Breast Imaging Reporting And Data System Assessment Categories Research Articles

Prediction of benignity or malignancy of a lesion using BI-RADS.

To determine the Positive Predictive Value (PPV) of the Breast Imaging Reporting and Data System (BI-RADS) categories as confirmed by histopathology of localized lesion. An observational study. All patients undergoing mammographic needle localizations for breast lesions from January 2001 to December 2003 were included in the study. The study was conducted at the Radiology Department of Aga Khan University Hospital, Karachi. The medical records of all patients undergoing needle localization during the study period were retrospectively reviewed. In a total of 111 patients, 114 mammographically-guided needle localizations were performed for breast lesions. In all these patients the mammographic finding and BI-RADS category were recorded. Statistical analysis was done on SPSS. Histopathological results of the localized sample were used as a gold standard to calculate the PPV. The PPV for each assessment category was calculated by dividing the number of patients positive for malignancy on histopathological examination by the total number of patients who underwent biopsy for each BI-RADS assessment category. In 114 localizations the histopathologic finding showed 25 (22%) cancers (10 [40%] were ductal carcinoma in situ (DCIS); (15 [60 %] were invasive cancers) and 89 (78 %) benign lesions. All the mammographic lesions were categorized as, 7 (6%) category 0 lesions; 3 (2%) category 1 lesions, and 30 (26%) category 2 lesions; the PPV was 0% for category 0,1,2 (0% of 7,3,30 lesions respectively). There were 18 (16 %) category 3 lesions; the PPV was 5% (1 of 18 lesions). One case of cancer in this group was DCIS. There were 50 (44%) category 4 lesions; the PPV was 34% (17 of 50 lesions). There were 6 (5%) category 5 lesions; the PPV was 83% (5 of 6 lesions). The number of biopsies performed for benign lesion can be reduced by using BI-RADS categories, as it has a good PPV.

Predictors of Invasive Breast Cancer in Patients With Ductal Carcinoma In Situ in Ultrasound-Guided Core Needle Biopsy.

To determine predictors of invasiveness of lesions with US-guided biopsy-confirmed ductal carcinoma in situ (DCIS), focusing on US features, including shear wave elastography (SWE). From January 2015 to September 2016, a total of 80 lesions with US-guided biopsy-confirmed DCIS were detected in patients who underwent preoperative mammography, B-mode US, and SWE. Data were retrospectively reviewed from clinical records, pathologic reports, and imaging assessments. Imaging data included mammographic findings, B-mode US findings based on the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS), and the mean and maximum elasticity values on SWE. The final BI-RADS assessment, including the degree of elasticity of the mass, was evaluated. Continuous variables were examined by an independent t test, and categorical variables were examined by the Fisher exact test. The independent factors for predicting a histologic upgrade were evaluated by a multivariate logistic regression analysis. Among the 80 lesions, 27 (33.8%) showed an invasive component after surgical excision. None of the BI-RADS US descriptors, which include shape, orientation, margin, and echogenicity, showed a significant correlation with the rate of a histologic upgrade to invasive cancer. However, the BI-RADS assessment category (P = .015) and nuclear grade (P = .005) were significantly correlated with invasiveness of the mass. The maximum stiffness value was lower in the pure DCIS group (119.04 vs 85.33 kPa; P = .041). The BI-RADS category based on US findings, maximum stiffness value on SWE, and nuclear grade of DCIS are predictive of invasive components in DCIS.

BI-RADS categorisation of 2708 consecutive nonpalpable breast lesions in patients referred to a dedicated breast care unit

To determine the malignancy rate of nonpalpable breast lesions, categorised according to the Breast Imaging Reporting and Data System (BI-RADS) classification in the setting of a Breast Care Unit. All nonpalpable breast lesions from consecutive patients referred to a dedicated Breast Care Unit were prospectively reviewed and classified into 5 BI-RADS assessment categories (0, 2, 3, 4, and 5). A total of 2708 lesions were diagnosed by mammography (71.6%), ultrasound (8.7%), mammography and ultrasound (19.5%), or MRI (0.2%). The distribution of the lesions by BI-RADS category was: 152 in category 0 (5.6%), 56 in category 2 (2.1%), 742 in category 3 (27.4%), 1523 in category 4 (56.2%) and 235 in category 5 (8.7%). Histology revealed 570 malignant lesions (32.9%), 152 high-risk lesions (8.8%), and 1010 benign lesions (58.3%). Malignancy was detected in 17 (2.3%) category 3 lesions, 364 (23.9%) category 4 lesions and 185 (78.7%) category 5 lesions. Median follow-up was 36.9 months. This pragmatic study reflects the assessment and management of breast impalpable abnormalities referred for care to a specialized Breast Unit. Multidisciplinary evaluation with BI-RADS classification accurately predicts malignancy, and reflects the quality of management. This assessment should be encouraged in community practice appraisal.

