Investigation of Factors Affecting Histopathological Upgrade in Breast Papillary Lesions: A Single-Center Study

Guam's breast cancer incidence is lower than the U.S. (86.6 vs. 126.8 per 100, 000), but mortality rates are higher (27.3 vs. 20.3). This disparity underscores the importance of improving screening and understanding risk factors. Breast density, categorized using the Breast Imaging Reporting and Data System (BIRADS), complicates tumor detection and correlates with malignancy risk. This study investigates the demographic and reproductive factors associated with breast density in Guam. Data from 1, 940 women undergoing mammography at FHP Health Center (Nov 2023-Aug 2024) in the HIPIMR study were analyzed. Associations between demographic factors (ethnicity, BMI) and reproductive factors (family history, age at menarche, menopausal status, parity, age at first live birth) with BIRADS categories were tested using chi-square tests. Reproductive factors were categorized by risk level. Multinomial logistic regression, adjusted for age and BMI, examined associations. Missing data (e.g., BMI, age, parity) were imputed using series means/medians. Women with extremely dense breast tissue (BIRADS D) were younger (mean age 57.13 vs. 58.32, p<0.001) and had lower BMI (74% vs. 37%, p<0.001). They more often experienced menarche after age 12 (58% vs. 51%, p=0.002), had fewer than two births (64% vs. 53%, p<0.001), and gave birth after age 30 (65% vs. 51%, p<0.001). Premenopausal (32%) and early menopause women (51%) had higher BIRADS D proportions (p<0.001, p=0.005). Regression showed BMI inversely associated with BIRADS D (OR = 0.815, 95% CI: 0.787-0.844), BIRADS C (OR = 0.875, 95% CI: 0.842-0.908), and BIRADS B (OR = 0.937, 95% CI: 0.907-0.968) (p<0.001). Family history increased odds of BIRADS D (OR = 1.64, 95% CI: 1.041-2.584, p=0.033) and BIRADS B (OR = 1.674, 95% CI: 1.042-2.69, p=0.033). Nulliparous women were 2.66 times more likely in BIRADS B (OR = 2.66, 95% CI: 1.157-6.114, p=0.021). Micronesians were less likely to belong to BIRADS D (OR = 0.419, 95% CI: 0.19-0.924, p=0.031) or BIRADS C (OR = 0.265, 95% CI: 0.092-0.766, p=0.014). BMI, parity, and childbirth age significantly influence breast density in Guam. Women with lower BMI, fewer births, or later childbirth were more likely to have extremely dense breast tissue, potentially complicating tumor detection. Ethnicity also played a role, with Micronesian women less likely in BIRADS D. While imputation mitigated missing data, bias remains possible. Future research should expand sample sizes and include malignancy risk via BIRADS outcomes for deeper insight. This research was funded by the National Cancer Institute: The University of Guam/University of Hawaii Cancer Center Partnership to Advance Cancer Health Equity (PIPCHE), Grant U54CA143728 (University of Guam, YCP)/Grant U54CA143727 (University of Hawaii Cancer Center). Citation Format: Gian M. Paras, Rodney S. Teria, Su Bin Jin, Grazyna Badowski, John Shepherd, Rose Grino. Epidemiological factors associated with breast density in Guam [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4515.

