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A Glioblastoma Tumor Growth Prediction Model Using Volumetric MR Spectroscopic Imaging for Radiation Therapy Response

E Schreibmann

E. Schreibmann1, S Cordova2, H. Shim2, I. Crocker1, H. K. G. Shu1 1Department of Radiation Oncology, Winship Cancer Institute of Atlanta, GA, 2Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA


TH-CD-204-3 (Thursday, July 16, 2015) 10:00 AM - 12:00 PM Room: 204

Purpose: We implemented high resolution, 3D whole brain MR spectroscopic imaging (MRSI) technology to provide additional information on glioblastoma (GBM) brain tumors not readily apparent on standard-of-care MR images. Here, we report integrating these images into a prediction model of tumor growth with radiation therapy.

Materials and Methods: To predict therapy response, a mathematical algorithm of tumor infiltration based on the classical reaction-diffusion equation was customized to create a map of tumoral density for every location in the brain from the presence of choline (Cho), a proliferation-derived metabolite and N-acetylaspartate (NAA), a neuronal metabolite. Input images are pre and mid-treatment (2 weeks) MRSI acquisitions. The model is initialized using the gradient of the Cho/NAA ratio in the vicinity of tumor (periphery) as a measure of tumor infiltration, and the absolute Cho level as a measure of actively proliferating tumor cells. Customized, per-patient model parameters are tested until simulated images of tumor growth derived from the pre-treatment MRSI datasets match mid-treatment imaging at 2 weeks. Customization is done by iteratively modifying model parameters to minimize the discrepancy between simulated and acquired images as judged by the squared sum differences of voxel intensities within the PTV. We hypothesize that this model will predict response at week 10, 4 weeks following completion of concurrent radiation and temozolomide.

Results: Utility of the MRSI-based tumor growth model was investigated by comparing the net treatment gain against clinical observations in GBM patients. Model parameters of tumor proliferation plus effectiveness of therapy correlated with clinical outcomes being positive if progression free survival is longer than 6 months.

Conclusions: MRSI could provide more accurate information the reaction-diffusion tumor growth model than conventional MR images, as it provides information about proliferation and infiltration directly and can be easily integrated with clinical observations to predict per-patient response to therapy.

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