Literature DB >> 3495132

Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study.

J N Wolfe, A F Saftlas, M Salane.   

Abstract

The classification of breast parenchymal patterns (N1, P1, P2, DY) and the percentage of the breast containing radiographic densities are two highly correlated radiographic measures proposed as predictors of the risk of breast cancer. In this case-control study, 160 cases of breast cancer and 160 matched controls from a mammography referral practice were compared to determine the risk of breast cancer associated with each of these two radiographic measures. The mammographic densities were quantified on caudal projections by means of a compensating polar planimeter. A relative risk estimate of 3.3 (p less than .05) was associated with the P2 + DY patterns compared with the N1 + P1 patterns. Significantly elevated risks of 4.3 to 5.5 also were observed among women whose breasts contained at least 25% mammographic densities, compared with women with less than 25% involvement. These radiographic measures tended to be more predictive of the risk of breast cancer in black women than in white women. Although the precise clinical roles of breast parenchymal patterns and densities have not been defined fully, the results of this study suggest that they are useful in the recognition of women at high risk of breast cancer. We make no claims that the findings of this study are sufficiently developed to be used as a basis for screening strategies.

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Year:  1987        PMID: 3495132     DOI: 10.2214/ajr.148.6.1087

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  63 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

Review 2.  Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention.

Authors:  N F Boyd; L J Martin; J Stone; C Greenberg; S Minkin; M J Yaffe
Journal:  Curr Oncol Rep       Date:  2001-07       Impact factor: 5.075

Review 3.  Clinical and epidemiological issues in mammographic density.

Authors:  Valentina Assi; Jane Warwick; Jack Cuzick; Stephen W Duffy
Journal:  Nat Rev Clin Oncol       Date:  2011-12-06       Impact factor: 66.675

4.  Dietary fat and breast cancer risk: the feasibility of a clinical trial of breast cancer prevention.

Authors:  N F Boyd; M Cousins; G Lockwood; D Tritchler
Journal:  Lipids       Date:  1992-10       Impact factor: 1.880

5.  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

6.  Quantification of breast density with dual energy mammography: a simulation study.

Authors:  Justin L Ducote; Sabee Molloi
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

7.  Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.

Authors:  Shandong Wu; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

8.  Breast density estimation from high spectral and spatial resolution MRI.

Authors:  Hui Li; William A Weiss; Milica Medved; Hiroyuki Abe; Gillian M Newstead; Gregory S Karczmar; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-28

9.  Association between mammographic density and age-related lobular involution of the breast.

Authors:  Karthik Ghosh; Lynn C Hartmann; Carol Reynolds; Daniel W Visscher; Kathleen R Brandt; Robert A Vierkant; Christopher G Scott; Derek C Radisky; Thomas A Sellers; V Shane Pankratz; Celine M Vachon
Journal:  J Clin Oncol       Date:  2010-03-29       Impact factor: 44.544

10.  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

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