Evidence suggests that breast density and body mass index (BMI) are strong breast cancer risk factors; however, their interactive associations are unknown. Elucidation of these interactive associations may help to increase understanding of the causes of breast cancer and find effective interventions for susceptible subgroups. To explore the association of the interaction of mammographic breast density and BMI with breast cancer risks among premenopausal and postmenopausal women. This prospective observational cohort study used population-based data of the Korean National Cancer Screening Program embedded in the National Health Insurance Service database to evaluate the breast cancer risk of 3 248 941 premenopausal cancer-free women and 4 373 473 postmenopausal cancer-free women aged 40 years or older who underwent mammographic screening between January 1, 2009, and December 31, 2013, and were followed up until December 31, 2018. Statistical analysis was performed from June 1 to July 15, 2021. Breast Imaging Reporting and Data System (BI-RADS)-defined breast density (with a scale from 1 to 4, where 1 indicates almost entirely fat, 2 indicates scattered fibroglandular densities, 3 indicates heterogeneously dense tissue, and 4 indicates extremely dense tissue) and BMI levels classified according to the World Health Organization Asia-Pacific Region classification. Adjusted relative risk (aRR) of breast cancer during the follow-up period and interactions in additive and multiplicative scales. The study end point was the development of breast cancer. Of 3 248 941 premenopausal women (mean [SD] age, 44.6 [4.3] years) and 4 373 473 postmenopausal women (mean [SD] age, 59.6 [8.4] years) aged 40 years or older, 34 466 cases of breast cancer were identified among the premenopausal women, and 30 816 cases of breast cancer were identified among the postmenopausal women. Increased breast density was associated with an increased risk of breast cancer in both premenopausal and postmenopausal women across the BMI categories. Among premenopausal women, those in BI-RADS category 4 had an approximately 2-fold higher risk of breast cancer irrespective of BMI (all women: aRR, 2.36 [95% CI, 2.24-2.49]; underweight: aRR, 1.80 [95% CI, 1.25-2.59]; normal weight: aRR, 2.10 [95% CI, 1.93-2.28]; overweight: aRR, 2.47 [95% CI, 2.27-2.68]; obese: aRR, 2.87 [95% CI, 2.49-3.32]) than those with underweight status and in BI-RADS category 1. However, an association between BMI and the risk of breast cancer was found only in the postmenopausal women in all breast density categories compared with underweight women with BI-RADS category 1 (BI-RADS category 4, all women: aRR, 2.91 [95% CI, 2.78-3.04]; underweight: aRR, 2.74 [95% CI, 1.89-3.98]; normal weight: aRR, 3.05 [95% CI, 2.82-3.30]; overweight: aRR, 2.85 [95% CI, 2.67-3.04]; obese: aRR, 2.52 [95% CI, 2.22-2.88]). When the combined associations of breast density and BMI with the risk of breast cancer were considered, a high breast density and high BMI had a significant positive interaction on the additive scale for both premenopausal and postmenopausal women, especially the latter (premenopausal women: adjusted relative excess risk due to interaction, 0.53 [95% CI, 0.35-0.71]; postmenopausal women: adjusted relative excess risk due to interaction, 1.68 [95% CI, 1.26-2.10]). This study suggests that breast density and BMI interact synergistically to augment breast cancer risk, with a stronger association found among postmenopausal women. Both factors should be incorporated into risk stratification in a population-based screening for public health significance. Women with overweight or obesity and dense breast tissue might benefit from tailored early screening strategies to detect breast cancer.

Breast Imaging-Reporting and Data System (BI-RADS) classification in 51 excised palpable pediatric breast masses

BackgroundBreast cancer is one of the commonest malignancy cancer worldwide and the Breast Imaging-Reporting and Data System (BI-RADS) classification has been extensively utilised as an adjunct to histopathological examination for malignant breast diseases. This study aims to analyse the concordance between radiological and histopathological findings, demonstrate the high predictive value in the BI-RADS category and evaluate the impact of these findings on surgical intervention and treatment outcome.MethodsThis is a single-centre retrospective study, analysing patients who underwent radiological examination with BI-RADS categories 3, 4 and 5 followed by histopathological examination confirming the diagnosis based on breast core biopsy or excision specimen over 3 years.ResultsA total of 316 specimens from 310 patients were included in this study; 75 cases were categorised BI-RADS 3, 166 as BI-RADS 4 and 75 as BI-RADS 5. Of these, 66 (20.8%) patients in BI-RADS category 3, 82 (25.9%) in BI-RADS category 4 and 5 (1.6%) in BI-RADS category 5 were reported as benign on histopathological examination. Malignant cases were reported in nine (2.8%) cases in BI-RADS category 3, 84 (26.6%) in BI-RADS category 4 and 70 (22.2%) in BI-RADS category 5. The positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity were 63.9%, 88%, 94.48%, and 43.14%, respectively.ConclusionThere is a significant correlation between BI-RADS score and histopathological results of breast cancer. A higher BI-RADS score is associated with a higher possibility of malignancy (P < 0.001). Our institution’s performance is comparable to other previously published data.

