Literature DB >> 11887946

Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors.

John J Heine1, Poonam Malhotra.   

Abstract

This work is presented as a sequence of two parts. In this leading section, a review of the breast tissue-risk research is provided. Although controversy remains, there is substantial evidence indicating that dense mammographic tissue (a) is a breast cancer risk factor that is at least similar, if not greater, in magnitude with the other known breast cancer risk factors and (b) may be a partial biomarker for some of the other risk factors. Understanding these influences may provide a mechanism for measuring the dynamics of breast cancer risk. The totality of this work is to provide support for an automated serial mammography study under way at the authors' institution, where digital mammographic images are acquired with a full-field digital mammography system. This is a filmless imaging system, where the image is acquired in digital format. This electronic imaging acquisition system provides a prime opportunity to easily couple and manipulate the image data with patient information such as risk probability analysis or other pertinent personal history data for improved automated decision making. In this leading section, the main focus is on understanding elements that will assist in fusing risk probability analysis with automated computer-aided diagnosis. 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 image appearance over time. 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. In the second part of this sequence, the more time-related tissue influences are reviewed.

Entities:  

Mesh:

Year:  2002        PMID: 11887946     DOI: 10.1016/s1076-6332(03)80373-2

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


  23 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.  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

3.  Evaluating the effect of a wavelet enhancement method in characterization of simulated lesions embedded in dense breast parenchyma.

Authors:  L Costaridou; S Skiadopoulos; P Sakellaropoulos; E Likaki; C P Kalogeropoulou; G Panayiotakis
Journal:  Eur Radiol       Date:  2005-02-09       Impact factor: 5.315

4.  Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Authors:  Lee-Jane W Lu; Thomas K Nishino; Tuenchit Khamapirad; James J Grady; Morton H Leonard; Donald G Brunder
Journal:  Phys Med Biol       Date:  2007-07-30       Impact factor: 3.609

5.  Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment.

Authors:  Hui Li; Maryellen L Giger; Olufunmilayo I Olopade; Michael R Chinander
Journal:  J Digit Imaging       Date:  2008-01-03       Impact factor: 4.056

6.  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

7.  The Effect of Atorvastatin on Breast Cancer Biomarkers in High-Risk Women.

Authors:  YongLi Ji; Tiffany Rounds; Abigail Crocker; Betsy Sussman; Russell C Hovey; Fonda Kingsley; Hyman B Muss; Judy E Garber; Marie E Wood
Journal:  Cancer Prev Res (Phila)       Date:  2016-02-23

8.  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

9.  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

10.  Calibrated measures for breast density estimation.

Authors:  John J Heine; Ke Cao; Dana E Rollison
Journal:  Acad Radiol       Date:  2011-03-02       Impact factor: 3.173

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