Investigating Feature Redundancy - Learning to Predict Disease-Free Survival

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4.2 Learning to Predict Disease-Free Survival

4.2.3 Investigating Feature Redundancy

Classification experiments, described in Section 3.8.3, were performed to investi-gateredundancyamong clinical factors, PET parameters and radiomics features. Re-dundancy is referred to in this thesis as repeated feature information, measured in terms ofintra-andinter-featurecorrelations.

Adjustments to Reduce Intra-Feature Correlations

Recall from Section 3.8.3 that radiomics features were modified after Hassan et al.

(2018) [119] to reduce their dependency on the number of image intensity bins. This dependency, referred to as intra-feature correlation, was measured using the Intra-class Correlation Coefficient(ICC) [31]. The ICC was calculated for groups of features extracted from differently discretised images using the same feature definition. This procedure was applied to features from the standard and artifact corrected feature matrices. A higher ICC score after feature modification implies that the correlation

Adjustments to Reduce Intra-Feature Correlations

between the image discretisation level and the feature was reduced. Abbreviations to texture feature categories are given in Table 4.2.

Figure 4.14 shows that adjustments to features, given in Table 3.1, increased the ICC for all PET and CT texture features. Thus, by modifying these features, information on image discretisation was incorporated to relax the association between features and the level of discretisation.

The ICC scores of features extracted from artefact-filtered images are shown in Fig-ure 4.15. In FigFig-ure 4.15, the ICC score exceeds 0.8 for all featFig-ures prior to modifica-tions. Thereby, these features appeared to have become invariant to image discreti-sation after artefact correction. Moreover, adjustments to these features decreased the ICC, meaning that the correlation between the features and the number of image intensity bins increased.

GLCM Contrast

GLCM Difference AverageGLCM Difference EntropyGLCM Difference VarianceGLCM Joint EntropyGLCM Sum AverageGLCM Sum EntropyGLRLM GLNUGLRLM HGLREGLRLM SRHGLENGTDM ComplexityNGTDM ContrastNGTDM Strength 0.00

0.20 0.40 0.60 0.80 1.00

Intraclass Correlation Coefficient

Original Modified

a) PET texture features.

GLCM Contrast

0.20 0.40 0.60 0.80 1.00

Intraclass Correlation Coefficient

Original Modified

b) CT texture features.

Figure 4.14: The Intraclass Correlation Coefficient (vertical axis) of a) PET and b) CT texture features (horisontal axis) extracted from the original images discretised at 32, 64 and 128 bins. TheOriginalandModifiedlabels refers to the original and

adjusted feature definitions, to account for image discretisation levels.

Adjustments to Reduce Intra-Feature Correlations

GLCM Contrast

0.20 0.40 0.60 0.80 1.00

Intraclass Correlation Coefficient

Original Modified

a) PET texture features.

GLCM Contrast

0.20 0.40 0.60 0.80 1.00

Intraclass Correlation Coefficient

Original Modified

b) CT texture features.

Figure 4.15: The Intraclass Correlation Coefficient (vertical axis) of a) PET and b) CT texture features (horisontal axis) extracted from the artifact corrected images discretised at 32, 64 and 128 bins. The Originaland Modified labels refers to the original and adjusted feature definitions, to account for image discretisation levels.

Replacing the features in a group corresponding of least 0.8 ICC by their average, as outlined in Section 3.6, reduced the number of features in the standard feature matrix from 513 to 341. Results from classifying disease-free survival using this subset of 341 features, described in Section 3.8.3, are given in Appendix B.1, Figure B.1. Omitting feature selection with the Light Gradient Boosting Machine classifier gave the highest wAUC score close to 67 %, while Fisher Score feature selection and the Support Vector Classifier (SVC) gave approximately 65 % wAUC. The lowest score of about 55 % wAUC was obtained with Wilcoxon Rank Sum feature selection and Logistic Regression.

