BI-RADS is an acronym for Breast Imaging-Reporting and Data System, a quality assurance tool originally designed for use with mammography. The system is a collaborative effort of many health groups but is published and trademarked by the American College of Radiology (ACR).
The system is designed to standardize reporting, and is used by medical professionals to communicate a patient's risk of developing breast cancer. The document focuses on patient reports used by medical professionals, not "lay reports" that are provided to patients.
The BI-RADS is published by ACR in the form of the BI-RADS Atlas. As of 2013 the Atlas is divided into 3 publications:
While BI-RADS is a quality control system, in day-to-day usage the term "BI-RADS" refers to the mammography assessment categories. These are standardized numerical codes typically assigned by a radiologist after interpreting a mammogram. This allows for concise and unambiguous understanding of patient records between multiple doctors and medical facilities.
The assessment categories were developed for mammography and later adapted for use with MRI and Ultrasound findings. The summary of each category, given below, is nearly identical for all 3 modalities.
Category 6 was added in the 4th edition of the BI-RADS.
BI-RADS Assessment Categories are:
- 0: Incomplete
- 1: Negative
- 2: Benign
- 3: Probably benign
- 4: Suspicious
- 5: Highly suggestive of malignancy
- 6: Known biopsy – proven malignancy
An incomplete (BI-RADS 0) classification warrants either an effort to ascertain prior imaging for comparison or to call the patient back for additional views and/or higher quality films. A BI-RADS classification of 4 or 5 warrants biopsy to further evaluate the offending lesion. Some experts believe that the single BI-RADS 4 classification does not adequately communicate the risk of cancer to doctors and recommend a subclassification scheme:
- 4A: low suspicion for malignancy, about 2%
- 4B: intermediate suspicion of malignancy, about 10%
- 4C: moderate concern, but not classic for malignancy, about 50%
Breast Composition Categories
As of the BI-RADS 5th edition 
- a. The breasts are almost entirely fatty
- b. There are scattered areas of fibroglandular density
- c. The breasts are heterogeneously dense, which may obscure small masses
- d. The breasts are extremely dense, which lowers the sensitivity of mammography
- "Bi-RADS® for: mammography and ultrasound (2013 updated version) (PDF Download Available)". ResearchGate. doi:10.13140/rg.2.2.24908.82562/1.
- American College of Radiology (ACR) Breast Imaging Reporting and Data System Atlas (BI-RADS Atlas). Reston, Va: © American College of Radiology; 2003
- ACR Practice Guideline for the Performance of Ultrasound-Guided Percutaneous Breast Interventional Procedures Res. 29; American College of Radiology; 2009
- Sanders, M. A.; Roland, L.; Sahoo, S. (2010). "Clinical Implications of Subcategorizing BI-RADS 4 Breast Lesions associated with Microcalcification: A Radiology–Pathology Correlation Study". The Breast Journal. 16 (1): 28–31. doi:10.1111/j.1524-4741.2009.00863.x. PMID 19929890.
- D'Orsi CJ, Sickles EA, Mendelson EB, Morris EA, et al. (2013). ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology.
- Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David (2009). "Information Extraction for Clinical Data Mining: A Mammography Case Study" (PDF). IEEE International Conference on Data Mining (ICDM'09) Workshops. Miami: 37–42.
- Nassif, Houssam; Cunha, Filipe; Moreira, Ines C; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Ines (2012). "Extracting BI-RADS Features from Portuguese Clinical Texts" (PDF). IEEE International Conference on Bioinformatics and Biomedicine (BIBM'12): 539–542.
- Sippo, Dorothy A; Warden, Graham I; Andriole, Katherine P; Lacson, Ronilda; Ikuta, Ichiro; Birdwell, Robyn L; Khorasani, Ramin (2013). "Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing". Journal of Digital Imaging. 26 (5): 989–994. doi:10.1007/s10278-013-9616-5.
- Percha, Bethany; Nassif, Houssam; Lipson, Jafi; Burnside, Elizabeth; Rubin, Daniel (2012). "Automatic classification of mammography reports by BI-RADS breast tissue composition class" (PDF). Journal of the American Medical Informatics Association (JAMIA). 19 (5): 913–916.