Literature DB >> 12917203

Wolfe's parenchymal pattern and percentage of the breast with mammographic densities: redundant or complementary classifications?

Jacques Brisson1, Caroline Diorio, Benoît Mâsse.   

Abstract

Mammographic breast densities are one of the strongest breast cancer risk factors. The two most frequently used classifications of breast densities are Wolfe's parenchymal pattern and the percentage of the breast with densities. In this analysis, associations of these two classifications with breast cancer risk were compared, and the dose response curve of risk with densities was examined. Three case-control studies were combined totaling 1060 cases with newly diagnosed breast cancer and 2352 controls. A single observer had assessed parenchymal pattern and percent density without any information on subjects. Relative risks (RRs) were estimated with logistic regression and spline functions adjusting for age and body weight. The two classifications were strongly correlated (r = 0.81, P = 0.0001). Breast cancer risk increased progressively with percent density reaching a 5-6-fold increase for women with 85% or more of the breast with densities compared with women with no density. In contrast, women with P2 or DY patterns had only a 2-3-fold increase in risk compared with women with N1 pattern. More importantly, among women with P2 or DY, RR varied substantially with percent density, whereas, among women with a given percent density, RR varied little with parenchymal pattern. Comparisons of multivariate models reveal that in the presence of parenchymal pattern, inclusion of percent density in the model improved the prediction of breast cancer risk (chi(2) = 35.5, P = 0.0082) but not the opposite (chi(2) = 1.1, P = 0.7662). These findings show that the percentage of the breast with densities provide more information on breast cancer risk than Wolfe's parenchymal patterns and that, once percent breast density is taken into account, no more information on breast cancer risk is given by assessing parenchymal pattern.

Entities:  

Mesh:

Year:  2003        PMID: 12917203

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  23 in total

1.  Interobserver agreement in breast radiological density attribution according to BI-RADS quantitative classification.

Authors:  D Bernardi; M Pellegrini; S Di Michele; P Tuttobene; C Fantò; M Valentini; M Gentilini; S Ciatto
Journal:  Radiol Med       Date:  2012-01-07       Impact factor: 3.469

2.  Consistency of visual assessments of mammographic breast density from vendor-specific "for presentation" images.

Authors:  Mohamed Abdolell; Kaitlyn Tsuruda; Christopher B Lightfoot; Eva Barkova; Melanie McQuaid; Judy Caines; Sian E Iles
Journal:  J Med Imaging (Bellingham)       Date:  2015-10-30

3.  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

4.  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

5.  Methods for assessing and representing mammographic density: an analysis of 4 case-control studies.

Authors:  Christy G Woolcott; Shannon M Conroy; Chisato Nagata; Giske Ursin; Celine M Vachon; Martin J Yaffe; Ian S Pagano; Celia Byrne; Gertraud Maskarinec
Journal:  Am J Epidemiol       Date:  2013-10-11       Impact factor: 4.897

6.  Associations of aspirin and other anti-inflammatory medications with mammographic breast density and breast cancer risk.

Authors:  Lusine Yaghjyan; Akemi Wijayabahu; A Heather Eliassen; Graham Colditz; Bernard Rosner; Rulla M Tamimi
Journal:  Cancer Causes Control       Date:  2020-05-31       Impact factor: 2.506

7.  Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls.

Authors:  Hui Li; Maryellen L Giger; Li Lan; Jyothi Janardanan; Charlene A Sennett
Journal:  J Med Imaging (Bellingham)       Date:  2014-11-13

8.  Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.

Authors:  Hui Li; Maryellen L Giger; Li Lan; Jeremy Bancroft Brown; Aoife MacMahon; Mary Mussman; Olufunmilayo I Olopade; Charlene Sennett
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

9.  Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems.

Authors:  Kayla R Mendel; Hui Li; Li Lan; Cathleen M Cahill; Victoria Rael; Hiroyuki Abe; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2017-09-19

10.  Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification.

Authors:  Xingwei Wang; Dror Lederman; Jun Tan; Xiao Hui Wang; Bin Zheng
Journal:  Acad Radiol       Date:  2010-10       Impact factor: 3.173

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.