Literature DB >> 34161135

Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.

John Heine1, Erin Fowler1, Christopher G Scott2, Matthew R Jensen2, John Shepherd3, Carrie B Hruska4, Stacey J Winham2, Kathleen R Brandt4, Fang F Wu2, Aaron D Norman2, Vernon S Pankratz5, Diana L Miglioretti6,7, Karla Kerlikowske8, Celine M Vachon9.   

Abstract

OBJECTIVE. Our previous work showed that variation measures, which represent breast architecture derived from mammograms, were significantly associated with breast cancer. For replication purposes, we examined the association of three variation measures (variation [V], which is measured in the image domain, and P1 and p1 [a normalized version of P1], which are derived from restricted regions in the Fourier domain) with breast cancer risk in an independent population. We also compared these measures to volumetric density measures (volumetric percent density [VPD] and dense volume [DV]) from a commercial product. MATERIALS AND METHODS. We examined 514 patients with breast cancer and 1377 control patients from a screening practice who were matched for age, date of examination, mammography unit, facility, and state of residence. Spearman rank-order correlation was used to evaluate the monotonic association between measures. Breast cancer associations were estimated using conditional logistic regression, after adjustment for age and body mass index. Odds ratios were calculated per SD increment in mammographic measure. RESULTS. These variation measures were strongly correlated with VPD (correlation, 0.68-0.80) but not with DV (correlation, 0.31-0.48). Similar to previous findings, all variation measures were significantly associated with breast cancer (odds ratio per SD: 1.30 [95% CI, 1.16-1.46] for V, 1.55 [95% CI, 1.35-1.77] for P1, and 1.51 [95% CI, 1.33-1.72] for p1). Associations of volumetric density measures with breast cancer were similar (odds ratio per SD: 1.54 [95% CI, 1.33-1.78] for VPD and 1.34 [95% CI, 1.20-1.50] for DV). When DV was included with each variation measure in the same model, all measures retained significance. CONCLUSION. Variation measures were significantly associated with breast cancer risk (comparable to the volumetric density measures) but were independent of the DV.

Entities:  

Keywords:  breast cancer; risk prediction; variation measures; volumetric breast density

Mesh:

Year:  2021        PMID: 34161135      PMCID: PMC9009534          DOI: 10.2214/AJR.20.22794

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   6.582


  27 in total

1.  Spectral analysis of full field digital mammography data.

Authors:  John J Heine; Robert P Velthuizen
Journal:  Med Phys       Date:  2002-05       Impact factor: 4.071

Review 2.  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

3.  Impact of adding breast density to breast cancer risk models: A systematic review.

Authors:  Bolette Mikela Vilmun; Ilse Vejborg; Elsebeth Lynge; Martin Lillholm; Mads Nielsen; Michael Bachmann Nielsen; Jonathan Frederik Carlsen
Journal:  Eur J Radiol       Date:  2020-04-19       Impact factor: 3.528

4.  Breast cancer screening in average-risk women: towards personalized screening.

Authors:  Almir Gv Bitencourt; Carolina Rossi Saccarelli; Christiane Kuhl; Elizabeth A Morris
Journal:  Br J Radiol       Date:  2019-09-23       Impact factor: 3.039

5.  Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.

Authors:  Valerie A McCormack; Isabel dos Santos Silva
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2006-06       Impact factor: 4.254

6.  Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment.

Authors:  Despina Kontos; Stacey J Winham; Andrew Oustimov; Lauren Pantalone; Meng-Kang Hsieh; Aimilia Gastounioti; Dana H Whaley; Carrie B Hruska; Karla Kerlikowske; Kathleen Brandt; Emily F Conant; Celine M Vachon
Journal:  Radiology       Date:  2018-10-30       Impact factor: 11.105

7.  Texture features from mammographic images and risk of breast cancer.

Authors:  Armando Manduca; Michael J Carston; John J Heine; Christopher G Scott; V Shane Pankratz; Kathy R Brandt; Thomas A Sellers; Celine M Vachon; James R Cerhan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-03       Impact factor: 4.254

8.  Mammographic texture resemblance generalizes as an independent risk factor for breast cancer.

Authors:  Mads Nielsen; Celine M Vachon; Christopher G Scott; Konstantin Chernoff; Gopal Karemore; Nico Karssemeijer; Martin Lillholm; Morten A Karsdal
Journal:  Breast Cancer Res       Date:  2014-04-08       Impact factor: 6.466

9.  A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies.

Authors:  Chao Wang; Adam R Brentnall; Jack Cuzick; Elaine F Harkness; D Gareth Evans; Susan Astley
Journal:  Breast Cancer Res       Date:  2017-10-18       Impact factor: 6.466

10.  The combined effect of mammographic texture and density on breast cancer risk: a cohort study.

Authors:  Johanna O P Wanders; Carla H van Gils; Nico Karssemeijer; Katharina Holland; Michiel Kallenberg; Petra H M Peeters; Mads Nielsen; Martin Lillholm
Journal:  Breast Cancer Res       Date:  2018-05-02       Impact factor: 6.466

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  1 in total

1.  Associations of Oral Contraceptives with Mammographic Breast Density in Premenopausal Women.

Authors:  Lusine Yaghjyan; Carmen Smotherman; John Heine; Graham A Colditz; Bernard Rosner; Rulla M Tamimi
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2021-12-03       Impact factor: 4.090

  1 in total

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