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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

Receiver operating characteristic curves for distinguishing between vAT and vE. FFS denotes full feature set, and FS is the feature selection.
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fig5: Receiver operating characteristic curves for distinguishing between vAT and vE. FFS denotes full feature set, and FS is the feature selection.

Mentions: Moreover, AUC value shows a range of 77.66–96.05% for full feature set and 73.88–92.50% for subset feature with a highest value achieved using the decision tree classifier (Figure 5). This demonstrates the feasibility to discriminate between vAT and vE using the feature selection extracted from the FLAIR sequence.


High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features.

Chaddad A, Tanougast C - Adv Bioinformatics (2015)

Receiver operating characteristic curves for distinguishing between vAT and vE. FFS denotes full feature set, and FS is the feature selection.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Receiver operating characteristic curves for distinguishing between vAT and vE. FFS denotes full feature set, and FS is the feature selection.
Mentions: Moreover, AUC value shows a range of 77.66–96.05% for full feature set and 73.88–92.50% for subset feature with a highest value achieved using the decision tree classifier (Figure 5). This demonstrates the feasibility to discriminate between vAT and vE using the feature selection extracted from the FLAIR sequence.

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