Literature DB >> 4027868

Wolfe mammographic parenchymal patterns. A study of the masking hypothesis of Egan and Mosteller.

J Whitehead, T Carlile, K J Kopecky, D J Thompson, F I Gilbert, A J Present, B A Threatt, P Krook, E Hadaway.   

Abstract

Wolfe defined four different classes of breast parenchymal patterns and claimed that they were associated with different risks for the subsequent development of breast cancer. Egan and Mosteller suggested that these patterns did not constitute a true risk factor, rather the effect was caused by the greater difficulty of detecting breast cancers in the dense (P2, DY) patterns compared with the fatty (N1, P1) patterns. Similarly, Mendell believed that a bias was introduced into Wolfe's work by requiring a negative mammogram before a patient entered the study. This study of 221 prevalent and 706 incident cancers followed for up to 10 years indicates that a masking effect does exist, but that it operates in addition to a difference in risk of breast cancer within the four Wolfe classes. Wolfe's hypothesis is found to be valid.

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Year:  1985        PMID: 4027868     DOI: 10.1002/1097-0142(19850915)56:6<1280::aid-cncr2820560610>3.0.co;2-8

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  13 in total

Review 1.  Breast tissue composition and susceptibility to breast cancer.

Authors:  Norman F Boyd; Lisa J Martin; Michael Bronskill; Martin J Yaffe; Neb Duric; Salomon Minkin
Journal:  J Natl Cancer Inst       Date:  2010-07-08       Impact factor: 13.506

2.  Evaluation of the kinetic properties of background parenchymal enhancement throughout the phases of the menstrual cycle.

Authors:  Alana R Amarosa; Jason McKellop; Ana Paula Klautau Leite; Melanie Moccaldi; Tess V Clendenen; James S Babb; Anne Zeleniuch-Jacquotte; Linda Moy; Sungheon Kim
Journal:  Radiology       Date:  2013-05-08       Impact factor: 11.105

3.  Mammographic breast density and risk of breast cancer: masking bias or causality?

Authors:  C H van Gils; J D Otten; A L Verbeek; J H Hendriks
Journal:  Eur J Epidemiol       Date:  1998-06       Impact factor: 8.082

4.  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

5.  Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study.

Authors:  Jun Wei; Heang-Ping Chan; Yi-Ta Wu; Chuan Zhou; Mark A Helvie; Alexander Tsodikov; Lubomir M Hadjiiski; Berkman Sahiner
Journal:  Radiology       Date:  2011-03-15       Impact factor: 11.105

6.  Impact of the California breast density law on primary care physicians.

Authors:  Kathleen A Khong; Jonathan Hargreaves; Shadi Aminololama-Shakeri; Karen K Lindfors
Journal:  J Am Coll Radiol       Date:  2014-12-24       Impact factor: 5.532

7.  Risk factors for breast cancer by mode of diagnosis: some results from a breast cancer screening study.

Authors:  J Whitehead; J Cooper
Journal:  J Epidemiol Community Health       Date:  1989-06       Impact factor: 3.710

8.  The ACR BI-RADS experience: learning from history.

Authors:  Elizabeth S Burnside; Edward A Sickles; Lawrence W Bassett; Daniel L Rubin; Carol H Lee; Debra M Ikeda; Ellen B Mendelson; Pamela A Wilcox; Priscilla F Butler; Carl J D'Orsi
Journal:  J Am Coll Radiol       Date:  2009-12       Impact factor: 5.532

Review 9.  Beyond mammography: new frontiers in breast cancer screening.

Authors:  Jennifer S Drukteinis; Blaise P Mooney; Chris I Flowers; Robert A Gatenby
Journal:  Am J Med       Date:  2013-04-03       Impact factor: 4.965

10.  Mammographic densities as a criterion for entry to a clinical trial of breast cancer prevention.

Authors:  N F Boyd; E Fishell; R Jong; J C MacDonald; R K Sparrow; I S Simor; V Kriukov; G Lockwood; D Tritchler
Journal:  Br J Cancer       Date:  1995-08       Impact factor: 7.640

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