Fig 1.
Division of data and under-sampling.
From the available 64 LGGS and 191 HGGs, one testing subset and 100 training subsets with balanced classes were created by randomly choosing gliomas.
Table 1.
Studied texture features.
Table 2.
Combinations studied, and numbers of calculated features.
Fig 2.
Obtaining the ordered highest frequency features.
Considering the d first ordered features (according to their p-value) of each training subset, histograms of the features located in the same place were created. Then, from the histograms, the highest frequency features were obtained.
Fig 3.
Complete process proposed for the classification of low-grade and high-grade gliomas.
Fig 4.
Three graphs are shown with the results of combinations 1 (a), 3 (b) and 6 (c), using different numbers of variables (horizontal axis). These results consist of the percentages (vertical axis) of sensitivity, specificity and accuracy obtained after applying the models to the testing subset. Three further graphs (d, e and f) indicate the values (in arbitrary units, au) of the mean absolute errors (mae) obtained in each model. The best classification results were obtained in combination 6 by the model with five variables (▼, ▲).
Fig 5.
a. Percentages of sensitivity, specificity and accuracy (vertical axis), obtained by the 30 reduced models, in addition to the combination of variables utilized in each one (horizontal axis), using the following numbering: 1, Fszm.z.perc; 2, Fszm.zs.var; 3, Fszm.lzlge; 4, Fszm.lze; and 5, Fszm.zsnu. The first four were measured in T2 contrasts and the fifth in T1Gd contrasts. All features were measured in the NCR/NET region. b. Values (in arbitrary units, au) of the mean absolute errors (mae) obtained in each reduced model. The reduced model that obtained the best results with the lowest number of variables and the smallest error corresponded to the one that combined variables 1-2-5 (▼, ▲).
Table 3.
Data for the best reduced model.
Fig 6.
Predictions made by the best reduced model, when applied to the testing subset.
Testing gliomas (34 LGGs and 34 HGGs; vertical axis) and their predictions (in arbitrary units, au; horizontal axis) are presented. A solid vertical line at zero indicates the chosen threshold. Dotted vertical lines at -10 and 10 indicate the ideal prediction of the LGGs and HGGs, respectively. The filled circles and squares correspond to the true HGGs and true LGGs, respectively, and the empty circles and squares correspond to the false LGGs (or HGGs misclassified) and false HGGs (or LGGs misclassified), respectively.
Fig 7.
Boxplots of the texture features or variables 1-2-5, calculated from the testing gliomas.
The grades of the testing gliomas (horizontal axis) and their texture values (in arbitrary units, au; vertical axis) are presented. a. Boxplot of feature number 1, Fszm.z.perc (measured in the T21 contrast). b. Boxplot of feature number 2, Fszm.zs.var (measured in the T21 contrast). c. Boxplot of feature number 5, Fszm.zsnu (measured in the T1Gd1 contrast).