To determine performance and utilization of screening breast ultrasonography (US) in women with dense breast tissue who underwent additional screening breast US in the 1st year since implementation of Connecticut Public Act 09-41 requiring radiologists to inform patients with heterogeneous or extremely dense breasts at mammography that they may benefit from such examination. Informed consent was waived for this institutional review board-approved, HIPAA-compliant retrospective review of 935 women with dense breasts at mammography who subsequently underwent handheld screening and whole-breast US from October 1, 2009, through September 30, 2010. Of 935 women, 614 (65.7%) were at low risk, 149 (15.9%) were at intermediate risk, and 87 (9.3%) were at high risk for breast cancer. Of the screening breast US examinations, in 701 (75.0%), results were classified as Breast Imaging Reporting and Data System (BI-RADS) category 1 or 2; in 187 (20.0%), results were classified as BI-RADS category 3; and in 47 (5.0%), results were classified as BI-RADS category 4. Of 63 aspirations or biopsies recommended and performed in 53 patients, in nine, lesions were BI-RADS category 3, and in 54, lesions were BI-RADS category 4. Among 63 biopsies and aspirations, three lesions were malignant (all BI-RADS category 4, diagnosed with biopsy). All three cancers were smaller than 1 cm, were found in postmenopausal patients, and were solid masses. One cancer was found in each risk group. In 44 of 935 (4.7%) patients, examination results were false-positive. Overall positive predictive value (PPV) for biopsy or aspirations performed in patients with BI-RADS category 4 masses was 6.5% (three of 46; 95% confidence interval [CI]: 1.7%, 19%). Overall cancer detection rate was 3.2 cancers per 1000 women screened (three of 935; 95% CI: 0.8 cancers per 1000 women screened, 10 cancers per 1000 women screened). Technologist-performed handheld screening breast US offered to women in the general population with dense breasts can aid detection of small mammographically occult breast cancers (cancer detection rate, 0.8-10 cancers per 1000 women screened), although the overall PPV is low.

Evaluating Hormone Therapy-associated Increases in Breast Density: Comparison Between Reported and Simultaneous Assignment of BI-RADS Categories, Visual Assessment, and Quantitative Analysis 1

Computerised assessment, the objective classification of breast density provides a second opinion to the radiologist in classifying breast density. Subjective classification of breast density by the radiologist involves using: i) Wolfe’s classification, ii) Breast Imaging Reporting and Data System (BI-RADS) classification, and iii) Tabar’s pattern. The objective of this study was to compare the results of breast density obtained by using a computerised assessment technique with the subjective assessment by BI-RADS classification and Tabar’s pattern from two-view computed radiography (CR) mammography. A computerised assessment technique to quantify breast density from two-view CR-mammography was developed using the MATLAB GUI applications that utilise basic MATLAB functionality. Hundred sets of CR-mammograms (from fifty cases) were initially classified by the radiologist (SR) into parenchymal patterns, according to BI-RADS schemes and Tabar’s pattern. Median age of the patients was 53.3 years (range of 40–69 years). The radiologist then reanalysed the CR-mammograms using the computerised technique. The correlation between computerised results with BI-RADS classification and Tabar’s pattern were analysed respectively. Classification performance of each class and pattern was also analysed. The breast density calculated using the computerised assessment technique correlated well with the subjective estimation of BI-RADS classification (0.82) and Tabar’s pattern (0.95). The computerised assessment technique correctly classified 78.3% and 73.2% of the total cases based on BI-RADS classification and Tabar’s pattern respectively. This computerised technique can be useful in providing a clinically accurate measurement of breast density and an assessment of future risk of developing breast cancer. This technique can also effectively assist in patient management.

To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software. Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification. The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively). Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk. On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.

Breast density is a strong predictor of breast cancer. However, the difference in risk between breast density categories C and D remains inadequately explored. Given the low occurrence of extremely dense breasts, this investigation is crucial because it may lead to modifications in screening techniques for those with these conditions. The objective of the study is to evaluate the difference in breast cancer risk among women undergoing mammography and biopsy at a tertiary referral hospital in Colombia, based on American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) results in categories of breast density C (heterogeneously dense) and D (extremely dense). Methods: This retrospective cross-sectional study recorded variables from the mammographic BI-RADS scale, as well as histological and clinical variables from digital medical records. A stratified analysis of lesion malignancy/benignity was conducted according to density category by mammography and histological findings. The association between mammographic breast density subclassification of dense breasts and the occurrence or not of pathology-defined malignancy was sought. Results: A total of 107 patients with breast density in categories C and D were included, with 88.7% having heterogeneously dense breasts. The frequency of breast cancer was 32%. Infiltrating ductal carcinoma was the most frequently diagnosed malignancy (N = 14). Ahigher BI-RADS category was correlated withbreast density grade D and malignancy. A statistically significant association (p <0.045, RR 1.95, CI: 1.12-3.50) was found when comparing breast density (categories C and D) with the risk of malignancy. The positive predictive value (PPV) varied across different BI-RADS categories (BI-RADS 4A 25% vs. 0%; p-value 0.005; BI-RADS 4B 50% vs 10.5%; p-value 0.032). Efforts and resources should be focused on patients with extremely dense breasts, emphasizing the importance of additional (individualized) screening. Breast density could change the PPV within each BI-RADS category. However, further studies are needed to define the risk associated with each breast density subcategory and within BI-RADS categories, as well as to assess the efficacy of additional screening in patients with extremely dense breasts.

