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3 March 2009 Histogram-based classification with Gaussian mixture modeling for GBM tumor treatment response using ADC map
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601Y (2009)
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
This study applied a Gaussian Mixture Model (GMM) to apparent diffusion coefficient (ADC) histograms to evaluate glioblastoma multiforme (GBM) tumor treatment response using diffusion weighted (DW) MR images. ADC mapping, calculated from DW images, has been shown to reveal changes in the tumor's microenvironment preceding morphologic tumor changes. In this study, we investigated the effectiveness of features that represent changes from pre- and post-treatment tumor ADC histograms to detect treatment response. The main contribution of this work is to model the ADC histogram as the composition of two components, fitted by GMM with expectation maximization (EM) algorithm. For both pre- and post-treatment scans taken 5-7 weeks apart, we obtained the tumor ADC histogram, calculated the two-component features, as well as the other standard histogram-based features, and applied supervised learning for classification. We evaluated our approach with data from 85 patients with GBM under chemotherapy, in which 33 responded and 52 did not respond based on tumor size reduction. We compared AdaBoost and random forests classification algorithms, using ten-fold cross validation, resulting in a best accuracy of 69.41%.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jing Huo, Hyun J. Kim, Whitney B. Pope, Kazunori Okada, Jeffery R. Alger, Yang Wang, Jonathan G. Goldin, and Matthew S. Brown "Histogram-based classification with Gaussian mixture modeling for GBM tumor treatment response using ADC map", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601Y (3 March 2009);

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