توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Voxel Based Treatment Prediction Using Diffusion Anisotropy Indices and Spatial Information in Glioblastoma Multiform Tumor
اطلاعات انتشار: هجدهمین کنفرانس مهندسی پزشکی ایران، سال
تعداد صفحات: ۴
In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform(GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatmentresponse of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre–treatment post–contrast T1–weighted Magnetic Resonance Images (pc–T1–MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc–T1–MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center arecalculated. Second, the method registers pre–treatment DAI maps and post–treatment pc–T1–MRI to pre–treatment pc–T1– MRI. Next, the tumor is segmented using thresholding technique from pc–T1–MRI. Then, Gd–enhanced voxels of the pre– and post–treatment pc–T1–MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K–Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for KNearest Neighbor based on well–established performance measures.<\div>
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