Mount Sinai Medical Center

“Clinical Outcome in Patients with Gliomas using Perfusion MR Metrics in a Multi-Institutional Setting”

The current reference standard for determining the biology of human glioma is histopathologic assessment (microscopic examination of tissue).  Currently, at least three approaches are utilized in this assessment. However, there is a lack of consensus among experts as to which is the single best approach and diagnoses are often open to subjective interpretations. These obstacles are further compounded by limitations of neurosurgical sampling error and inter-observer variability in neuropathologic assessment.

Clinicians and investigators believe that more objective measures than histopathologic assessment need be developed to predict glioma grade and determine subsequent therapy to improve clinical outcome.

Dr. Law’s study will evaluate the promise of integrating advanced Magnetic Resonance Imaging (MRI) into clinical practice as diagnostic and prognostic biomarkers of disease status and in monitoring response to therapy.

Although there are a number of institutions around the world using perfusion both in the clinical and research settings for characterizing human gliomas, there are very few, if any, multi-institutional outcome studies performed. As a result, there is no standardized method for acquiring and post-processing perfusion data.

The overarching goals of this proposal are to determine if perfusion MRI metrics can predict patient outcome better than current histopathologic techniques in a multi-center setting and if perfusion MRI can be standardized between institutions (acquired and post-processed in a multi-center setting).

Overcoming this hurdle will ensure that perfusion can be easily translated into clinical practice and be used for therapeutic monitoring as well as investigating therapeutic efficacy of novel agents. Once perfusion MRI is standardized, extramural funding will be sought to determine if perfusion metrics can be used in multi-institutional setting for predicting response to molecularly targeted therapies such as bevacizumab, an anti-VEGF (vascular endothelial growth factor) monoclonal antibody.






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