Literature DB >> 2070577

The mammographic parenchymal patterns of nulliparous women and women with a family history of breast cancer.

Z Kaufman1, W I Garstin, R Hayes, M J Michell, M Baum.   

Abstract

One-hundred-and-thirteen mammograms of nulliparous women and 44 mammograms of women with a family history of breast cancer were graded according to Wolfe's parenchymal pattern classification. These were compared to 437 mammograms of women without these risk factors. Mammograms were read by two independent observers in order to evaluate inter- and intra-observer variation. The interobserver variation was reduced from 17% to 5% by combining high risk patterns (P2 and DY) and low risk patterns (N1 and P1). A significantly higher proportion of high risk patterns was found in nulliparous women compared to parous women (P less than 0.01). The proportion of high risk patterns decreased significantly with the number of children (P less than 0.01). Women with a family history of breast cancer had almost the same parenchymal patterns as women without a family history. In conclusion, while nulliparity and family history are recognized risk factors for developing breast cancer, only nulliparity would appear to influence the mammographic parenchymal pattern. This probably reflects the different mechanism by which the two factors affect breast tissue.

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Year:  1991        PMID: 2070577     DOI: 10.1016/s0009-9260(05)80565-3

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  7 in total

Review 1.  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

2.  Noninvasive functional optical spectroscopy of human breast tissue.

Authors:  N Shah; A Cerussi; C Eker; J Espinoza; J Butler; J Fishkin; R Hornung; B Tromberg
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-03       Impact factor: 11.205

3.  Mammographic density does not differ between unaffected BRCA1/2 mutation carriers and women at low-to-average risk of breast cancer.

Authors:  Gretchen L Gierach; Jennifer T Loud; Catherine K Chow; Sheila A Prindiville; Jennifer Eng-Wong; Peter W Soballe; Claudia Giambartolomei; Phuong L Mai; Claudia E Galbo; Kathryn Nichols; Kathleen A Calzone; Celine Vachon; Mitchell H Gail; Mark H Greene
Journal:  Breast Cancer Res Treat       Date:  2010-02-04       Impact factor: 4.872

Review 4.  Can genes for mammographic density inform cancer aetiology?

Authors:  Linda E Kelemen; Thomas A Sellers; Celine M Vachon
Journal:  Nat Rev Cancer       Date:  2008-09-05       Impact factor: 60.716

5.  Predictors of mammographic density: insights gained from a novel regression analysis of a twin study.

Authors:  Gillian S Dite; Lyle C Gurrin; Graham B Byrnes; Jennifer Stone; Anoma Gunasekara; Margaret R E McCredie; Dallas R English; Graham G Giles; Jennifer Cawson; Robert A Hegele; Anna M Chiarelli; Martin J Yaffe; Norman F Boyd; John L Hopper
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-12       Impact factor: 4.254

6.  Pregnancy postponement and childlessness leads to chronic hypervascularity of the breasts and cancer risk.

Authors:  H W Simpson; C S McArdle; W D George; K Griffiths; A Turkes; A W Pauson
Journal:  Br J Cancer       Date:  2002-11-18       Impact factor: 7.640

7.  RANKL and OPG and their influence on breast volume changes during pregnancy in healthy women.

Authors:  Marius Wunderle; Matthias Ruebner; Lothar Häberle; Eva Schwenke; Carolin C Hack; Christian M Bayer; Martin C Koch; Judith Schwitulla; Ruediger Schulz-Wendtland; Ivona Kozieradzki; Michael P Lux; Matthias W Beckmann; Sebastian M Jud; Josef M Penninger; Michael O Schneider; Peter A Fasching
Journal:  Sci Rep       Date:  2020-03-20       Impact factor: 4.379

  7 in total

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