Mammographic characteristics and vacuum-assisted breast biopsy (VABB) of non-palpable breast lesions

The new imaging technology made available today allows for an early detection of small subclinical breast lesions which frequently call for guided presurgical micro-histology. To evaluate the relationship between vacuum-assisted breast biopsy (VABB) histopathological diagnoses and mammographic findings in non-palpable breast lesions. The breast lesions of 1393 women who had received consecutive screening mammograms between 2001 and 2007 were assessed by VABB. The mammographic breast lesions, classified according to the Breast Imaging Reporting and Data System (BI-RADS), were subjected to VABB only if rated as highly suspicious (2%), suspicious (64.5%) for malignancy, or probably benign (33.5%). VABB findings included 981 (70.5%) probably benign lesions, 407 (29.2%) suspicious/malignant lesions, and five (0.3%) cases which were considered as inappropriate for diagnostic purposes. At histology, 10.2% of the suspicious/malignant lesions were classified as proliferative lesions, 11.1% as ductal carcinoma in situ (DCIS), and 8% as invasive ductal carcinoma (IDC). The positive predictive value (PPV) of BI-RADS assessment categories 3, 4 and 5 was 4.1%, 25.3% and 75%, respectively. The occurrence of obscured or spiculated masses was found to exhibit the highest PPV for malignancy (12.5% in BI-RADS 3 and 63% in BI-RADS 4), followed by microcalcifications which showed a malignancy rate of 6.4% in BI-RADS 3, and 20% in BI-RADS 4. VABB turns out to be effective in the assessment of many malignant and benign preclinical tumour lesions thus allowing for a significant reduction of the number of surgical biopsies.

Inter-observer variability of Breast Imaging Reporting and Data System(BI-RADS) ultrasound final assessment

Objective To evaluate the inter-observer variability of static breast sonogram final assessment among observers with different breast imaging experience, using the first edition of the Breast Imaging Reporting and Data System(BI-RADS) for ultrasound. Methods Thirty patients with 30 breast lesions were included who underwent beast lesions resection operation. A pathological diagnosis was available for all 30 lesions:16 (53%) malignant and 14 (47%) benign. Twelve radiologists independently reviewed two sonograms of each lesion, and assigned a final BI-RADS assessment category. Inter-observer variability was measured using kappa statistic. Positive predictive value(PPV) and negative predictive value (NPV) for final assessment were also calculated. Results As for the experienced observers,kappa values of categories 3,4 and 5 were 0.72,0.28 and 0.60,NPV of category 3 was 93% ,PPV of category 5 was 97% ,all of which decreased as the breast imaging experience reduced. PPVs of categories 4a,4b and 4c were 56 % ,88% and 69%, respectively. Conclusions Using BI-RADS final assessment, radiologists with sufficient breast imaging experience can provide accurate and consistent assessment for breast ultrasonography,but the agreement of diagnosis decreased as the breast imaging experience reduced. The clinical feasibility of 4a,4b and 4c subcategories is uncertain. Key words: Ultrasonography; Breast diseases

A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis

The purpose of our study was to create a breast cancer risk estimation model based on the descriptors of the National Mammography Database using logistic regression that can aid in decision making for the early detection of breast cancer. We created two logistic regression models based on the mammography features and demographic data for 62,219 consecutive mammography records from 48,744 studies in 18,269 [corrected] patients reported using the Breast Imaging Reporting and Data System (BI-RADS) lexicon and the National Mammography Database format between April 5, 1999 and February 9, 2004. State cancer registry outcomes matched with our data served as the reference standard. The probability of cancer was the outcome in both models. Model 2 was built using all variables in Model 1 plus radiologists' BI-RADS assessment categories. We used 10-fold cross-validation to train and test the model and to calculate the area under the receiver operating characteristic curves (A(z)) to measure the performance. Both models were compared with the radiologists' BI-RADS assessments. Radiologists achieved an A(z) value of 0.939 +/- 0.011. The A(z) was 0.927 +/- 0.015 for Model 1 and 0.963 +/- 0.009 for Model 2. At 90% specificity, the sensitivity of Model 2 (90%) was significantly better (p < 0.001) than that of radiologists (82%) and Model 1 (83%). At 85% sensitivity, the specificity of Model 2 (96%) was significantly better (p < 0.001) than that of radiologists (88%) and Model 1 (87%). Our logistic regression model can effectively discriminate between benign and malignant breast disease and can identify the most important features associated with breast cancer.

