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Grant Details

Grant Number: 4R01CA175080-04 Interpret this number
Primary Investigator: Tamimi, Rulla
Organization: Brigham And Women'S Hospital
Project Title: Mammographic Density and Texture Features in Relation to Breast Cancer Risk
Fiscal Year: 2016
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DESCRIPTION (provided by applicant): Mammographic density is one of the strongest risk factors for breast cancer. Despite this, the current measurement of breast density in the clinical setting (i.e., BI-RADS) is relatively subjective and utilization of this measure is minimal. The motivation for assessing BI-RADS is to alert radiologists because sensitivity of mammography is lower in women with dense breasts; the intention was not for risk assessment The most widely accepted research measure of mammographic density utilizes an operator-assisted technique based on the percentage of mammographic density (PMD). While these measures are well accepted to predict risk of breast cancer, they still require a reader which is both time intensive and can lead to measurement error. The lack of automation is an impediment to clinical utilization. Further, there is additional information in mammographic images that are not captured by current PMD measurements. This heterogeneity in patterns of breast density is often referred to as 'texture'. We propose to evaluate the following three complementary automated measures of mammographic breast features in relation to subsequent breast cancer risk (Aim 1): (1) an automated measure of percent mammographic density, (2) individual texture measures and (3) a new measure, called V that captures a wide-band of textural information including spatial variation in a single measure. Each of these measures has demonstrated to predict breast cancer risk in at least one population. The three proposed measures developed by co-investigators are objective, automated techniques that are applicable to digitized film mammograms as well as digital mammograms. In Aim 2, we will evaluate breast cancer risk factor in relation to the texture features and will determine the extent to which breast cancer ris factors are mediated through mammographic density (i.e., automated PMD) and textural features (i.e., individual texture measures and V). Very little is known about the biology underlying mammographic texture features. We will determine if texture features on a mammogram are related to specific morphologic changes in the normal breast that are associated with breast cancer risk by examining these features on women whose benign breast disease specimens have undergone centralized pathology review (expected n=1304) (Aim 3). This proposal builds on a wealth of existing resources within the Nurses' Health Studies. As part of this study, we expect to have digitized screening film mammograms from 3480 breast cancer cases and 6974 controls. Because PMD is one of the strongest risk factors for breast cancer, a proposal to mandate the reporting of a relatively subjective non-automated measure of PMD, BI-RADS, to women undergoing screening is currently under Congressional review. The major goals of this proposal are to determine if automated measures of PMD and texture are associated with breast cancer, and to better understand the mechanisms by which they influence risk. Having automated and validated measures that strongly predict breast cancer risk has important implications for breast cancer risk prediction, screening, and chemoprevention.

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Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density.
Authors: Yaghjyan L. , Colditz G. , Eliassen H. , Rosner B. , Gasparova A. , Tamimi R.M. .
Source: Cancer Causes & Control : Ccc, 2018-06-25 00:00:00.0; , .
EPub date: 2018-06-25 00:00:00.0.
PMID: 29938357
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Associations of coffee consumption and caffeine intake with mammographic breast density.
Authors: Yaghjyan L. , Colditz G. , Rosner B. , Gasparova A. , Tamimi R.M. .
Source: Breast Cancer Research And Treatment, 2018-01-17 00:00:00.0; , .
EPub date: 2018-01-17 00:00:00.0.
PMID: 29340883
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Breast cancer risk prediction: an update to the Rosner-Colditz breast cancer incidence model.
Authors: Rice M.S. , Tworoger S.S. , Hankinson S.E. , Tamimi R.M. , Eliassen A.H. , Willett W.C. , Colditz G. , Rosner B. .
Source: Breast Cancer Research And Treatment, 2017-07-12 00:00:00.0; , .
EPub date: 2017-07-12 00:00:00.0.
PMID: 28702896
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Percent mammographic density prediction: development of a model in the nurses' health studies.
Authors: Rice M.S. , Rosner B.A. , Tamimi R.M. .
Source: Cancer Causes & Control : Ccc, 2017 Jul; 28(7), p. 677-684.
EPub date: 2017-05-06 00:00:00.0.
PMID: 28478536
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Mammographic Texture And Risk Of Breast Cancer By Tumor Type And Estrogen Receptor Status
Authors: Malkov S. , Shepherd J.A. , Scott C.G. , Tamimi R.M. , Ma L. , Bertrand K.A. , Couch F. , Jensen M.R. , Mahmoudzadeh A.P. , Fan B. , et al. .
Source: Breast Cancer Research : Bcr, 2016-12-06 00:00:00.0; 18(1), p. 122.
PMID: 27923387
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Mammographic Density And Breast Cancer Risk: A Mediation Analysis
Authors: Rice M.S. , Bertrand K.A. , VanderWeele T.J. , Rosner B.A. , Liao X. , Adami H.O. , Tamimi R.M. .
Source: Breast Cancer Research : Bcr, 2016-09-21 00:00:00.0; 18(1), p. 94.
PMID: 27654859
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Adolescent fiber intake and mammographic breast density in premenopausal women.
Authors: Yaghjyan L. , Ghita G.L. , Rosner B. , Farvid M. , Bertrand K.A. , Tamimi R.M. .
Source: Breast Cancer Research : Bcr, 2016-08-12 00:00:00.0; 18(1), p. 85.
EPub date: 2016-08-12 00:00:00.0.
PMID: 27520794
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Reproductive factors related to childbearing and mammographic breast density.
Authors: Yaghjyan L. , Colditz G.A. , Rosner B. , Bertrand K.A. , Tamimi R.M. .
Source: Breast Cancer Research And Treatment, 2016 Jul; 158(2), p. 351-9.
EPub date: 2016-06-28 00:00:00.0.
PMID: 27351801
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