Literature DB >> 9456242

The detection of changes in mammographic densities.

G Ursin1, M A Astrahan, M Salane, Y R Parisky, J G Pearce, J R Daniels, M C Pike, D V Spicer.   

Abstract

We previously reported reductions in mammographic densities in women participating in a trial of a gonadotropin-releasing hormone agonist (GnRHA)-based regimen for breast cancer prevention. In our previous report, we compared (by simultaneous evaluation) three basic elements of mammographic densities. The purpose of the present study was to evaluate whether a standard (expert) method of measuring mammographic densities would detect such changes in densities and whether a novel nonexpert computer-based threshold method could do so. Mammograms were obtained from 19 women at baseline and 12 months after randomization to the GnRHA-based regimen. The extent of mammographic densities was determined by: (a) a standard expert outlining method developed by Wolfe and his colleagues (Am. J. Roentgenol., 148: 1087-1092, 1987); and (b) a new computer-based threshold method of determining densities. The results from both the expert outlining method and the computer-based threshold method were highly consistent with the results of our original (simultaneous evaluation) method. All three methods yielded statistically significant reductions in densities from baseline to the 12-month follow-up mammogram in women on the contraceptive regimen. The difference between the treated and the control group was statistically significant with the expert outlining method and was of borderline statistical significance with the computer-based threshold method. The computer-based results correlated highly (r > 0.85) with the results from the expert outlining method. Both the standard expert outlining method and the computer-based threshold method detected the reductions we had previously noted in mammographic densities induced by the GnRHA-based regimen.

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Year:  1998        PMID: 9456242

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


  49 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.  Mammographic density and risk of breast cancer by adiposity: an analysis of four case-control studies.

Authors:  Shannon M Conroy; Christy G Woolcott; Karin R Koga; Celia Byrne; Chisato Nagata; Giske Ursin; Celine M Vachon; Martin J Yaffe; Ian Pagano; Gertraud Maskarinec
Journal:  Int J Cancer       Date:  2011-09-17       Impact factor: 7.396

3.  Genetic variation in peroxisome proliferator-activated receptor gamma, soy, and mammographic density in Singapore Chinese women.

Authors:  Eunjung Lee; Chris Hsu; David Van den Berg; Giske Ursin; Woon-Puay Koh; Jian-Min Yuan; Daniel O Stram; Mimi C Yu; Anna H Wu
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2012-02-01       Impact factor: 4.254

4.  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

5.  A Longitudinal Study of the Association between Mammographic Density and Gene Expression in Normal Breast Tissue.

Authors:  Helga Bergholtz; Tonje Gulbrandsen Lien; Giske Ursin; Marit Muri Holmen; Åslaug Helland; Therese Sørlie; Vilde Drageset Haakensen
Journal:  J Mammary Gland Biol Neoplasia       Date:  2019-01-06       Impact factor: 2.673

6.  Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer.

Authors:  C Rauh; C C Hack; L Häberle; A Hein; A Engel; M G Schrauder; P A Fasching; S M Jud; A B Ekici; C R Loehberg; M Meier-Meitinger; S Ozan; R Schulz-Wendtland; M Uder; A Hartmann; D L Wachter; M W Beckmann; K Heusinger
Journal:  Geburtshilfe Frauenheilkd       Date:  2012-08       Impact factor: 2.915

7.  Mammographic Density and Prediction of Nodal Status in Breast Cancer Patients.

Authors:  C C Hack; L Häberle; K Geisler; R Schulz-Wendtland; A Hartmann; P A Fasching; M Uder; D L Wachter; S M Jud; C R Loehberg; M P Lux; C Rauh; M W Beckmann; K Heusinger
Journal:  Geburtshilfe Frauenheilkd       Date:  2013-02       Impact factor: 2.915

8.  A Randomized Controlled Trial of Green Tea Extract Supplementation and Mammographic Density in Postmenopausal Women at Increased Risk of Breast Cancer.

Authors:  Hamed Samavat; Giske Ursin; Tim H Emory; Eunjung Lee; Renwei Wang; Carolyn J Torkelson; Allison M Dostal; Karen Swenson; Chap T Le; Chung S Yang; Mimi C Yu; Douglas Yee; Anna H Wu; Jian-Min Yuan; Mindy S Kurzer
Journal:  Cancer Prev Res (Phila)       Date:  2017-09-13

9.  Genotypes and haplotypes in the insulin-like growth factors, their receptors and binding proteins in relation to plasma metabolic levels and mammographic density.

Authors:  Margarethe Biong; Inger T Gram; Ilene Brill; Fredrik Johansen; Hiroko K Solvang; Grethe I G Alnaes; Toril Fagerheim; Yngve Bremnes; Stephen J Chanock; Laurie Burdett; Meredith Yeager; Giske Ursin; Vessela N Kristensen
Journal:  BMC Med Genomics       Date:  2010-03-19       Impact factor: 3.063

10.  Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density.

Authors:  Vilde D Haakensen; Margarethe Biong; Ole Christian Lingjærde; Marit Muri Holmen; Jan Ole Frantzen; Ying Chen; Dina Navjord; Linda Romundstad; Torben Lüders; Ida K Bukholm; Hiroko K Solvang; Vessela N Kristensen; Giske Ursin; Anne-Lise Børresen-Dale; Aslaug Helland
Journal:  Breast Cancer Res       Date:  2010-08-27       Impact factor: 6.466

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