Literature DB >> 6587146

Mammographic patterns and breast cancer risk: methodologic standards and contradictory results.

N F Boyd, B O'Sullivan, E Fishell, I Simor, G Cooke.   

Abstract

The claim that classification of the pattern of the breast parenchyma upon mammography can distinguish groups at different risks for breast cancer has been examined by 17 other studies published in the English language literature with contradictory results; this controversy was assessed by us in examination of these studies for their adherence to commonly accepted methodologic standards for the investigation of causal relationships. The nine standards for the examination of the studies included a description of the way the study population had been assembled and followed up and descriptions of the methods of mammographic pattern classification and breast cancer risk analysis. A strong association was found between the standards adopted by a study and the obtained results. Among nine cohort and case-control studies that found a statistically significant association between mammographic pattern and breast cancer risk, all met at least four standards, whereas only two of six "negative" cohort or case-control studies met as many as four standards. Among prevalence surveys, the association between methods and results was less striking, but several negative prevalence surveys were associated with "positive" cohort studies employing the same group of patients. These results indicate that methodologic differences among studies contribute substantially to the controversy surrounding this subject. Studies that follow the usual scientific methods employed in the epidemiologic investigation of risk generally have confirmed an association between mammographic pattern and breast cancer risk.

Entities:  

Mesh:

Year:  1984        PMID: 6587146

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  6 in total

1.  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

2.  Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience.

Authors:  Yuji Ikedo; Takako Morita; Daisuke Fukuoka; Takeshi Hara; Gobert Lee; Hiroshi Fujita; Etsuo Takada; Tokiko Endo
Journal:  Int J Comput Assist Radiol Surg       Date:  2009-03-14       Impact factor: 2.924

3.  Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.

Authors:  Hui Li; Maryellen L Giger; Chang Sun; Umnouy Ponsukcharoen; Dezheng Huo; Li Lan; Olufunmilayo I Olopade; Andrew R Jamieson; Jeremy Bancroft Brown; Anna Di Rienzo
Journal:  Med Phys       Date:  2014-03       Impact factor: 4.071

4.  Cellular proliferative activity of mammographic normal dense and fatty tissue determined by DNA S phase percentage.

Authors:  P C Stomper; R B Penetrante; S B Edge; M A Arredondo; L E Blumenson; C C Stewart
Journal:  Breast Cancer Res Treat       Date:  1996       Impact factor: 4.872

5.  Age as a confounding factor in the association of mammographic dysplasia and estrogen receptor concentration in breast cancer.

Authors:  N F Boyd; E Fishell; K Tonkin; B G Mobbs
Journal:  Breast Cancer Res Treat       Date:  1987-10       Impact factor: 4.872

6.  Breast cancer screening, with particular reference to the concept of 'high risk' groups.

Authors:  F De Waard; H J Collette; J J Rombach; C Collette
Journal:  Breast Cancer Res Treat       Date:  1988-05       Impact factor: 4.872

  6 in total

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