Breast MRI: Are T2 IR sequences useful in the evaluation of breast lesions?

Aim To evaluate the potential role of signal intensities calculated in T2 images as an adjunctive parameter in the analysis of mass-like enhancements classified as BIRADS (Breast Imaging Reporting and Data System) assessment categories 2, 3, 4 or 5 with the standard T1 criteria. Materials and methods After a retrospective review of 338-breast Magnetic Resonance Imaging (MRI) performed for the evaluation of a suspicious lesion we selected a group of 65 mass-like enhancements ranging from 5 to 20 mm, classified as BIRADS assessment categories 2, 3, 4 or 5, histologically proved. In all cases we calculated the ratio between the signal intensity (SI) of the nodule and the pectoralis major muscle (LMSIR, lesion to muscle signal intensity ratio) with a multiROIs (region of interest) analysis on T2 images. A ROC analysis was performed to test the ability of the two diagnostic parameters separately considered (BIRADS and LMSIR) and combined in a new mono-dimensional variable obtained by a computerized discriminant function. Results Histological examination assessed 34 malignant lesions (52.3%) and 31 benign lesions (47.7%). The evaluation of ROC curves gave the following results: BIRADS area under the curve (AUC) 0.913, S.E. 0.0368, LMSIR AUC 0.854, S.E. 0.0487, combined BIRADS–LMSIR AUC 0.965, S.E. 0.0191 with a definitive increase in the AUC between the overall ROC area and those of the two diagnostic modalities separately considered. Discussion T2-weighted SI assessment with LMSIR measurement improves the diagnostic information content of standard breast MRI and can be considered a promising potential tool in the differential diagnosis of mass-like enhancements judged as borderline lesions (BIRADS 3 and 4).

Computer Aided Classification System for Breast Ultrasound Based on Breast Imaging Reporting and Data System (BI-RADS)

Clinically, the ultrasound findings are evaluated by its sonographic characteristics and then assigned to assessment categories according to the definitions of Breast Imaging Reporting and Data System (BI-RADS) developed by the American College of Radiology. In this study, a computer-aided classification (CAC) system was proposed to classify the masses into assessment categories 3, 4 and 5, which simulated the clinical diagnosis of radiologists. Compared with current computer-aided diagnosis systems, the proposed CAC system classifies the indeterminate cases into BI-RADS category 4 for further diagnosis. Six hundred twenty-six cases were collected from three ultrasound systems and confirmed by pathology and retrospectively classified into categories 3, 4 and 5 by radiologists. The multinomial logistic regression model was trained as the CAC system for predicting the assessment category from the computerized BI-RADS features and from a set of machine-dependent factors. By using the machine-dependent factors to indicate the adopted ultrasound systems, the same regression model could be applied for the cases acquired from different ultrasound systems. A basic CAC system was trained by using the classification result of radiologists. A weighted CAC system, to improve the capacity of the basic CAC system in differentiating benign from malignant lesions, was trained by adding the pathologic result. Between the radiologists and the basic CAC system, a substantial agreement was indicated by Cohen’s kappa statistic and the differences in either the performance indices or the A Z of receiver operating characteristic (ROC) analysis were not statistically significant. For the weighted CAC system, the performance indices accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 73.00% (457 of 626), 98.17% (215 of 219), 59.46% (242 of 407), 56.58% (215 of 380) and 98.37% (242 of 246), respectively; the A Z was 0.94; and the correlation with the radiologists was also substantial agreement. The indices accuracy and specificity of weighted CAC system, compared with those of the radiologists, were improved by 5.91% and 8.85%, respectively and the indices of sensitivity and NPV, compared with those of a conventional CAD system, were improved by 10.5% and 5.21%, respectively; all improvements were statistically significant. To classify the mass into BI-RADS assessment categories by the CAC system is feasible. Moreover, the proposed CAC system is flexible because it can be used to diagnose the cases acquired from different ultrasound systems. (E-mail: rfchang@csie.ntu.edu.tw)