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High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features.

Ahmad Chaddad¹, Camel Tanougast¹

¹Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, 7 rue Marconie, Metz, 57070 Lorraine, France.

Advances in Bioinformatics

|December 8, 2015

Summary

This summary is machine-generated.

Statistical features aid in assessing glioblastoma (GBM) tumor heterogeneity via magnetic resonance imaging. Decision tree feature selection effectively identified key statistical features for discriminating tumor phenotypes, improving classification accuracy.

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Area of Science:

Radiology
Medical Imaging
Machine Learning

Background:

Statistical features are crucial for assessing tumor heterogeneity in magnetic resonance (MR) imaging.
Glioblastoma (GBM) phenotype classification requires identifying relevant statistical features.

Purpose of the Study:

To employ decision tree-based feature selection for identifying significant statistical features of glioblastoma (GBM).
To discriminate between active tumor (vAT) and edema/invasion (vE) phenotypes using selected features.

Main Methods:

Analysis of Variance (ANOVA) with p < 0.01 was used for initial feature selection.
Decision tree implementation defined an optimal subset of features for phenotype classification.
Naïve Bayes, Support Vector Machine, and Decision Tree classifiers evaluated performance.

Main Results:

Nine features were statistically significant (p < 0.01) for classifying vAT from vE.
Decision tree feature selection outperformed the full feature set.
Kurtosis and Skewness achieved high accuracy (58.33-75.00%) and AUC (73.88-92.50%).

Conclusions:

Statistical features offer quantitative, individualized measurements for glioblastoma patients.
Selected features effectively assess glioblastoma phenotype progression.