Abstract

In clinical oncology and drug development there is genuine need for sensitive and reproducible quantitative imaging methods for early prediction of therapy response and accurate assessment of post-therapy residual cancer. Such methods have the potential to spare non-responding patients from ineffective and/or toxic treatments, improve clinical management, and accelerate efficacy evaluation of novel therapies. Dynamic- Contrast-Enhanced (DOE) MRI can provide an excellent measure of therapy-induced tumor vascular changes. The Standard-Model (SM) for pharmacokinetic DCE-MRl analysis incorrectly assumes effectively infinitely fast equilibrium inter-compartmental water exchange kinetics and, as a result, often underestimates the pharmacokinetic parameters Ktrans and Ve. The Shutter-Speed Model (SSM) accounts for finite water exchange kinetics effects and corrects the imaging biomarker underestimations. It has proven more sensitive to vascular changes than the SM. In addition, SSM DCE-MRl allows quantification of novel imaging biomarkers, such as ?Ktrans(= Ktrans (SSM) - Ktrans (SM)] - a measure of precisely the water exchange (shutter-speed) effect on Ktrans estimation. ?Ktransis not only a more sensitive biomarker for therapeutic response, but also less prone to other systematic errors often observed in DCE-MRl quantification.
In Specific Aim 1, the Shutter-Speed Model will be applied to phase l/ll clinical trials in two disease areas (breast cancer and soft tissue sarcoma), SSM DCE-MRl will be compared/combined with SM DCE-MRl, diffusion-weighted MRI and tumor size measurement for assessment of therapy response.
In Aim 2, the effects of data acquisition and processing schemes on DCE-MRl biomarker values will be investigated within the context of therapeutic monitoring.
In Aims 1 and 2, pathology results will be used as endpoints for correlation with imaging results and statistical analyses.
In Aim 3, caBIG-compliant software tools will be developed that utilize a single and/or a set of imaging biomarkers to aid clinical research.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA154602-03
Application #
8533769
Study Section
Special Emphasis Panel (ZCA1-SRLB-Y (M1))
Program Officer
Nordstrom, Robert J
Project Start
2011-09-01
Project End
2016-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
3
Fiscal Year
2013
Total Cost
$542,095
Indirect Cost
$190,085

  Institution

Name
Oregon Health and Science University
Department
Type
Organized Research Units
DUNS #
096997515
City
Portland
State
OR
Country
United States
Zip Code
97239

  Publications

Hectors, Stefanie J; Wagner, Mathilde; Besa, Cecilia et al. (2018) Multiparametric FDG-PET/MRI of Hepatocellular Carcinoma: Initial Experience. Contrast Media Mol Imaging 2018:5638283
Sorace, Anna G; Partridge, Savannah C; Li, Xia et al. (2018) Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial. J Med Imaging (Bellingham) 5:011019
Jarrett, Angela M; Hormuth, David A; Barnes, Stephanie L et al. (2018) Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 63:105015
Bane, Octavia; Hectors, Stefanie J; Wagner, Mathilde et al. (2018) Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study. Magn Reson Med 79:2564-2575
Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003
Joint Head and Neck Radiotherapy-MRI Development Cooperative (2018) Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers. Sci Data 5:180008
Hectors, Stefanie J; Besa, Cecilia; Wagner, Mathilde et al. (2017) DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness. J Magn Reson Imaging 46:837-849
Ahmed, Zaki; Levesque, Ives R (2017) Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches. Magn Reson Med 78:1547-1557
Thibault, Guillaume; Tudorica, Alina; Afzal, Aneela et al. (2017) DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. Tomography 3:23-32
Joint Head and Neck Radiotherapy-MRI Development Cooperative (2017) A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations. Sci Rep 7:11185

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