Currently, breast tumor response to chemotherapy is monitored by frank changes in tumor morphology as measured by physical exam, mammography and/or ultrasound. Clinical judgments of the effectiveness of treatments are subjective and prone to error. A repeatable, non-invasive imaging method which can reliably assess tumor response would greatly improve clinical breast cancer care. The specialized magnetic resonance imaging (MRI) methods of dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) have matured to the point where they offer unique information on tumor status. DCE-MRI reports on relevant physiological parameters including vessel perfusion, vessel wall permeability, extravascular extracellular volume fraction, and (recently) cell size. DW-MRI can provide detailed information on tissue cellularity. We propose to combine a novel analysis of DCE-MRI data with DW-MRI data obtained at 3T to provide functional assessments of the response of breast cancer to treatment. We hypothesize that integrating these quantitative MRI methods will provide accurate and predictive measurements of tumor response after the first cycle of treatment. Furthermore, we will validate the imaging metrics by performing quantitative co- registering the in vivo MR images to histopathological staining of ex vivo mastectomy specimens. To test these hypotheses we will pursue three specific aims: 1. In patients selected by a specific treatment protocol, differentiate responders vs. non-responders by the differences in tumor vessel blood flow and integrity, tissue volume fractions, and tumor cell density. 2. Perform uni- and multi-variate correlation analysis between blood flow, vessel perfusion, extravascular extracellular volume fraction, intracellular water lifetime, and cell density to provide a more complete understanding of the breast tumor environment. 3. Perform quantitative co-registration between in vivo MR images and ex vivo histological specimens to validate the MRI measures. The proposed research will combine several new imaging methods to obtain quantitative information on how breast tumors respond to treatment. We hypothesize that this will let us distinguish responders from non- responders early in the course of treatment so that treatments can be optimized on an individual basis.

Public Health Relevance

The proposed research will combine specialized magnetic resonance imaging (MRI) methods to obtain quantitative information on how human breast tumors respond to treatment. Developing methods of tumor characterization that could be applied early in treatment to assess response would have profound impact on the management of many patients. We hypothesize that the combined analysis of contrast enhanced MRI and diffusion MRI data will provide predictive, non-invasive measurements of tumor response to treatment.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA129961-03
Application #
7761188
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Henderson, Lori A
Project Start
2008-04-01
Project End
2012-01-31
Budget Start
2010-02-01
Budget End
2011-01-31
Support Year
3
Fiscal Year
2010
Total Cost
$316,562
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Kang, Hakmook; Hainline, Allison; Arlinghaus, Lori R et al. (2018) Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results. J Med Imaging (Bellingham) 5:011015
Whisenant, Jennifer G; Dortch, Richard D; Grissom, William et al. (2016) Bloch-Siegert B1-Mapping Improves Accuracy and Precision of Longitudinal Relaxation Measurements in the Breast at 3 T. Tomography 2:250-259
Abramson, Richard G; Lambert, Katrina F; Jones-Jackson, Laurie B et al. (2015) Prone Versus Supine Breast FDG-PET/CT for Assessing Locoregional Disease Distribution in Locally Advanced Breast Cancer. Acad Radiol 22:853-9
Smith, David S; Li, Xia; Arlinghaus, Lori R et al. (2015) DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ 3:e909
Dula, Adrienne N; Dewey, Blake E; Arlinghaus, Lori R et al. (2015) Optimization of 7-T chemical exchange saturation transfer parameters for validation of glycosaminoglycan and amide proton transfer of fibroglandular breast tissue. Radiology 275:255-61
Li, Xia; Kang, Hakmook; Arlinghaus, Lori R et al. (2014) Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol 7:14-22
Li, Xia; Arlinghaus, Lori R; Ayers, Gregory D et al. (2014) DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings. Magn Reson Med 71:1592-602
Atuegwu, Nkiruka C; Li, Xia; Arlinghaus, Lori R et al. (2014) Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results. Med Phys 41:052302
Smith, David S; Li, Xia; Abramson, Richard G et al. (2013) Potential of compressed sensing in quantitative MR imaging of cancer. Cancer Imaging 13:633-44
Abramson, Richard G; Li, Xia; Hoyt, Tamarya Lea et al. (2013) Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results. Magn Reson Imaging 31:1457-64

Showing the most recent 10 out of 26 publications