Literature DB >> 11887947

Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences.

John J Heine1, Poonam Malhotra.   

Abstract

The work presented herein is the second part of a review of breast tissue-related cancer-risk research. Briefly, in part 1, the tissue-risk research is discussed. In this part, factors that influence temporal breast tissue change are reviewed. Since breast composition is correlated with some of the known risk factors, understanding these influences may provide a mechanism for measuring the dynamics of breast cancer risk. The purpose of this work is to provide support for an automated serial mammography study under way at the authors' institution, where the digital mammographic images are acquired with a full-field digital mammography imaging system. At the initiation of the serial study, it was clear that the authors did not fully understand the nature of the problem: automatically comparing similar mammographic scenes acquired at different times. The evidence indicates that there are many factors that influence breast tissue at any given time and thus have the ability to alter the associated radiographic appearance over time. In general, the topics considered herein include aging; involution; breast development; exogenous and endogenous hormonal interactions such as hormone replacement therapy, oral contraceptive use, and menstrual timing; screening sensitivity issues and interval cancers; tumor growth rates; sojourn times; and lifestyle factors such as diet and exercise. Throughout this work, commentaries and suggestive strategies for automated serial image analysis are provided.

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Year:  2002        PMID: 11887947     DOI: 10.1016/s1076-6332(03)80374-4

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  15 in total

1.  Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study.

Authors:  Huanjun Ding; Sabee Molloi
Journal:  Phys Med Biol       Date:  2012-07-06       Impact factor: 3.609

2.  Breast density across a regional screening population: effects of age, ethnicity and deprivation.

Authors:  Samantha L Heller; Sue Hudson; Louise S Wilkinson
Journal:  Br J Radiol       Date:  2015-09-02       Impact factor: 3.039

3.  Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

Authors:  Brad M Keller; Andrew Oustimov; Yan Wang; Jinbo Chen; Raymond J Acciavatti; Yuanjie Zheng; Shonket Ray; James C Gee; Andrew D A Maidment; Despina Kontos
Journal:  J Med Imaging (Bellingham)       Date:  2015-04-03

4.  Classification of breast computed tomography data.

Authors:  Thomas R Nelson; Laura I Cerviño; John M Boone; Karen K Lindfors
Journal:  Med Phys       Date:  2008-03       Impact factor: 4.071

5.  Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment.

Authors:  Despina Kontos; Lynda C Ikejimba; Predrag R Bakic; Andrea B Troxel; Emily F Conant; Andrew D A Maidment
Journal:  Radiology       Date:  2011-07-19       Impact factor: 11.105

6.  Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

Authors:  Yuanjie Zheng; Brad M Keller; Shonket Ray; Yan Wang; Emily F Conant; James C Gee; Despina Kontos
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

7.  Validation of a method for measuring the volumetric breast density from digital mammograms.

Authors:  O Alonzo-Proulx; N Packard; J M Boone; A Al-Mayah; K K Brock; S Z Shen; M J Yaffe
Journal:  Phys Med Biol       Date:  2010-05-12       Impact factor: 3.609

8.  Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study.

Authors:  Despina Kontos; Predrag R Bakic; Ann-Katherine Carton; Andrea B Troxel; Emily F Conant; Andrew D A Maidment
Journal:  Acad Radiol       Date:  2009-03       Impact factor: 3.173

9.  Empirically-derived synthetic populations to mitigate small sample sizes.

Authors:  Erin E Fowler; Anders Berglund; Michael J Schell; Thomas A Sellers; Steven Eschrich; John Heine
Journal:  J Biomed Inform       Date:  2020-03-12       Impact factor: 6.317

Review 10.  Insulin receptor substrates (IRSs) and breast tumorigenesis.

Authors:  Bonita Tak-Yee Chan; Adrian V Lee
Journal:  J Mammary Gland Biol Neoplasia       Date:  2008-11-22       Impact factor: 2.673

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