The long-term goal of this project is to improve brain cancer patient care by developing and validating advanced dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) methods for tumor characterization and therapeutic response assessment. Improving the biophysical characterization of brain tumors remains a highly relevant clinical objective. Currently, Contrast-Enhanced MRI (CE-MRI) is the primary means to detect primary and recurrent tumors and assess therapeutic response for essentially all brain tumor patients. Yet despite widespread use, CE-MRI accuracy remains limited, as it is incapable of delineating between tumor types, is confounded by treatment effects and can require months to discern true therapeutic response. We believe that advanced and clinically optimized DSC-MRI techniques can overcome these limitations and could impact radiographic diagnosis, response assessment and image-guided biopsies. During the first period of support of this grant we developed and validated, in pre-clinical animal models, novel DSC- MRI techniques for quantitative and simultaneous imaging of brain tumor hemodynamics, vascular integrity and cytoarchitecture. Given the demonstrated validity of these techniques in pre-clinical studies we now seek to optimize and validate their utility in brain tumor patients.
Our first Aim i s to develop and optimize a clinically practical, simultaneous spiral-based spin and gradient echo (SAGE) DSC-MRI strategy for mapping total and microvascular hemodynamics, vessel size, vessel architecture, vascular permeability and cytoarchitecture. Such a sequence overcomes many of the obstacles that reduce the quality of current DSC-MRI scans, requires lower doses of Gadolinium based contrast agents, enables more reliable perfusion measures and improves registration accuracy between DSC-MRI data and conventional images used for surgical planning. We next aim to establish threshold values for SAGE based metrics that accurately differentiate high-grade glioma recurrence from post treatment radiation effect and validate by direct correlation to image-guided tissue histopathology. This would enable clinicians to make earlier treatment decisions and initiate second-line therapies sooner.
Our final aim i s to validate the sensitivity of SAGE-based parameters to histologic tumor content both within enhancing and non-enhancing tissue. We will determine whether SAGE based metrics, individually or combined, enable the identification of tumor rich biopsy sampling sites, thereby providing a validated biomarker-based image guided biopsy approach that could improve histologic and genetic profiling. SAGE-based measures of tumor perfusion, permeability and cellularity would help overcome many of the limitations of CE-MRI as they are more likely to improve tumor characterization, localization and offer early and more specific indicators of treatment response.

Public Health Relevance

The proposed research focuses on the development of a clinically relevant magnetic resonance imaging method that can more reliably assess biophysics traits of brain tumors. The proposed approach could decrease health care costs by reducing the duration of imaging studies and improve the way treatments are planned and monitored.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA158079-08
Application #
9393982
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Zhang, Huiming
Project Start
2011-09-16
Project End
2021-11-30
Budget Start
2017-12-01
Budget End
2018-11-30
Support Year
8
Fiscal Year
2018
Total Cost
Indirect Cost
Name
St. Joseph's Hospital and Medical Center
Department
Type
DUNS #
131606022
City
Phoenix
State
AZ
Country
United States
Zip Code
85013
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Semmineh, Natenael B; Stokes, Ashley M; Bell, Laura C et al. (2017) A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. Tomography 3:41-49
Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M et al. (2017) Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 19:128-137

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