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.
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.
|Schmainda, K M; Prah, M A; Rand, S D et al. (2018) Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 39:1008-1016|
|Quarles, C Chad; Bell, Laura C; Stokes, Ashley M (2018) Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI. Neuroimage :|
|Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006|
|Woodall, Ryan T; Barnes, Stephanie L; Hormuth 2nd, David A et al. (2018) The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains. Magn Reson Med 80:330-340|
|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|
|Bell, L C; Does, M D; Stokes, A M et al. (2017) Optimization of DSC MRI Echo Times for CBV Measurements Using Error Analysis in a Pilot Study of High-Grade Gliomas. AJNR Am J Neuroradiol 38:1710-1715|
|Sorace, Anna G; Syed, Anum K; Barnes, Stephanie L et al. (2017) Quantitative [18F]FMISO PET Imaging Shows Reduction of Hypoxia Following Trastuzumab in a Murine Model of HER2+ Breast Cancer. Mol Imaging Biol 19:130-137|
|Bell, Laura C; Hu, Leland S; Stokes, Ashley M et al. (2017) Characterizing the Influence of Preload Dosing on Percent Signal Recovery (PSR) and Cerebral Blood Volume (CBV) Measurements in a Patient Population With High-Grade Glioma Using Dynamic Susceptibility Contrast MRI. Tomography 3:89-95|
|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|
Showing the most recent 10 out of 23 publications