ObjectiveTo develop a scoring system stratifying the malignancy risk of mammographic microcalcifications using the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS).Materials and MethodsOne hundred ninety-four lesions with microcalcifications for which surgical excision was performed were independently reviewed by two radiologists according to the 5th edition of BI-RADS. Each category's positive predictive value (PPV) was calculated and a scoring system was developed using multivariate logistic regression. The scores for benign and malignant lesions or BI-RADS categories were compared using an independent t test or by ANOVA. The area under the receiver operating characteristic curve (AUROC) was assessed to determine the discriminatory ability of the scoring system. Our scoring system was validated using an external dataset.ResultsAfter excision, 69 lesions were malignant (36%). The PPV of BI-RADS descriptors and categories for calcification showed significant differences. Using the developed scoring system, mean scores for benign and malignant lesions or BI-RADS categories were significantly different (p < 0.001). The AUROC of our scoring system was 0.874 (95% confidence interval, 0.840–0.909) and the PPV of each BI-RADS category determined by the scoring system was as follows: category 3 (0%), 4A (6.8%), 4B (19.0%), 4C (68.2%), and 5 (100%). The validation set showed an AUROC of 0.905 and PPVs of 0%, 8.3%, 11.9%, 68.3%, and 94.7% for categories 3, 4A, 4B, 4C, and 5, respectively.ConclusionA scoring system based on BI-RADS morphology and distribution descriptors could be used to stratify the malignancy risk of mammographic microcalcifications.

Objectives: To assess the performance of elastography (ES) and ultrasound (US) in predicting the malignancy of breast lesions and to compare their combined diagnostic value with that of magnetic resonance imaging (MRI).Materials and Methods: The study prospectively enrolled 242 female patients with dense breasts treated in 35 heath care facilities in China between November 2018 and October 2019. Based on conventional US and elastography, radiologists classified the degree of suspicion of breast lesions according to the US Breast Imaging Reporting and Data System (BI-RADS) criteria. The diagnostic value was compared between US BI-RADS and MRI BI-RADS, with pathological results used as the reference standard.Results: The results demonstrated that irregular tumor shape, a nonparallel growth orientation, indistinct margins, angular contours, microcalcifications, color Doppler flow and ES score on US imaging were significantly related to breast cancer in dense breasts (P=0.001; P=0.001; P=0.008; P<0.001; P=0.019; P=0.008; P=0.002, respectively). The sensitivity, specificity, PPV, NPV, accuracy and AUC of US BI-RADS category were 94.7%, 90.7%, 95.8%, 88.0%, 93.4% and 0.93 (95%CI, 0.88-0.97), respectively, while those of MRI BI-RADS category were 98.2%, 57.5%, 84.3%, 83.3%, 86.0% and 0.78 (95%CI, 0.71-0.85), respectively. MRI BI-RADS showed a significantly higher sensitivity than US BI-RADS (98.2% vs 94.7%, P=0.043), whereas US BI-RADS showed significantly higher specificity (90.7% vs 57.5%, P<0.001). US BI-RADS showed better diagnostic efficiency in differentiating nodules in dense breasts than MRI BI-RADS (AUC 0.93 vs 0.78, P<0.001).Conclusion: By combining the use of ES and conventional US, US BI-RADS had better diagnostic efficiency in differentiating nodules in dense breasts than MRI. For the diagnosis of malignant tumors in patients with dense breasts, MRI and US BI-RADS can be used as supplemental diagnostic tools to detect lesions, with US BI-RADS considered the preferred adjunctive resource.

