The overall goal is to develop and validate both standard and novel perfusion-weighted MRI (PWI) and diffusion-weighted MRI (DWI) biomarkers to monitor treatment response for both therapeutic clinical trials and standard of care treatment plans for patients with brain tumors. This goal addresses an urgent need for better ways to monitor targeted therapies, for which standard measures of enhancing tumor volumes are no longer sufficient. The two PWI methods that will be characterized for clinical trials are based on many years of PWI research in Dr Schmainda's laboratory. The first more wide-spread DSC (dynamic suceptibility contrast) approach provides tumor rCBV (relative cerebral blood volume) measurements obtained after a pre- load of contrast agent and corrected for confounding contrast agent leakage effects. A multi-year comparison study of rCBV methods suggests that this algorithm is one of the most accurate approaches currently available. The second approach, while less-proven has high-potential to become the most comprehensive perfusion solution. It consists of using a dual-echo gradient-echo (DEGES) spiral method, which enables the simultaneous collection of both DSC (dynamic susceptibility contrast) and DCE (dynamic contrast enhanced) perfusion data using only a single dose of contrast agent and incorporates comprehensive correction for leakage effects. Also, newly developed for purposes of longitudinal monitoring is the """"""""standardization"""""""" of rCBV images where rCBV values are transformed to a standard measurement scale so greater visual and quantitative consistency is maintained across studies. Subjective errors are minimized since user-defined reference ROIs are no longer needed for quantification. These developments are clearly beneficial for ease of incorporation into clinical trials and standard practice. In recent years it has become increasingly clear that the full evaluation of brain tumor response also requires the assessment of tumor cell density, death and invasion, especially in non-enhancing tumors. In this context, our laboratory has put forth great effort, evidenced by several recent publications, to develop and validate diffusion methods to monitor tumor growth and invasion. By computing changes in the apparent diffusion coefficient (ADC) across time, we have created functional diffusion maps (fDM) within non-contrast- agent-enhancing regions. We have found that changes in ADC suggestive of increased cell density were more predictive of response to the anti-angiogenic drug, bevacizumab, than standard contrast-agent enhanced MRI. While both PWI and DWI have demonstrated great promise for treatment monitoring, studies defining their test-retest repeatability, necessary for use of these techniques in clinical trials, are lacking, and thus represent the focus of Aim 1. In addition, early results suggest that hybrid PWI/DWI maps will likely provide the most complete assessment of treatment response, a hypothesis that will be tested in Aim 2. Finally, in order to make the optimized PWI/DWI technology and workflow available in a robust and cost-effective manner for clinical trials and standard practice, Aim 3 involves the development of a commercial integrated image analysis platform for use in large-scale multi-center clinical trials. Taken together this effort should result in a robust and ready to use advanced imaging platform for the advanced imaging evaluation of both conventional and targeted brain tumor therapies. This should lead to greater clinical trial efficiency enabling more rapid drug discovery and translation and improved individualized care for patients.
This U01 application proposes the development and validation of a combined perfusion and diffusion MRI (magnetic resonance imaging) methods for use in clinical trials to evaluate the response of brain tumors to targeted therapies. Given that standard MRI methods to monitor treatment response have been found lacking this addresses an urgent clinical need. The perfusion technology is based on developments made over the past twelve years in the Principal Investigator 's laboratory and therefore may represent the most comprehensive and accurate solution to monitoring tumor vessel growth. This combined with recent advances in diffusion imaging, which provide complementary information about tumor cell invasion, has the potential to change the way by which brain tumor treatments are monitored and aid in the discovery of new treatments and combinations. Finally, working in close collaboration with an industrial partner, the proven technical methods resulting from this study will be translated into a low cost commercial software platform for widespread use within the QIN and beyond.
|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|
|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|
|Prah, Melissa A; Al-Gizawiy, Mona M; Mueller, Wade M et al. (2018) Spatial discrimination of glioblastoma and treatment effect with histologically-validated perfusion and diffusion magnetic resonance imaging metrics. J Neurooncol 136:13-21|
|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|
|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|
|Nguyen, H S; Milbach, N; Hurrell, S L et al. (2016) Progressing Bevacizumab-Induced Diffusion Restriction Is Associated with Coagulative Necrosis Surrounded by Viable Tumor and Decreased Overall Survival in Patients with Recurrent Glioblastoma. AJNR Am J Neuroradiol 37:2201-2208|
|McGarry, Sean D; Hurrell, Sarah L; Kaczmarowski, Amy L et al. (2016) Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. Tomography 2:223-228|
|Paulson, Eric S; Prah, Douglas E; Schmainda, Kathleen M (2016) Spiral Perfusion Imaging With Consecutive Echoes (SPICE™) for the Simultaneous Mapping of DSC- and DCE-MRI Parameters in Brain Tumor Patients: Theory and Initial Feasibility. Tomography 2:295-307|
|Huang, Wei; Chen, Yiyi; Fedorov, Andriy et al. (2016) The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography 2:56-66|
|Boxerman, Jerrold L; Schmainda, Kathleen M; Zhang, Zheng et al. (2015) Dynamic susceptibility contrast MRI measures of relative cerebral blood volume continue to show promise as an early response marker in the setting of bevacizumab treatment. Neuro Oncol 17:1538-9|
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