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Advances in Bioinformatics
Volume 2015 (2015), Article ID 728164, 7 pages
Research Article

High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features

Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, 7 rue Marconie, Metz, 57070 Lorraine, France

Received 24 May 2015; Revised 28 September 2015; Accepted 1 October 2015

Academic Editor: Klaus Jung

Copyright © 2015 Ahmad Chaddad and Camel Tanougast. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naïve Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33–75.00% accuracy classifier and 73.88–92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression.