Literature DB >> 8479209

Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions.

A M Knutzen1, J J Gisvold.   

Abstract

To determine the likelihood of malignant disease for mammographically detected, nonpalpable breast lesions, we defined 11 morphologic categories and retrospectively reviewed the mammograms in 859 cases in which biopsy was performed after a wire localization procedure at our institution during 1989 and 1990. Within each category, the total number of lesions and the percentage of cases with a surgical pathologic diagnosis of malignant involvement were as follows: benign calcification, 25 (0% malignant); indeterminate calcification, 200 (22%); malignant calcification, 39 (92%); smooth mass, 84 (1%); irregular mass, 337 (40%); architectural distortion, 45 (47%); asymmetric breast tissue, 37 (3%, or 1 case of asymmetrically prominent ducts); smooth mass with calcification, 3 (0%); irregular mass with calcification, 68 (66%); architectural distortion with calcification, 14 (57%); and asymmetric breast tissue with calcification, 7 (29%). The overall rate of malignant involvement for the 859 cases was 34%. If follow-up examinations rather than biopsies had been done for the lesions categorized as benign calcification, smooth mass, smooth mass with calcification, and asymmetric breast tissue (excluding asymmetrically prominent ducts), the overall positive predictive value would have increased from 34 to 41%, and 148 biopsies would have been deferred (17% of all biopsies). If morphologic criteria are applied to the evaluation of mammographically detected, nonpalpable lesions, the rate of malignant disease at biopsy may reach 40%. This rate correlates with that in recent large series.

Entities:  

Mesh:

Year:  1993        PMID: 8479209     DOI: 10.1016/s0025-6196(12)60194-3

Source DB:  PubMed          Journal:  Mayo Clin Proc        ISSN: 0025-6196            Impact factor:   7.616


  21 in total

1.  Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.

Authors:  Shantanu Banik; Rangaraj M Rangayyan; J E Leo Desautels
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-03-30       Impact factor: 2.924

2.  Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; J E Leo Desautels
Journal:  J Vis Exp       Date:  2013-08-30       Impact factor: 1.355

3.  Monckeberg medial calcific sclerosis mimicking malignant calcification pattern at mammography.

Authors:  A Saxena; I C Waddell; R W Friesen; R T Michalski
Journal:  J Clin Pathol       Date:  2005-04       Impact factor: 3.411

4.  Number of mammography cases read per year is a strong predictor of sensitivity.

Authors:  Wasfi I Suleiman; Sarah J Lewis; Dianne Georgian-Smith; Michael G Evanoff; Mark F McEntee
Journal:  J Med Imaging (Bellingham)       Date:  2014-05-07

5.  Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.

Authors:  Qi Guo; Jiaqing Shao; Virginie F Ruiz
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

6.  Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; Jayasree Chakraborty; Sudipta Mukhopadhyay; J E Leo Desautels
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-09-30       Impact factor: 2.924

7.  Outcomes of classic lobular neoplasia diagnosed on breast core needle biopsy: a retrospective multi-center study.

Authors:  Iskender Sinan Genco; Bugra Tugertimur; Qing Chang; Lauren Cassell; Sabina Hajiyeva
Journal:  Virchows Arch       Date:  2019-11-27       Impact factor: 4.064

8.  MRI in the differential diagnosis of primary architectural distortion detected by mammography.

Authors:  Lifang Si; Renyou Zhai; Xiaojuan Liu; Kaiyan Yang; Li Wang; Tao Jiang
Journal:  Diagn Interv Radiol       Date:  2016 Mar-Apr       Impact factor: 2.630

9.  Positive predictive value of specific mammographic findings according to reader and patient variables.

Authors:  Aruna Venkatesan; Philip Chu; Karla Kerlikowske; Edward A Sickles; Rebecca Smith-Bindman
Journal:  Radiology       Date:  2009-01-21       Impact factor: 11.105

Review 10.  Strategies to Increase Cancer Detection: Review of True-Positive and False-Negative Results at Digital Breast Tomosynthesis Screening.

Authors:  Katrina E Korhonen; Susan P Weinstein; Elizabeth S McDonald; Emily F Conant
Journal:  Radiographics       Date:  2016-10-07       Impact factor: 5.333

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.