Glioblastoma (GBM) is the most common primary malignant neoplasm of the adult brain. Even after multimodal therapy, treatment outcomes remain poor, with a median survival of approximately one year. A central challenge facing investigators in the modern era is how to resolve the heterogeneity inherent in GBM pathology using technology and how to identify individual genetic or molecular markers that indicate how treatment can be individualized to improve outcomes with an emphasis on using this heterogeneity to improve patient care. With advances in imaging and the potential for genetic sequence analysis, increasingly clinicians and researchers have focused on specific clinical, imaging, and genetic biomarkers to allow the personalization of brain tumor treatment in an attempt to improve the limitations we have faced in extending patient survival from this devastating disease. Specific methodologies have been developed to allow genetic microarray analysis of patient's tumor tissue, and this type of research is ongoing at one of our participating institutions, Swedish Medical Center. In addition, centers such as Washington University School of Medicine in St. Louis, Missouri have extensive experience pursuing advanced imaging biomarkers and their applications to clinical neuro-oncology research. Of importance, however, although clinicians and researchers have come to recognize that in-vivo imaging technologies may have as much if not more relevance than genetic biomarkers in the personalization of brain tumor treatment, clinical trials attempting to validate these biomarkers and correlate them with particular outcomes have been limited by a lack of technology infrastructure that would allow multi-site image acquisition, processing, data analysis, subsequent correlation with clinical and genetic data, and ultimately sharing of anonymized data with other researchers from a central archiving site. This proposal seeks to use BIRN infrastructure to integrate neuroimaging, genetic microarray, and clinical data with a focus on integrating imaging biomarkers into prospective clinical research in patients with malignant brain tumors. In this project, a consortium of neuro-oncology research centers will be federated to obtain a unified set of clinical, genetic, and imaging data. In the initial phase, 100 patients with malignant brain tumors at two participating sites will be studied. Our ultimate goal will be to use the developed protocols and informatics infrastructure to expand the consortium to include a large number neuro-oncology clinical sites suitable for executing large scale clinical trials that will facilitate the generation of data to identify which imaging biomarkers are relevant for the personalization of brain tumor treatment and ultimately improvement of outcomes for patients with this devastating disease.
Project Relevance: Glioblastoma is the most common primary malignant neoplasm of the adult brain. In this proposal, we proposed to implement an imaging protocol and informatics platform suitable for running multi-site trials to investigate treatments for affected individuals. We anticipate that the developed resources will therefore lead directly to improved national healthcare.
|Dierker, Donna; Roland, Jarod L; Kamran, Mudassar et al. (2017) Resting-state Functional Magnetic Resonance Imaging in Presurgical Functional Mapping: Sensorimotor Localization. Neuroimaging Clin N Am 27:621-633|
|Milchenko, Mikhail; Snyder, Abraham Z; LaMontagne, Pamela et al. (2016) Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research. Neuroinformatics 14:305-17|
|Vlassenko, Andrei G; McConathy, Jonathan; Couture, Lars E et al. (2015) Aerobic Glycolysis as a Marker of Tumor Aggressiveness: Preliminary Data in High Grade Human Brain Tumors. Dis Markers 2015:874904|
|Fouke, Sarah Jost; Benzinger, Tammie L; Milchenko, Mikhail et al. (2014) The comprehensive neuro-oncology data repository (CONDR): a research infrastructure to develop and validate imaging biomarkers. Neurosurgery 74:88-98|
|Milchenko, Mikhail V; Rajderkar, Dhanashree; LaMontagne, Pamela et al. (2014) Comparison of perfusion- and diffusion-weighted imaging parameters in brain tumor studies processed using different software platforms. Acad Radiol 21:1294-303|
|Prior, Fred W; Fouke, Sarah J; Benzinger, Tammie et al. (2013) Predicting a multi-parametric probability map of active tumor extent using random forests. Conf Proc IEEE Eng Med Biol Soc 2013:6478-81|
|Jagadeesan, B D; Cross 3rd, D T; Delgado Almandoz, J E et al. (2012) Susceptibility-weighted imaging: a new tool in the diagnosis and evaluation of abnormalities of the vein of Galen in children. AJNR Am J Neuroradiol 33:1747-51|
|Jagadeesan, Bharathi D; Delgado Almandoz, Josser E; Moran, Christopher J et al. (2011) Accuracy of susceptibility-weighted imaging for the detection of arteriovenous shunting in vascular malformations of the brain. Stroke 42:87-92|
|Jagadeesan, Bharathi D; Delgado Almandoz, Josser E; Benzinger, Tammie L S et al. (2011) Postcontrast susceptibility-weighted imaging: a novel technique for the detection of arteriovenous shunting in vascular malformations of the brain. Stroke 42:3127-31|
|Roe, C M; Mintun, M A; Ghoshal, N et al. (2010) Alzheimer disease identification using amyloid imaging and reserve variables: proof of concept. Neurology 75:42-8|