Literature DB >> 9648759

The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories.

L Liberman1, A F Abramson, F B Squires, J R Glassman, E A Morris, D D Dershaw.   

Abstract

OBJECTIVE: The purpose of the study was to assess the positive predictive value of mammographic features and final assessment categories described in the Breast Imaging Reporting and Data System (BI-RADS) for lesions on which biopsies have been performed. SUBJECTS AND METHODS: We prospectively evaluated 492 impalpable mammographically detected lesions on which surgical biopsy (as opposed to percutaneous biopsy) was performed. Each lesion was classified according to BI-RADS descriptors for masses (margins and shape) and calcifications (morphology and distribution) and was categorized by the BI-RADS final assessment categories as category 3 ("probably benign"), category 4 ("suspicious abnormality"), or category 5 ("highly suggestive of malignancy"). Mammographic and pathologic findings were reviewed.
RESULTS: Carcinoma was present in 225 (46%) of 492 lesions. For the 492 lesions subject to biopsy, BI-RADS final assessment categories were category 3 in eight lesions (2%), category 4 in 355 (72%), and category 5 in 129 (26%). The features with highest positive predictive value for carcinoma were spiculated margins (81%), irregular shape (73%), linear calcification morphology (81%), and segmental or linear calcification distribution (74% and 68%, respectively). Carcinoma was present in 105 (81%) of 129 category 5 lesions compared with 120 (34%) of 355 category 4 lesions (p < .001). The frequency of carcinoma was higher in category 5 than in category 4 lesions for all mammographic lesion types and all interpreting radiologists.
CONCLUSION: The standardized terminology of the BI-RADS lexicon allows quantification of the likelihood of carcinoma in an impalpable breast lesion. The features with highest positive predictive value--spiculated margins, irregular shape, linear morphology, and segmental or linear distribution--warrant designation of a lesion as category 5.

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Mesh:

Year:  1998        PMID: 9648759     DOI: 10.2214/ajr.171.1.9648759

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  72 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

Review 2.  Imaging for the diagnosis and management of ductal carcinoma in situ.

Authors:  Carl J D'Orsi
Journal:  J Natl Cancer Inst Monogr       Date:  2010

3.  External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

Authors:  Matthias Benndorf; Elizabeth S Burnside; Christoph Herda; Mathias Langer; Elmar Kotter
Journal:  Med Phys       Date:  2015-08       Impact factor: 4.071

4.  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

5.  Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation.

Authors:  Yirong Wu; Craig K Abbey; Xianqiao Chen; Jie Liu; David C Page; Oguzhan Alagoz; Peggy Peissig; Adedayo A Onitilo; Elizabeth S Burnside
Journal:  J Med Imaging (Bellingham)       Date:  2015-08-17

Review 6.  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

7.  Ductal carcinoma in situ: correlations between high-resolution magnetic resonance imaging and histopathology.

Authors:  Yoshihide Kanemaki; Yasuyuki Kurihara; Kyoko Okamoto; Yasuo Nakajima; Mamoru Fukuda; Ichiro Maeda; Futoshi Akiyama
Journal:  Radiat Med       Date:  2007-01-25

8.  Comparison of algorithms to enhance spicules of spiculated masses on mammography.

Authors:  Mehul P Sampat; Gary J Whitman; Alan C Bovik; Mia K Markey
Journal:  J Digit Imaging       Date:  2008-03       Impact factor: 4.056

9.  Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

Authors:  Katrina Armstrong; Elizabeth A Handorf; Jinbo Chen; Mirar N Bristol Demeter
Journal:  Am J Prev Med       Date:  2013-01       Impact factor: 5.043

10.  Quantitative assessment of in vivo breast masses using ultrasound attenuation and backscatter.

Authors:  Kibo Nam; James A Zagzebski; Timothy J Hall
Journal:  Ultrason Imaging       Date:  2013-04       Impact factor: 1.578

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