A new treatment appears hopeful for the most severe form of brain cancer, glioblastoma. Monitoring these antiangiogenic therapies mechanistically would greatly aid our understanding of how they might best be used in patients, and recent data suggest that measurement of brain perfusion with MRI using gadolinium-based approaches could be very useful, particularly as an imaging biomarker. However, no single methodology for acquisition or analysis has been tested in at variable field strengths or across vendor platforms. We propose to develop acquisition and analysis methods that will be compatible across field strengths and vendor platforms and quantify the differences in acquisition and analysis. We will first study volunteers to understand the on-site manufacturer and field strength variability. We will then study patients with newly diagnosed glioblastoma who are undergoing treatment with anti-angiogenic agents. It is expected that such an approach will improve the reliability of perfusion MRI as a potential imaging biomarker, and pave the way for a large-scale, multi-center trial that could standardize the implementation of perfusion MRI in measuring tumor response to anti-angiogenic therapies.

Public Health Relevance

Perfusion MRI is a technique that may improve our ability to provide an accurate diagnosis and prognosis as well as potentially guide treatment choices for both newly diagnosed and recurrent brain tumors. Our proposed research will help establish a common, standardized approach to acquisition and analysis of perfusion MRI data across different MRI machines with a goal of minimizing variations across machines. This will enable this technique to become more widely available and more appropriately establish its benefit to patients.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Acute Neural Injury and Epilepsy Study Section (ANIE)
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Fountain, Jane W
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Massachusetts General Hospital
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Eichner, Cornelius; Jafari-Khouzani, Kourosh; Cauley, Stephen et al. (2014) Slice accelerated gradient-echo spin-echo dynamic susceptibility contrast imaging with blipped CAIPI for increased slice coverage. Magn Reson Med 72:770-8
Emblem, Kyrre E; Due-Tonnessen, Paulina; Hald, John K et al. (2014) Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI. J Magn Reson Imaging 40:47-54
Pinho, Marco C; Polaskova, Pavlina; Kalpathy-Cramer, Jayashree et al. (2014) Low incidence of pseudoprogression by imaging in newly diagnosed glioblastoma patients treated with cediranib in combination with chemoradiation. Oncologist 19:75-81
Emblem, Kyrre E; Mouridsen, Kim; Bjornerud, Atle et al. (2013) Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat Med 19:1178-83
Sorensen, A Gregory; Emblem, Kyrre E; Polaskova, Pavlina et al. (2012) Increased survival of glioblastoma patients who respond to antiangiogenic therapy with elevated blood perfusion. Cancer Res 72:402-7
Bjornerud, Atle; Sorensen, A Gregory; Mouridsen, Kim et al. (2011) T1- and T2*-dominant extravasation correction in DSC-MRI: part I--theoretical considerations and implications for assessment of tumor hemodynamic properties. J Cereb Blood Flow Metab 31:2041-53
Emblem, Kyrre E; Bjornerud, Atle; Mouridsen, Kim et al. (2011) T(1)- and T(2)(*)-dominant extravasation correction in DSC-MRI: part II-predicting patient outcome after a single dose of cediranib in recurrent glioblastoma patients. J Cereb Blood Flow Metab 31:2054-64