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High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features.

Chaddad A, Tanougast C - Adv Bioinformatics (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Related in: MedlinePlus

Block diagram of the proposed approach.
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fig2: Block diagram of the proposed approach.

Mentions: To prove the hypothesis, we focused on the optimal subset features from the statistical features which are derived from GBM tumors using active GBM portion with high intensity pixels vAT and peritumoral vE of GBM with middle intensity pixels. Two Gaussian distributions could clearly be observed in the histogram data of GBM (Figure 1). To assist automated recognition of the GBM phenotype based heterogeneity, histogram statistical features and classifier techniques were used for discriminating active tumor parts from edema parts in FLAIR images. Decision tree was considered to recognize the dominant statistical features which represented the foremost characteristic of GBM heterogeneity [13]. The proposed approach is presented in Figure 2.


High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features.

Chaddad A, Tanougast C - Adv Bioinformatics (2015)

Block diagram of the proposed approach.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4660016&req=5

fig2: Block diagram of the proposed approach.
Mentions: To prove the hypothesis, we focused on the optimal subset features from the statistical features which are derived from GBM tumors using active GBM portion with high intensity pixels vAT and peritumoral vE of GBM with middle intensity pixels. Two Gaussian distributions could clearly be observed in the histogram data of GBM (Figure 1). To assist automated recognition of the GBM phenotype based heterogeneity, histogram statistical features and classifier techniques were used for discriminating active tumor parts from edema parts in FLAIR images. Decision tree was considered to recognize the dominant statistical features which represented the foremost characteristic of GBM heterogeneity [13]. The proposed approach is presented in Figure 2.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Related in: MedlinePlus