Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The χ test was used to test for significant associations. Results Of 1 309 950 diagnostic mammograms, 125 447 (9.6%) were category 4, of which 33.3% (41 841 of 125 447) were subdivided. Subdivision utilization rates were higher (P < .001) in practices that were community, suburban, or in the West; for examination indication of prior history of breast cancer; and for the imaging finding of architectural distortion. Of 41 841 category 4 subdivided examinations, 4A constituted 55.6% (23 258 of 41 841) of the examinations; 4B, 31.8% (13 302 of 41 841) of the examinations; and 4C, 12.6% (5281 of 41 841) of the examinations. Pathologic outcomes were available in 91 563 examinations, and overall category 4 PPV3 was 21.1% (19 285 of 91 563). There was a statistically significant difference in PPV3 according to category 4 subdivision (P < .001): The PPV of 4A was 7.6% (1274 of 16 784), that of 4B was 22% (2317 of 10 408), and that of 4C was 69.3% (2839 of 4099). Conclusion Although BI-RADS suggests their use, subdivisions were utilized in the minority (33.3% [41 841 of 125 447]) of category 4 diagnostic mammograms, with variability based on facility and examination characteristics. When subdivisions were used, PPV3s were in BI-RADS-specified malignancy ranges. This analysis supports the use of subdivisions in broad practice and, given benefits for patient care, should motivate increased utilization. RSNA, 2018 Online supplemental material is available for this article.

OBJECTIVE BI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to ≤ 10%), 4B (> 10% to ≤ 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS–specified rates of malignancy for mammography and ultrasound. MATERIALS AND METHODS All screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated. RESULTS Of 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7). CONCLUSION Category 4 subdivisions for MRI yielded malignancy rates within BI-RADS–specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.

Rationale and Objectives The BI-RADS Atlas 5th Edition includes screening breast magnetic resonance imaging (MRI) outcome benchmarks. However, the metrics are from expert practices and clinical trials of women with hereditary breast cancer predispositions, and it is unknown if they are appropriate for routine practice. We evaluated screening breast MRI audit outcomes in routine practice across a spectrum of elevated risk patients. Materials and Methods This Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study included all consecutive screening breast MRI examinations from July 1, 2010 to June 30, 2013. Examination indications were categorized as gene mutation carrier (GMC), personal history (PH) breast cancer, family history (FH) breast cancer, chest radiation, and atypia/lobular carcinoma in situ (LCIS). Outcomes were determined by pathology and/or ≥12 months clinical and/or imaging follow-up. We calculated abnormal interpretation rate (AIR), cancer detection rate (CDR), positive predictive value of recommendation for tissue diagnosis (PPV2) and biopsy performed (PPV3), and median size and percentage of node-negative invasive cancers. Results Eight hundred and sixty examinations were performed in 566 patients with a mean age of 47 years. Indications were 367 of 860 (42.7%) FH, 365 of 860 (42.4%) PH, 106 of 860 (12.3%) GMC, 14 of 860 (1.6%) chest radiation, and 8 of 22 (0.9%) atypia/LCIS. The AIR was 134 of 860 (15.6%). Nineteen cancers were identified (13 invasive, 4 DCIS, two lymph nodes), resulting in CDR of 19 of 860 (22.1 per 1000), PPV2 of 19 of 88 (21.6%), and PPV3 of 19 of 80 (23.8%). Of 13 invasive breast cancers, median size was 10 mm, and 8 of 13 were node negative (61.5%). Conclusions Performance outcomes of screening breast MRI in routine clinical practice across a spectrum of elevated risk patients met the American College of Radiology Breast Imaging Reporting and Data System benchmarks, supporting broad application of these metrics. The indication of a personal history of treated breast cancer accounted for a large proportion (42%) of our screening examinations, with breast MRI performance in this population at least comparable to that of other screening indications.