Local mammographic density as a predictor of breast cancer

Mayu Otsuka; Elaine Harkness; Xin Chen; Emmanouil Moschidis; Megan Bydder; Soujanya Gadde; Yit Lim; Anthony J Maxwell; D Gareth Evans; Anthony Howell; Paula Stavrinos; Mary Wilson; Susan Astley

doi:10.1117/12.2082691

Local mammographic density as a predictor of breast cancer

Mayu Otsuka, Elaine Harkness, Xin Chen, Emmanouil Moschidis, Megan Bydder, Soujanya Gadde, Yit Lim, Anthony J Maxwell, D Gareth Evans, Anthony Howell, Paula Stavrinos, Mary Wilson, Susan Astley

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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

Original language	English
Title of host publication	Medical Imaging 2015: Computer-Aided Diagnosis
Editors	Lubomir M Hadjiiski, Georgia D Tourassi
Place of Publication	USA
Publisher	SPIE
Number of pages	8
Volume	9414
DOIs	https://doi.org/10.1117/12.2082691
Publication status	Published - 20 Mar 2015
Event	SPIE Medical Imaging - Orlando, Florida Duration: 15 Feb 2015 → 20 Feb 2015

Publication series

Name	Medical Imaging 2015: Computer-Aided Diagnosis

Conference

Conference	SPIE Medical Imaging
City	Orlando, Florida
Period	15/02/15 → 20/02/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1117/12.2082691

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Otsuka, M., Harkness, E., Chen, X., Moschidis, E., Bydder, M., Gadde, S., Lim, Y., Maxwell, A. J., Evans, D. G., Howell, A., Stavrinos, P., Wilson, M., & Astley, S. (2015). Local mammographic density as a predictor of breast cancer. In L. M. Hadjiiski, & G. D. Tourassi (Eds.), Medical Imaging 2015: Computer-Aided Diagnosis (Vol. 9414). (Medical Imaging 2015: Computer-Aided Diagnosis). SPIE. https://doi.org/10.1117/12.2082691

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title = "Local mammographic density as a predictor of breast cancer",

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",

author = "Mayu Otsuka and Elaine Harkness and Xin Chen and Emmanouil Moschidis and Megan Bydder and Soujanya Gadde and Yit Lim and Maxwell, {Anthony J} and Evans, {D Gareth} and Anthony Howell and Paula Stavrinos and Mary Wilson and Susan Astley",

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doi = "10.1117/12.2082691",

language = "English",

volume = "9414",

series = "Medical Imaging 2015: Computer-Aided Diagnosis",

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editor = "Hadjiiski, {Lubomir M} and Tourassi, {Georgia D}",

booktitle = "Medical Imaging 2015: Computer-Aided Diagnosis",

address = "United States",

note = "SPIE Medical Imaging ; Conference date: 15-02-2015 Through 20-02-2015",

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Otsuka, M, Harkness, E, Chen, X, Moschidis, E, Bydder, M, Gadde, S, Lim, Y, Maxwell, AJ, Evans, DG , Howell, A, Stavrinos, P, Wilson, M & Astley, S 2015, Local mammographic density as a predictor of breast cancer. in LM Hadjiiski & GD Tourassi (eds), Medical Imaging 2015: Computer-Aided Diagnosis. vol. 9414, Medical Imaging 2015: Computer-Aided Diagnosis, SPIE, USA, SPIE Medical Imaging, Orlando, Florida, 15/02/15. https://doi.org/10.1117/12.2082691

Local mammographic density as a predictor of breast cancer. / Otsuka, Mayu; Harkness, Elaine; Chen, Xin et al.
Medical Imaging 2015: Computer-Aided Diagnosis. ed. / Lubomir M Hadjiiski; Georgia D Tourassi. Vol. 9414 USA: SPIE, 2015. (Medical Imaging 2015: Computer-Aided Diagnosis).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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T1 - Local mammographic density as a predictor of breast cancer

AU - Otsuka, Mayu

AU - Harkness, Elaine

AU - Chen, Xin

AU - Moschidis, Emmanouil

AU - Bydder, Megan

AU - Gadde, Soujanya

AU - Lim, Yit

AU - Maxwell, Anthony J

AU - Evans, D Gareth

AU - Howell, Anthony

AU - Stavrinos, Paula

AU - Wilson, Mary

AU - Astley, Susan

PY - 2015/3/20

Y1 - 2015/3/20

N2 - 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

AB - 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

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DO - 10.1117/12.2082691

M3 - Conference contribution

VL - 9414

T3 - Medical Imaging 2015: Computer-Aided Diagnosis

BT - Medical Imaging 2015: Computer-Aided Diagnosis

A2 - Hadjiiski, Lubomir M

A2 - Tourassi, Georgia D

PB - SPIE

CY - USA

T2 - SPIE Medical Imaging

Y2 - 15 February 2015 through 20 February 2015

ER -

Local mammographic density as a predictor of breast cancer

Abstract

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Conference

UN SDGs

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