than other typically benign microcalcifications, and for fine linear or fine linear branching microcalifications than other probably malignant calcifications. There was a fair interobserver agreement (κ: 0.30) in the description of the morphologic type of microcalcifications according to Le Gal's classification. Discussions and conclusion: In our study, both BI-RADS lexicon and Le Gal's classification did not succeed expectedly in reducing the ambiguity in assessment of breast microcalcifications. Further studies and perhaps development of new methods are required to improve accuracy and standardization in mammographic interpretation.

Bi-rads categorization and positive predictive value of mammographic features

Objective: To determine whether the categories defined in the Breast Imaging Reporting and Data System (BI-RADS) are useful predictors of malignancy. Methods: A total of 348 cases with benign and malignant breast diseases were collected. Mammographic and pathologic findings were reviewed. Mammograms of 348 cases were characterized according to BI-RADS descriptors and were categorized by the final assessment categories. Results: Of the all 348 patients, 232 (67%) were carcinomas. Significantly more masses with calcification and speculation were found in breast cancers than in benign breast diseases. BI-RADS final assessment categories were category 2 and 3 in 106 cases, in which 75% (79/106) were benign; category 4 and 5 in 242 cases, in which 85% (205/242) were carcinomas. BI-RADS categories 4 and 5 are useful predictors of relative likelihood of malignancy. The features with higher positive predictive values for carcinomas were irregular shape, indistinct or speculated margins, fine or linear calcification morphology, and regional calcification distribution. Conclusion: BI-RADS lexicon is successful in providing a standardized language for physicians to describe lesion morphology. BI-RADS category is useful for predicting the presence of malignancy.