BI-RADS

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, particularly for patients with dense breast tissue. The document focuses on patient reports used by medical professionals, not "lay reports" that are provided to patients.

Published Documents

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:

  • Mammography, Fifth Edition
  • Ultrasound, Second Edition
  • MRI, Second Edition

Assessment Categories

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.[1]

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:[2]

  • 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.[3] 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:[4]

  • 4A: low suspicion for malignancy, about > 2% to ≤ 10% likelihood of malignancy
  • 4B: intermediate suspicion of malignancy, about > 10% to ≤ 50% likelihood of malignancy
  • 4C: moderate concern, but not classic for malignancy, about > 50% to < 95% likelihood of malignancy

Breast Composition Categories

As of the BI-RADS 5th edition [5]

  • 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

Automated Extraction

Automatic parsers have been developed to automatically extract BI-RADS features,[6][7] categories[8] and breast composition[9] from textual mammography reports.

There is also automatic parser available for BI-RADS final category inference by parsing only the semi-formatted finding section of the textual mammography report [10]

References
  1. Mehrjardi, Mohammad Zare (2015). "Bi-RADS® for: mammography and ultrasound (2013 updated version) (PDF Download Available)". ResearchGate. doi:10.13140/rg.2.2.24908.82562/1.
  2. American College of Radiology (ACR) Breast Imaging Reporting and Data System Atlas (BI-RADS Atlas). Reston, Va: © American College of Radiology; 2003
  3. ACR Practice Guideline for the Performance of Ultrasound-Guided Percutaneous Breast Interventional Procedures Res. 29; American College of Radiology; 2009
  4. 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. S2CID 9585100.
  5. 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.
  6. 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. doi:10.1109/icdmw.2009.63. ISBN 978-1-4244-5384-9. PMC 3676897. PMID 23765123.
  7. 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". IEEE International Conference on Bioinformatics and Biomedicine (BIBM'12): 539–542. doi:10.1109/bibm.2012.6392613. ISBN 978-1-4673-2560-8. PMC 3688645. PMID 23797461.
  8. 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. PMC 3782591. PMID 23868515.
  9. Percha, Bethany; Nassif, Houssam; Lipson, Jafi; Burnside, Elizabeth; Rubin, Daniel (2012). "Automatic classification of mammography reports by BI-RADS breast tissue composition class". Journal of the American Medical Informatics Association. 19 (5): 913–916. doi:10.1136/amiajnl-2011-000607. PMC 3422822. PMID 22291166.
  10. Banerjee, Imon; Bozkurt, Selen; Alkim, Emel; Sagreiya, Hersh; Kurian, Allison W; Rubin, Daniel L (2019-04-01). "Automatic inference of BI-RADS final assessment categories from narrative mammography report findings". Journal of Biomedical Informatics. 92: 103137. doi:10.1016/j.jbi.2019.103137. PMC 6462247. PMID 30807833.
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