Assessment of anti-angiogenic therapies for the most severe form of brain cancer, glioblastoma, is extremely timely given the recent approval of bevacizumab yet the moderate response rate and the challenging side effects of these therapies. Clinical decision-making tools are badly needed;fortunately, our recently published data suggest that measurement of microvascular properties of the tumor using MRI and gadolinium-based approaches could be very useful, as with proper quantitation these methods appear to be capable of serving as an effective prognostic imaging biomarker, and may be beneficially combined with blood biomarkers. We propose to join the NCI's Quantitative Imaging Network (QIN) and develop improved analysis methods for dynamic contrast enhanced MRI and dynamic susceptibility MRI that will improve quantification and decrease variability. We propose to develop techniques that will be applicable in the multicenter setting through a bottom-up approach of simulations, phantom studies, retrospective analysis, and prospective analysis in patients undergoing treatment with anti-angiogenic therapies. We anticipate that our proposed approach, in particular through working in close harmony with the QIN, will improve the reliability of advanced microvascular MRI methods as potential imaging biomarkers, and pave the way for a clinically useful decision-making tool.
Advanced MRI methods may improve our ability to provide an accurate prognosis and potentially guide treatment choices for glioblastoma patients. Our proposed research will help establish a common, standardized approach to acquisition and analysis of two forms of vascular MRI that have shown excellent promise. We will do this by careful reduction of variability and by close participation in the National Cancer Institute's Quantitative Imaging Network. These efforts will enable these advanced techniques to become more widely available and more appropriately establish their benefit to patients.
|Shazeeb, Mohammed Salman; Kalpathy-Cramer, Jayashree; Issa, Bashar (2017) MRI Simulation Study Investigating Effects of Vessel Topology, Diffusion, and Susceptibility on Transverse Relaxation Rates Using a Cylinder Fork Model. Sci Rep 7:16223|
|Zhou, M; Scott, J; Chaudhury, B et al. (2017) Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol :|
|MICCAI/M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group (2017) Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Sci Data 4:170077|
|Chang, Ken; Bai, Harrison X; Zhou, Hao et al. (2017) Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging. Clin Cancer Res :|
|Ger, Rachel B; Mohamed, Abdallah S R; Awan, Musaddiq J et al. (2017) A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations. Sci Rep 7:11185|
|Kalpathy-Cramer, Jayashree; Chandra, Vyshak; Da, Xiao et al. (2017) Phase II study of tivozanib, an oral VEGFR inhibitor, in patients with recurrent glioblastoma. J Neurooncol 131:603-610|
|Andronesi, Ovidiu C; Esmaeili, Morteza; Borra, Ronald J H et al. (2017) Early changes in glioblastoma metabolism measured by MR spectroscopic imaging during combination of anti-angiogenic cediranib and chemoradiation therapy are associated with survival. NPJ Precis Oncol 1:|
|Malyarenko, Dariya I; Wilmes, Lisa J; Arlinghaus, Lori R et al. (2016) QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials. Tomography 2:396-405|
|Joint Head and Neck Radiotherapy-MRI Development Cooperative (2016) Dynamic contrast-enhanced MRI detects acute radiotherapy-induced alterations in mandibular microvasculature: prospective assessment of imaging biomarkers of normal tissue injury. Sci Rep 6:29864|
|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|
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