• Xintao Hu, Kelvin K Wong, Geoffrey S Young, Lei Guo, and Stephen T Wong.
• Department of Radiology, Center for Bioengineering and Informatics, The Methodist Hospital Research Institute, The Methodist Hospital, Houston, Texas, USA.
• J Magn Reson Imaging. 2011 Feb 1;33(2):296-305.

PurposeTo automatically differentiate radiation necrosis from recurrent tumor at high spatial resolution using multiparametric MRI features.Materials And MethodsMRI data retrieved from 31 patients (15 recurrent tumor and 16 radiation necrosis) who underwent chemoradiation therapy after surgical resection included post-gadolinium T1, T2, fluid-attenuated inversion recovery, proton density, apparent diffusion coefficient (ADC), and perfusion-weighted imaging (PWI) -derived relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time maps. After alignment to post contrast T1WI, an eight-dimensional feature vector was constructed. An one-class-support vector machine classifier was trained using a radiation necrosis training set. Classifier parameters were optimized based on the area under receiver operating characteristic (ROC) curve. The classifier was then tested on the full dataset.ResultsThe sensitivity and specificity of optimized classifier for pseudoprogression was 89.91% and 93.72%, respectively. The area under ROC curve was 0.9439. The distribution of voxels classified as radiation necrosis was supported by the clinical interpretation of follow-up scans for both nonprogressing and progressing test cases. The ADC map derived from diffusion-weighted imaging and rCBV, rCBF derived from PWI were found to make a greater contribution to the discrimination than the conventional images.ConclusionMachine learning using multiparametric MRI features may be a promising approach to identify the distribution of radiation necrosis tissue in resected glioblastoma multiforme patients undergoing chemoradiation.Copyright © 2011 Wiley-Liss, Inc.

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