Literature DB >> 18843028

Using mammographic density to improve breast cancer screening outcomes.

Anne M Kavanagh1, Graham B Byrnes, Carolyn Nickson, Jennifer N Cawson, Graham G Giles, John L Hopper, Dorota M Gertig, Dallas R English.   

Abstract

It is possible that the performance of mammographic screening would be improved if it is targeted at women at higher risk of breast cancer or who are more likely to have their cancer missed at screening, through more intensive screening or alternative screening modalities. We conducted a case-control study within a population-based Australian mammographic screening program (1,706 invasive breast cancers and 5,637 randomly selected controls). We used logistic regression to examine the effects of breast density, age, and hormone therapy use, all known to influence both breast cancer risk and the sensitivity of mammographic screening, on the risk of small (<or=15 mm) and large (>15 mm) screen-detected and interval breast cancers. The risk of small screen-detected cancers was not associated with density, but the risk of large screen-detected cancers was nearly 3-fold for the second quintile and approximately 4-fold for the four highest density categories (third and fourth quintiles and the two highest deciles) compared with the lowest quintile. The risk of interval cancers increased monotonically across the density categories [highest decile odds ratio (OR), 4.65; 95% confidence interval (95% CI), 2.96-7.31]. The risk of small and large screen-detected cancers, but not interval cancers, increased with age. After adjusting for age and density, hormone therapy use was associated with a moderately elevated risk of interval cancers (OR, 1.43; 95% CI, 1.12-1.81). The effectiveness of the screening program could be improved if density were to be used to identify women most likely to have poor screening outcomes. There would be little additional benefit in targeting screening based on age and hormone therapy use.

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Year:  2008        PMID: 18843028     DOI: 10.1158/1055-9965.EPI-07-2835

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


  10 in total

1.  Automatic classification of mammography reports by BI-RADS breast tissue composition class.

Authors:  Bethany Percha; Houssam Nassif; Jafi Lipson; Elizabeth Burnside; Daniel Rubin
Journal:  J Am Med Inform Assoc       Date:  2012-01-29       Impact factor: 4.497

2.  Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset.

Authors:  Daniela Sacchetto; Lia Morra; Silvano Agliozzo; Daniela Bernardi; Tomas Björklund; Beniamino Brancato; Patrizia Bravetti; Luca A Carbonaro; Loredana Correale; Carmen Fantò; Elisabetta Favettini; Laura Martincich; Luisella Milanesio; Sara Mombelloni; Francesco Monetti; Doralba Morrone; Marco Pellegrini; Barbara Pesce; Antonella Petrillo; Gianni Saguatti; Carmen Stevanin; Rubina M Trimboli; Paola Tuttobene; Marvi Valentini; Vincenzo Marra; Alfonso Frigerio; Alberto Bert; Francesco Sardanelli
Journal:  Eur Radiol       Date:  2015-05-01       Impact factor: 5.315

3.  Digital mammography screening: how many breast cancers are additionally detected by bilateral ultrasound examination during assessment?

Authors:  Stefanie Weigel; Cornelis Biesheuvel; Shoma Berkemeyer; Harald Kugel; Walter Heindel
Journal:  Eur Radiol       Date:  2012-10-07       Impact factor: 5.315

Review 4.  The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening.

Authors:  Paula B Gordon
Journal:  Curr Oncol       Date:  2022-05-17       Impact factor: 3.109

5.  Screen-detected versus interval cancers: Effect of imaging modality and breast density in the Flemish Breast Cancer Screening Programme.

Authors:  Lore Timmermans; Luc Bleyen; Klaus Bacher; Koen Van Herck; Kim Lemmens; Chantal Van Ongeval; Andre Van Steen; Patrick Martens; Isabel De Brabander; Mathieu Goossens; Hubert Thierens
Journal:  Eur Radiol       Date:  2017-03-13       Impact factor: 5.315

6.  Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to the time since the mammogram.

Authors:  Lusine Yaghjyan; Graham A Colditz; Bernard Rosner; Rulla M Tamimi
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-04-19       Impact factor: 4.254

7.  AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes.

Authors:  Carolyn Nickson; Yulia Arzhaeva; Zoe Aitken; Tarek Elgindy; Mitchell Buckley; Min Li; Dallas R English; Anne M Kavanagh
Journal:  Breast Cancer Res       Date:  2013       Impact factor: 6.466

8.  Mammographic features associated with interval breast cancers in screening programs.

Authors:  Norman F Boyd; Ella Huszti; Olga Melnichouk; Lisa J Martin; Greg Hislop; Anna Chiarelli; Martin J Yaffe; Salomon Minkin
Journal:  Breast Cancer Res       Date:  2014-08-26       Impact factor: 6.466

9.  Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.

Authors:  Kavitha Krishnan; Laura Baglietto; Carmel Apicella; Jennifer Stone; Melissa C Southey; Dallas R English; Graham G Giles; John L Hopper
Journal:  Breast Cancer Res       Date:  2016-06-18       Impact factor: 6.466

10.  The TP53 mutation rate differs in breast cancers that arise in women with high or low mammographic density.

Authors:  Kylie L Gorringe; Ian G Campbell; Dane Cheasley; Lisa Devereux; Siobhan Hughes; Carolyn Nickson; Pietro Procopio; Grant Lee; Na Li; Vicki Pridmore; Kenneth Elder; G Bruce Mann; Tanjina Kader; Simone M Rowley; Stephen B Fox; David Byrne; Hugo Saunders; Kenji M Fujihara; Belle Lim
Journal:  NPJ Breast Cancer       Date:  2020-08-07
  10 in total

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