Grant Number:	1R21CA170955-01A1 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:	2013

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.




Fast ICCD-based temperature modulated fluorescence tomography.
Authors: Nouizi F. , Kwong T.C. , Turong B. , Nikkhah D. , Sampathkumaran U. , Gulsen G. .
Source: Applied Optics, 2023-10-01 00:00:00.0; 62(28), p. 7420-7430.
PMID: 37855510
Related Citations

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.
Authors: Zhang Y. , Chan S. , Chen J.H. , Chang K.T. , Lin C.Y. , Pan H.B. , Lin W.C. , Kwong T. , Parajuli R. , Mehta R.S. , et al. .
Source: Journal Of Digital Imaging, 2021-07-09 00:00:00.0; , .
EPub date: 2021-07-09 00:00:00.0.
PMID: 34244879
Related Citations

Association of mammographic density measures and breast cancer "intrinsic" molecular subtypes.
Authors: Kleinstern G. , Scott C.G. , Tamimi R.M. , Jensen M.R. , Pankratz V.S. , Bertrand K.A. , Norman A.D. , Visscher D.W. , Couch F.J. , Brandt K. , et al. .
Source: Breast Cancer Research And Treatment, 2021-01-04 00:00:00.0; , .
EPub date: 2021-01-04 00:00:00.0.
PMID: 33392844
Related Citations

Multi-Wavelength Photo-Magnetic Imaging System for Photothermal Therapy Guidance.
Authors: Algarawi M. , Erkol H. , Luk A. , Ha S. , Burcin Unlu M. , Gulsen G. , Nouizi F. .
Source: Lasers In Surgery And Medicine, 2020-11-10 00:00:00.0; , .
EPub date: 2020-11-10 00:00:00.0.
PMID: 33169857
Related Citations

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, 2019-01-31 00:00:00.0; , .
EPub date: 2019-01-31 00:00:00.0.
PMID: 30713130
Related Citations

A thermo-sensitive fluorescent agent based method for excitation light leakage rejection for fluorescence molecular tomography.
Authors: Nouizi F. , Kwong T.C. , Ruiz J. , Cho J. , Chan Y.W. , Ikemura K. , Erkol H. , Sampathkumaran U. , Gulsen G. .
Source: Physics In Medicine And Biology, 2019-01-22 00:00:00.0; 64(3), p. 035007.
EPub date: 2019-01-22 00:00:00.0.
PMID: 30561380
Related Citations

Quantitative analysis of peri-tumor fat in different molecular subtypes of breast cancer.
Authors: Chen J.H. , Zhang Y. , Chan S. , Chang R.F. , Su M.Y. .
Source: Magnetic Resonance Imaging, 2018 11; 53, p. 34-39.
EPub date: 2018-06-30 00:00:00.0.
PMID: 29969646
Related Citations

Breast density quantification using structured-light-based diffuse optical tomography simulations.
Authors: Ruiz J. , Nouizi F. , Cho J. , Zheng J. , Li Y. , Chen J.H. , Su M.Y. , Gulsen G. .
Source: Applied Optics, 2017-09-01 00:00:00.0; 56(25), p. 7146-7157.
PMID: 29047975
Related Citations

Evaluation Of The Association Between Quantitative Mammographic Density And Breast Cancer Occurred In Different Quadrants
Authors: Chan S. , Chen J.H. , Li S. , Chang R. , Yeh D.C. , Chang R.F. , Yeh L.R. , Kwong J. , Su M.Y. .
Source: Bmc Cancer, 2017-04-17 00:00:00.0; 17(1), p. 274.
PMID: 28415974
Related Citations

3d Mri For Quantitative Analysis Of Quadrant Percent Breast Density: Correlation With Quadrant Location Of Breast Cancer
Authors: Chen J.H. , Liao F. , Zhang Y. , Li Y. , Chang C.J. , Chou C.P. , Yang T.L. , Su M.Y. .
Source: Academic Radiology, 2017-01-25 00:00:00.0; , .
PMID: 28131498
Related Citations

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening.
Authors: Chen J.H. , Chan S. , Lu N.H. , Li Y. , Tsai Y.C. , Huang P.Y. , Chang C.J. , Su M.Y. .
Source: Academic Radiology, 2016 Sep; 23(9), p. 1154-61.
PMID: 27283069
Related Citations

Imaging Breast Density: Established and Emerging Modalities.
Authors: Chen J.H. , Gulsen G. , Su M.Y. .
Source: Translational Oncology, 2015 Dec; 8(6), p. 435-45.
PMID: 26692524
Related Citations

Investigation of factors affecting hypothermic pelvic tissue cooling using bio-heat simulation based on MRI-segmented anatomic models.
Authors: Lin Y. , Lin W.C. , Fwu P.T. , Shih T.C. , Yeh L.R. , Su M.Y. , Chen J.H. .
Source: Computer Methods And Programs In Biomedicine, 2015 Oct; 122(1), p. 76-88.
PMID: 26198131
Related Citations

Quantification of Regional Breast Density in Four Quadrants Using 3D MRI-A Pilot Study.
Authors: Fwu P.T. , Chen J.H. , Li Y. , Chan S. , Su M.Y. .
Source: Translational Oncology, 2015 Aug; 8(4), p. 250-7.
PMID: 26310370
Related Citations

Background Parenchymal Enhancement of the Contralateral Normal Breast: Association with Tumor Response in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy.
Authors: Chen J.H. , Yu H.J. , Hsu C. , Mehta R.S. , Carpenter P.M. , Su M.Y. .
Source: Translational Oncology, 2015 Jun; 8(3), p. 204-9.
PMID: 26055178
Related Citations

Impact of positional difference on the measurement of breast density using MRI.
Authors: Chen J.H. , Chan S. , Tang Y.T. , Hon J.S. , Tseng P.C. , Cheriyan A.T. , Shah N.R. , Yeh D.C. , Lee S.K. , Chen W.P. , et al. .
Source: Medical Physics, 2015 May; 42(5), p. 2268-75.
PMID: 25979021
Related Citations

Effect of taxane-based neoadjuvant chemotherapy on fibroglandular tissue volume and percent breast density in the contralateral normal breast evaluated by 3T MR.
Authors: Chen J.H. , Pan W.F. , Kao J. , Lu J. , Chen L.K. , Kuo C.C. , Chang C.K. , Chen W.P. , McLaren C.E. , Bahri S. , et al. .
Source: Nmr In Biomedicine, 2013 Dec; 26(12), p. 1705-13.
PMID: 23940080
Related Citations

Template-based automatic breast segmentation on MRI by excluding the chest region.
Authors: Lin M. , Chen J.H. , Wang X. , Chan S. , Chen S. , Su M.Y. .
Source: Medical Physics, 2013 Dec; 40(12), p. 122301.
PMID: 24320532
Related Citations

Background parenchymal enhancement in the contralateral normal breast of patients undergoing neoadjuvant chemotherapy measured by DCE-MRI.
Authors: Chen J.H. , Yu H. , Lin M. , Mehta R.S. , Su M.Y. .
Source: Magnetic Resonance Imaging, 2013 Nov; 31(9), p. 1465-71.
PMID: 23992630
Related Citations