The overall goal of this application is to develop novel protein-based MRI approaches and to demonstrate their potential to resolve the diagnostic dilemma of treatment effects in neuro-oncology. Radiation therapy is a key modality for the treatment of malignant brain tumors, and radiation necrosis is a major complication of radiation therapy. The inability of currently available MRI techniques to differentiate tumor progression from radiation necrosis after brain tumor therapy complicates daily patient care, and is a critical barrier to investigating the efficacy of new therapies for brain tumors. Recently, we have designed a new molecular MRI technique, dubbed amide proton transfer (APT) imaging, which detects amide protons of endogenous mobile proteins and peptides in tissue. Our preliminary data in preclinical models demonstrate that APT imaging provides a unique signal in untreated glioma (hyperintense relative to normal and normal-appearing contralateral tissue), compared to pure radiation necrosis (hypointense or isointense).
The specific aims of this proposal are: (i) Optimize our protein-based MRI techniques and determine their accuracy in distinguishing radiation necrosis from tumor recurrence and (ii) Evaluate whether APT-MRI signal is an early biomarker to predict tumor response and animal survival. To achieve our goal, we have assembled a multidisciplinary team of basic scientists and clinicians. The results of this preclinical study will provide the first accuracy data for a new imaging biomarker for identifying tumor progression versus radiation necrosis, and for the prediction the efficacy of radiation therapy in malignant gliomas. These data are needed for a further study in clinical populations.
Our goal is to evaluate the ability and accuracy to distinguish between radiation necrosis and active tumor in preclinical models using a novel protein-based molecular MRI approach. The results would establish a new non-invasive MRI biomarker for assessing viable malignancy versus radiation necrosis and predicting tumor response to therapy.
|Zhang, Yi; Heo, Hye-Young; Lee, Dong-Hoon et al. (2017) Chemical exchange saturation transfer (CEST) imaging with fast variably-accelerated sensitivity encoding (vSENSE). Magn Reson Med 77:2225-2238|
|Heo, Hye-Young; Zhang, Yi; Lee, Dong-Hoon et al. (2017) Accelerating chemical exchange saturation transfer (CEST) MRI by combining compressed sensing and sensitivity encoding techniques. Magn Reson Med 77:779-786|
|Lee, Dong-Hoon; Heo, Hye-Young; Zhang, Kai et al. (2017) Quantitative assessment of the effects of water proton concentration and water T1 changes on amide proton transfer (APT) and nuclear overhauser enhancement (NOE) MRI: The origin of the APT imaging signal in brain tumor. Magn Reson Med 77:855-863|
|Jiang, Shanshan; Yu, Hao; Wang, Xianlong et al. (2016) Molecular MRI differentiation between primary central nervous system lymphomas and high-grade gliomas using endogenous protein-based amide proton transfer MR imaging at 3 Tesla. Eur Radiol 26:64-71|
|Heo, Hye-Young; Zhang, Yi; Lee, Dong-Hoon et al. (2016) Quantitative assessment of amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging with extrapolated semi-solid magnetization transfer reference (EMR) signals: Application to a rat glioma model at 4.7 Tesla. Magn Reson Med 75:137-49|
|Ma, Bo; Blakeley, Jaishri O; Hong, Xiaohua et al. (2016) Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas. J Magn Reson Imaging 44:456-62|
|Heo, Hye-Young; Zhang, Yi; Jiang, Shanshan et al. (2016) Quantitative assessment of amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging with extrapolated semisolid magnetization transfer reference (EMR) signals: II. Comparison of three EMR models and application to human brain glioma at Magn Reson Med 75:1630-9|
|Zhang, Yi; Heo, Hye-Young; Lee, Dong-Hoon et al. (2016) Selecting the reference image for registration of CEST series. J Magn Reson Imaging 43:756-61|
|Yu, Yang; Lee, Dong-Hoon; Peng, Shin-Lei et al. (2016) Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms. J Neuroimaging 26:626-634|
|Yang, Chen; Lee, Dong-Hoon; Mangraviti, Antonella et al. (2015) Quantitative correlational study of microbubble-enhanced ultrasound imaging and magnetic resonance imaging of glioma and early response to radiotherapy in a rat model. Med Phys 42:4762-72|
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