Background Although Breast Imaging Reporting and Data System (BI-RADS) density classification has been used to assess future breast cancer risk, its reliability and validity are still debated in literature. Purpose To determine the association between overall longitudinal changes in mammographic breast density and breast cancer risk stratified by menopausal status. Materials and Methods In a retrospective cohort study using the Korean National Health Insurance Service database, women aged at least 40 years without a history of cancer who underwent three consecutive biennial mammographic screenings in 2009-2014 were followed up through December 2020. Participants were divided according to baseline breast density: fatty (BI-RADS categories a, b) versus dense (BI-RADS categories c, d) and then into subgroups on the basis of changes from the first to second and from second to third screenings. Women without change in breast density were used as the reference group. Main outcomes were incident breast cancer events, both invasive breast cancer and ductal carcinoma in situ. Cox proportion hazard regression was used to calculate the hazard ratio (HR) with adjustment for other covariables. Results Among 2 253 963 women (mean age, 59 years ± 9) there were 22 439 detected breast cancers. Premenopausal women with fatty breasts at the first screening had a higher risk of breast cancer as density increased in the second and third screenings (fatty-to-dense HR, 1.45 [95% CI: 1.27, 1.65]; dense-to-fatty HR, 1.53 [95% CI: 1.34, 1.74]; dense-to-dense HR, 1.93 [95% CI: 1.75, 2.13]). In premenopausal women with dense breasts at baseline, those in whom density continuously decreased had a 0.62-fold lower risk (95% CI: 0.56, 0.69). Similar results were observed in postmenopausal women, remaining significant after adjustment for baseline breast density or changes in body mass index (fatty-to-dense HR, 1.50 [95% CI: 1.39, 1.62]; dense-to-fatty HR, 1.42 [95% CI: 1.31, 1.53]; dense-to-dense HR, 1.62 [95% CI: 1.51, 1.75]). Conclusion In both premenopausal and postmenopausal women undergoing three consecutive biennial mammographic screenings, a consecutive increase in breast density augmented the future breast cancer risk whereas a continuous decrease was associated with a lower risk. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka et al in this issue.

Objective To assess the diagnostic value of virtual touch imaging quantification (VTIQ) technique combined with breast imaging reporting and data system (BI-RADS) classification for benign and malignant breast lesions. Methods A total of 172 breast lesions were selected from 172 patients who were admitted to the First Affiliated Hospital of Harbin Medical University from August 2016 to April 2017, and all the lesions were first examined by US and classified by BI-RADS, and then examined by elastograpy. Using the VTIQ mode, we can obtain both the quality mode map and the speed mode map, and record seven sets of SWV values for each lesion to calculate the average. Using the pathological results as the gold standard, the receiver operating characteristic (ROC) curves of VTIQ, BI-RADS classification method, and VTIQ combined with BI-RADS classification method in the identification of benign and malignant breast lesions were plotted to calculate the area under the curve and determine the cut-off value. The diagnostic efficacy of the three methods was compared. The biopsy rates of breast lesions calculated by BI-RADS classification and VTIQ combined with BI-RADS classification were also compared and analyzed. Results The area under the ROC curve of the BI-RADS classification was 0.762, and the best diagnostic boundary value was between BI-RADS 3 and 4a classification. The area under the ROC curve of VTIQ (SWV average) was 0.895, and the optimal diagnostic cut-off value was 3.13 m/s. The area under the ROC curve of the VTIQ combined with the BI-RADS classification was 0.908, which was significantly higher than that of the BI-RADS classification (Z=5.79, P<0.01). According to the ROC curve, the best diagnostic boundary value of VTIQ combined with BI-RADS classification was between BI-RADS 4a and 4b, and the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92.9%, 88.8%, 80.0%, 96.3%, and 90.1%, respectively. Compared with the BI-RADS classification, the diagnostic specificity, accuracy, and positive predictive value of the VTIQ combined with the BI-RADS classification increased by 32.8%, 20.9%, and 28.6%, respectively. According to the BI-RADS classification, there were 51 cases requiring biopsy among the 116 benign lesions (51/116, 43.9%), and there were only 16 patients requiring further biopsy according to the VTIQ combined with BI-RADS classification (16/116, 13.8%). There was a significant difference in the biopsy rate between them (χ2=25.71, P=0.00). Conclusion VTIQ technology combined with BI-RADS classification shows good diagnostic performance for benign and malignant breast lesions. Compared with BI-RADS classification alone, VTIQ technology combined with BI-RADS classification can improve the specificity and accuracy of diagnosis and reduce the clinical biopsy rate of benign lesions and unnecessary clinical interventions. Elasticity imaging technique can be used as an effective supplement and an auxiliary diagnostic method for the conventional ultrasound BI-RADS classification. Key words: Breast disease; Diagnosis, Differential; Ultrasonography; Breast imaging reporting and data system; Elasticity imaging techniques

Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures. Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists' reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided. There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race. After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening.

Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists