Literature DB >> 10401738

Positive predictive value of the Breast Imaging Reporting and Data System.

M A Lacquement1, D Mitchell, A B Hollingsworth.   

Abstract

BACKGROUND: The American College of Radiology has established guidelines for outcomes monitoring known as the Breast Imaging Reporting and Data System (BIRADS). These recommendations include calculation of positive predictive values (PPV) and tracking of both benign and malignant histology. We collected this data for 688 radiographically guided biopsies and organized it according to the BIRADS assessment categories. The objective was to evaluate the contribution of the BIRAD System when used to stratify PPV, histology, and biopsy modality data according to the overall assessment rating. STUDY
DESIGN: This study included data from 688 image-guided biopsies. Mammographic studies were either assigned a BIRADS rating at the time of examination or, if the image was taken before our use of BIRADS, examined retrospectively and rated. In these retrospective cases, the histologic outcomes of the biopsy remained unknown to the radiologist until ratings were assigned. Positive predictive value was calculated for each BIRADS category.
RESULTS: The overall PPV for the sample was 0.23. The PPVs increased with increasing level of suspicion as follows: category 1 (0.0), category 2 (0.04), category 3 (0.03), category 4 (0.23), category 5 (0.92). Category 1 lesions represented 0.1% of the biopsies; category 2, 3.6%; category 3, 46.8%; category 4, 34.0%; and category 5, 15.4%. The most common histologic diagnoses of benign lesions biopsied were fibroadenoma and fibrocystic changes-proliferative and nonproliferative. The most common histologic diagnoses of malignant lesions biopsied were infiltrating ductal carcinoma and ductal carcinoma in situ. Utilization rates of the biopsy techniques varied by BIRADS category.
CONCLUSIONS: Our study revealed that BIRADS does improve the quality of the risk assessment information by making the PPV more specific to a patient's mammogram rather than simply related to an overall PPV. Our histology analysis showed category 3 and category 4 benign biopsies were predominantly because of fibrocystic changes. Category 5 lesions were predominantly invasive ductal carcinoma. Analysis of biopsy modalities indicated the preferred method for management of radiographically detected lesions evolved from stereotactic core biopsy to directional, vacuum-assisted biopsy over the course of the study.

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Mesh:

Year:  1999        PMID: 10401738     DOI: 10.1016/s1072-7515(99)00080-0

Source DB:  PubMed          Journal:  J Am Coll Surg        ISSN: 1072-7515            Impact factor:   6.113


  18 in total

1.  The positive predictive value of the breast imaging reporting and data system (BI-RADS) as a method of quality assessment in breast imaging in a hospital population.

Authors:  Harmine M Zonderland; Thomas L Pope; Arend J Nieborg
Journal:  Eur Radiol       Date:  2004-07-09       Impact factor: 5.315

2.  A data-driven approach for quality assessment of radiologic interpretations.

Authors:  William Hsu; Simon X Han; Corey W Arnold; Alex At Bui; Dieter R Enzmann
Journal:  J Am Med Inform Assoc       Date:  2015-11-25       Impact factor: 4.497

Review 3.  Applications and literature review of the BI-RADS classification.

Authors:  S Obenauer; K P Hermann; E Grabbe
Journal:  Eur Radiol       Date:  2005-01-26       Impact factor: 5.315

4.  Stereotactic core-needle breast biopsy by surgeons: minimum 2-year follow-up of benign lesions.

Authors:  R P Burns; J P Brown; S M Roe; L R Sprouse; A E Yancey; L E Witherspoon
Journal:  Ann Surg       Date:  2000-10       Impact factor: 12.969

5.  Assessing Inaccuracies in Automated Information Extraction of Breast Imaging Findings.

Authors:  Ronilda Lacson; Martha E Goodrich; Kimberly Harris; Phyllis Brawarsky; Jennifer S Haas
Journal:  J Digit Imaging       Date:  2017-04       Impact factor: 4.056

6.  Automated annotation and classification of BI-RADS assessment from radiology reports.

Authors:  Sergio M Castro; Eugene Tseytlin; Olga Medvedeva; Kevin Mitchell; Shyam Visweswaran; Tanja Bekhuis; Rebecca S Jacobson
Journal:  J Biomed Inform       Date:  2017-04-18       Impact factor: 6.317

7.  BI-RADS categorisation of 2,708 consecutive nonpalpable breast lesions in patients referred to a dedicated breast care unit.

Authors:  A-S Hamy; S Giacchetti; M Albiter; C de Bazelaire; C Cuvier; F Perret; S Bonfils; P Charvériat; H Hocini; A de Roquancourt; M Espie
Journal:  Eur Radiol       Date:  2011-07-16       Impact factor: 5.315

8.  The mammographic density of a mass is a significant predictor of breast cancer.

Authors:  Ryan W Woods; Gale S Sisney; Lonie R Salkowski; Kazuhiko Shinki; Yunzhi Lin; Elizabeth S Burnside
Journal:  Radiology       Date:  2010-12-21       Impact factor: 11.105

9.  Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.

Authors:  Joana Diz; Goreti Marreiros; Alberto Freitas
Journal:  J Med Syst       Date:  2016-08-06       Impact factor: 4.460

10.  B3-lesions of the breast and cancer risk - an analysis of mammography screening patients.

Authors:  Oliver Hoffmann; Gesina Athina Stamatis; Ann-Kathrin Bittner; Georg Arnold; Rolf Schnabel; Karlgeorg Krüger; Rainer Kimmig; Martin Heubner
Journal:  Mol Clin Oncol       Date:  2016-02-23
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