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Grant Details

Grant Number: 5R21CA170955-02 Interpret this number
Primary Investigator: Su, Min-Ying
Organization: University Of California-Irvine
Project Title: Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk Prediction
Fiscal Year: 2014
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DESCRIPTION (provided by applicant): Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk Prediction The role of breast density as a strong risk predictor for development of breast cancer has been established by many studies. The Breast Cancer Prevention Collaborative Group has recommended that quantitative breast density should be incorporated into the cancer risk prediction model, but how to reliably measure quantitative density parameters is still an active research area. In addition to the amount of density, the morphological distribution pattern of dense tissue may also play a role in risk prediction, which can only be analyzed on 3-dimensional images. In this R21 application we will evaluate the role of MRI-based density parameters, including the volume and the morphology of the fibroglandular tissue, and build a risk prediction model using a case-control study design. Three aims are proposed. Aim-1 will develop a fully automated segmentation software to segment the breast and the fibroglandular tissue. This software will be made available for sharing, and it will provide a very useful tool for researchers in the breast densitometry research field to analyze large datasets. Aim-2 will develop a risk prediction model based on the MRI-analyzed fibroglandular tissue volume and the morphological distribution pattern, in combination with six basic risk factors (age, hormonal use, family history, prior benign disease, weight, number of live birth) to differentiate between patients who were found to have cancer in screening MRI (cases) vs. matching controls. We have access to a large screening MRI database for retrospective analysis. It is estimated that 220 cancer cases will be available, and by using a 1:5 ratio we will select 1,100 matching controls for analysis. Then Aim-3 will evaluate how the fibroglandular tissue volume and morphological index may be used to improve the risk prediction accuracy, by comparing to the risks estimated by using existing standard models. The history sheet that each subject filled out will be used to calculate the risk scores by using Gail, Claus, BRCAPRO and Tyrer-Cuzick models. The ability of these existing models in differentiating between the cancer cases and controls will be compared to that analyzed using the MRI-density model developed in Aim-2, and the results will allow us to evaluate the added value of breast density in risk prediction. The success of this R21 will build a great foundation for a subsequent longitudinal study, using a prospective screening database that is being collected now within the Athena Breast Health Network formed by five University of California campuses.

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Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.
Authors: Zhang Y. , Chen J.H. , Chang K.T. , Park V.Y. , Kim M.J. , Chan S. , Chang P. , Chow D. , Luk A. , Kwong T. , et al. .
Source: Academic radiology, 2019 11; 26(11), p. 1526-1535.
EPub date: 2019-01-31.
PMID: 30713130
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Source: Physics in medicine and biology, 2019-01-22; 64(3), p. 035007.
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Breast density quantification using structured-light-based diffuse optical tomography simulations.
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Authors: Chan S. , Chen J.H. , Li S. , Chang R. , Yeh D.C. , Chang R.F. , Yeh L.R. , Kwong J. , Su M.Y. .
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Imaging Breast Density: Established and Emerging Modalities.
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Template-based automatic breast segmentation on MRI by excluding the chest region.
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Effect of taxane-based neoadjuvant chemotherapy on fibroglandular tissue volume and percent breast density in the contralateral normal breast evaluated by 3T MR.
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