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Proceedings Paper

Local mammographic density as a predictor of breast cancer
Author(s): Mayu Otsuka; Elaine F. Harkness; Xin Chen; Emmanouil Moschidis; Megan Bydder; Soujanya Gadde; Yit Y. Lim; Anthony J. Maxwell; Gareth D. Evans; Anthony Howell; Paula Stavrinos; Mary Wilson; Susan M. Astley
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Paper Abstract

High overall mammographic density is associated with both an increased risk of developing breast cancer and the risk of cancer being masked. We compared local density at cancer sites in diagnostic images with corresponding previous screening mammograms (priors), and matched controls. VolparaTM density maps were obtained for 54 mammograms showing unilateral breast cancer and their priors which had been previously read as normal. These were each matched to 3 controls on age, menopausal status, hormone replacement therapy usage, body mass index and year of prior. Local percent density was computed in 15mm square regions at lesion sites and similar locations in the corresponding images. Conditional logistic regression was used to predict case-control status. In diagnostic and prior images, local density was increased at the lesion site compared with the opposite breast (medians 21.58%, 9.18%, p<0.001 diagnostic; 18.82%, 9.45%, p <0.001 prior). Women in the highest tertile of local density in priors were more likely to develop cancer than those in the lowest tertile (OR 42.09, 95% CI 5.37-329.94). Those in the highest tertile of VolparaTM gland volume were also more likely to develop cancer (OR 2.89, 95% CI 1.30-6.42). Local density is increased where cancer will develop compared with corresponding regions in the opposite breast and matched controls, and its measurement could enhance computer-aided mammography.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941417 (20 March 2015); doi: 10.1117/12.2082691
Show Author Affiliations
Mayu Otsuka, Univ. of Manchester Medical School (United Kingdom)
Elaine F. Harkness, Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Xin Chen, Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Emmanouil Moschidis, Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Megan Bydder, Univ. Hospital of South Manchester (United Kingdom)
Soujanya Gadde, Univ. Hospital of South Manchester (United Kingdom)
Yit Y. Lim, Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Anthony J. Maxwell, Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)
Gareth D. Evans, Univ. Hospital of South Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Manchester Academic Health Science Ctr., The Univ. of Manchester (United Kingdom)
Anthony Howell, Univ. Hospital of South Manchester (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)
Univ. of Manchester (United Kingdom)
Paula Stavrinos, Univ. Hospital of South Manchester (United Kingdom)
Mary Wilson, Univ. Hospital of South Manchester (United Kingdom)
Susan M. Astley, Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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