Removal of Intra- and Inter-Correlated Features

Further reduction of feature redundancy was carried out based on the subset of 341 features retained after removal of intra-feature correlations, described in the pre-vious section. The SCC was used to quantify inter-feature correlations. In a pair of features correlated by at least 0.95 SCC was one of the features arbitrarily selected and removed. Performing this operation removed 188 features to produce a feature matrix of 152 features.

Figure 4.16 shows a) the SCC calculated between the 513 original features, and b) the SCC for the 152 features retained after removal of intra- and inter-feature cor-relations.

Originally, 115 features in the standard feature matrix were correlated by at least 0.95 SCC, as shown in Figure 4.16 a). Clinical factors, described in Section 3.3.2, were correlated by less than 0.25 SCC, while PET parameters, defined in Section 3.3.3, were removed during SCC thresholding, which is given in Figure 4.16 b).

The relationship with the features correlated to the PET parameters are illustrated in Figure 4.17.

Figure 4.17 illustrates the relationship between the features determined to be the most correlated to PET parameters. In addition, Figure 4.17 b) and c) shows obser-vations deviating from the majority.

Removal of Intra- and Inter-Correlated Features

Figure 4.16: TheSpearman’s Rank Correlation Coefficientof a) the 513 in the standard feature matrix, and b) 152 features retained after removal of intra- and inter-feature

correlations. Abbreviations to feature categories are given in Table 4.2.

PET GLSZM High Gray Level Zone Emphasis 128bins

SUV Peak

SCC: 0.980

Shape Voxel Volume

Metabolic Tumor Volume

SCC: 0.950

PET First Order Energy

Total Lesion Glycolysis

SCC: 0.980

Figure 4.17: The relationship between PET parameters (vertical axis) and radiomics features (horisontal axis). Abbreviations: Spearman’s Rank Correlation Coefficient

(SCC).

Classifying Disease-Free Survival Using Redundancy Filtered Features The mean wAUC scores from classifying disease-free survival using the standard feature matrix subjected to filtering and removal of intra- and inter-correlated fea-tures, as described in Section 3.8.3, is given in Figure 4.18. The no information rate in Experiment 5 was 67 % disease-free survival. Abbreviations to classification al-gorithms are given in Table 4.1.

DT ET KNN LGBM LR QDA RF Ridge SVC XGB

64.7 63.4 61.7 68.1 62.5 63.4 65.4 63.1 63.5 63.6 62.2 61.0 59.8 68.1 58.7 58.1 59.3 60.0 65.1 60.0 65.0 64.0 61.9 68.4 64.4 63.6 65.1 64.3 65.1 64.0 62.3 61.3 57.9 64.0 62.3 60.4 62.3 62.4 62.6 60.9 63.9 64.0 62.1 67.5 64.3 63.3 64.2 66.2 65.8 62.2 60.8 60.5 57.5 62.9 63.1 61.5 61.6 63.2 62.6 60.3 60.9 60.4 61.1 60.5 60.6 63.3 60.6 63.3 62.1 58.6

wAUC (%)

Figure 4.18: Average wAUC (%) from including features retained after removal of intra- and inter-feature correlations to classify disease-free survival with combi-nations of feature selection (vertical axis) and classification (horisontal axis) algo-rithms. The colour bar shows that a higher score corresponds to more correct

clas-sifications.

The highest score in Figure 4.18, exceeding 68 % wAUC, was achieved with Fisher score feature selection and the Light Gradient Boosting Machine (LGBM). Note that superior performance was also obtained with LGBM either in combination with Chi-Square or Mutual Information feature selection or by omitting feature selection. Re-liefF andK-Nearest Neighbours (KNN) gave the lowest score of about 58 % wAUC.

Combining Fisher score with Logistic Regression (LR), Ridge Classification or Sup-port Vector Classifier (SVC) gave approximately 65 % wAUC. The standard deviations

In document Searching for biomarkers of disease-free survival in head and neck cancers using PET/CT radiomics (sider 110-118)