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.
According to the U. S. Preventive Services Task Force (USPSTF) guideline, routine screening is no longer recommended for women 40-49 years old, and the decision should be an individual one. However, the current risk-prediction models are not sufficient to accurately predict the cancer risk for each individual woman. The proposed study will build a new risk prediction model based on the volume and the morphology of fibroglandular tissue analyzed on 3D MRI. The accuracy of this model will be compared to those estimated from the existing models. The outcome will provide very useful information for understanding how the quantitative measure of fibroglandular tissue can be used to improve the accuracy of predicting breast cancer risk, to help women choose the optimal screening and risk management plan.
|Chen, Jeon-Hor; Liao, Fuyi; Zhang, Yang et al. (2017) 3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer. Acad Radiol 24:811-817|
|Chan, Siwa; Chen, Jeon-Hor; Li, Shunshan et al. (2017) Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants. BMC Cancer 17:274|
|Chen, Jeon-Hor; Chan, Siwa; Lu, Nan-Han et al. (2016) Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening. Acad Radiol 23:1154-61|
|Chen, Jeon-Hor; Chan, Siwa; Tang, Yi-Ting et al. (2015) Impact of positional difference on the measurement of breast density using MRI. Med Phys 42:2268-75|
|Chen, Jeon Hor; Yu, Hon J; Hsu, Christine et al. (2015) Background Parenchymal Enhancement of the Contralateral Normal Breast: Association with Tumor Response in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy. Transl Oncol 8:204-9|
|Lin, Yuting; Lin, Wei-Ching; Fwu, Peter T et al. (2015) Investigation of factors affecting hypothermic pelvic tissue cooling using bio-heat simulation based on MRI-segmented anatomic models. Comput Methods Programs Biomed 122:76-88|
|Fwu, Peter T; Chen, Jeon-Hor; Li, Yifan et al. (2015) Quantification of Regional Breast Density in Four Quadrants Using 3D MRI-A Pilot Study. Transl Oncol 8:250-7|
|Chen, Jeon-Hor; Yu, Hon; Lin, Muqing et al. (2013) Background parenchymal enhancement in the contralateral normal breast of patients undergoing neoadjuvant chemotherapy measured by DCE-MRI. Magn Reson Imaging 31:1465-71|
|Chen, Jeon-Hor; Pan, Wei-Fan; Kao, Julian et al. (2013) Effect of taxane-based neoadjuvant chemotherapy on fibroglandular tissue volume and percent breast density in the contralateral normal breast evaluated by 3T MR. NMR Biomed 26:1705-13|
|Lin, Muqing; Chen, Jeon-Hor; Wang, Xiaoyong et al. (2013) Template-based automatic breast segmentation on MRI by excluding the chest region. Med Phys 40:122301|