Literature DB >> 15551535

The quantitative analysis of mammographic densities.

J W Byng1, N F Boyd, E Fishell, R A Jong, M J Yaffe.   

Abstract

Quantitative classification of mammographic parenchyma based on radiological assessment has been shown to provide one of the strongest estimates of the risk of developing breast cancer. Existing classification schemes, however, are limited by coarse category scales. In addition, subjectivity can lead to sizeable interobserver and intraobserver variations. Here, we propose an interactive thresholding technique applied to digitized film-screen mammograms, which assesses the proportion of the mammographic image representing radiographically dense tissue. Observers viewed images on a CRT display and selected grey-level thresholds from which the breast and regions of dense tissue in the breast were identified. The proportion of radiographic density was then calculated from the image histogram. The technique was evaluated for the mammograms of 30 women and is well correlated (R > 0.91, Spearman coefficient) with a six-category subjective classification of radiographic density by radiologists. The technique was found to be very reliable with an intraclass correlation coefficient between observers typically R > 0.9. This technique may have a role in routine mammographic analysis for the purpose of assessing risk categories and as a tool in studies of the etiology of breast cancer, in particular for monitoring changes in breast parenchyma during potential preventive interventions.

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Year:  1994        PMID: 15551535     DOI: 10.1088/0031-9155/39/10/008

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  236 in total

1.  An investigation of the effects of mammographic acquisition parameters on a semiautomated quantitative measure of breast cancer risk.

Authors:  N J Hangiandreou; C J Mount; K R Brandt; J P Quam; A Manduca; C M Vachon; T A Sellers
Journal:  J Digit Imaging       Date:  2000-05       Impact factor: 4.056

2.  Mammographic density and risk of breast cancer by adiposity: an analysis of four case-control studies.

Authors:  Shannon M Conroy; Christy G Woolcott; Karin R Koga; Celia Byrne; Chisato Nagata; Giske Ursin; Celine M Vachon; Martin J Yaffe; Ian Pagano; Gertraud Maskarinec
Journal:  Int J Cancer       Date:  2011-09-17       Impact factor: 7.396

3.  Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling.

Authors:  R J Ferrari; R M Rangayyan; R A Borges; A F Frère
Journal:  Med Biol Eng Comput       Date:  2004-05       Impact factor: 2.602

4.  Mammographic breast density and breast cancer: evidence of a shared genetic basis.

Authors:  Jajini S Varghese; Deborah J Thompson; Kyriaki Michailidou; Sara Lindström; Clare Turnbull; Judith Brown; Jean Leyland; Ruth M L Warren; Robert N Luben; Ruth J Loos; Nicholas J Wareham; Johanna Rommens; Andrew D Paterson; Lisa J Martin; Celine M Vachon; Christopher G Scott; Elizabeth J Atkinson; Fergus J Couch; Carmel Apicella; Melissa C Southey; Jennifer Stone; Jingmei Li; Louise Eriksson; Kamila Czene; Norman F Boyd; Per Hall; John L Hopper; Rulla M Tamimi; Nazneen Rahman; Douglas F Easton
Journal:  Cancer Res       Date:  2012-01-19       Impact factor: 12.701

5.  A novel automated mammographic density measure and breast cancer risk.

Authors:  John J Heine; Christopher G Scott; Thomas A Sellers; Kathleen R Brandt; Daniel J Serie; Fang-Fang Wu; Marilyn J Morton; Beth A Schueler; Fergus J Couch; Janet E Olson; V Shane Pankratz; Celine M Vachon
Journal:  J Natl Cancer Inst       Date:  2012-07-03       Impact factor: 13.506

6.  The quantitative potential for breast tomosynthesis imaging.

Authors:  Christina M Shafer; Ehsan Samei; Joseph Y Lo
Journal:  Med Phys       Date:  2010-03       Impact factor: 4.071

Review 7.  Breast tissue composition and susceptibility to breast cancer.

Authors:  Norman F Boyd; Lisa J Martin; Michael Bronskill; Martin J Yaffe; Neb Duric; Salomon Minkin
Journal:  J Natl Cancer Inst       Date:  2010-07-08       Impact factor: 13.506

8.  Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Authors:  Songfeng Li; Jun Wei; Heang-Ping Chan; Mark A Helvie; Marilyn A Roubidoux; Yao Lu; Chuan Zhou; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Phys Med Biol       Date:  2018-01-09       Impact factor: 3.609

9.  Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI.

Authors:  Ke Nie; Jeon-Hor Chen; Siwa Chan; Man-Kwun I Chau; Hon J Yu; Shadfar Bahri; Tiffany Tseng; Orhan Nalcioglu; Min-Ying Su
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

10.  Using Speed of Sound Imaging to Characterize Breast Density.

Authors:  Mark Sak; Neb Duric; Peter Littrup; Lisa Bey-Knight; Haythem Ali; Patricia Vallieres; Mark E Sherman; Gretchen L Gierach
Journal:  Ultrasound Med Biol       Date:  2016-09-29       Impact factor: 2.998

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