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High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features

Abstract : 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.
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Submitted on : Wednesday, May 2, 2018 - 3:31:14 PM
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Ahmad Chaddad, Camel Tanougast. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Advances in Bioinformatics, 2015, 2015, pp.1 - 7. ⟨10.1155/2015/728164⟩. ⟨hal-01783640⟩



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