# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 3 Results
We have highlighted in blue the best set of features for each outcome classification. Regarding pN-Stage classification, the best model was obtained with features coming only from the original images, and with the combination of CT and PET images. In this case, we noticed that for both CT and PET images, the most important features were shape ones (5 for CT images and 5 for PET images). In detail, for both CT and PET images, 4 shape features are in common: \"Major Axis Length\", \"Maximum 2D Diameter Row\", \"Maximum 2D Diameter Slice\" and \"Sphericity\". For the features of the first order, 1 and 3 features were selected in CT and PET images, respectively, while for texture features, 1 GLCM feature was selected for CT images and 2 for PET images. Regarding pT-Stage classification, the best model was obtained with features in the \(M_{ALL}\) set from PET images only. In detail, 1 shape feature, 6 features from wavelet-filtered images and 2 features from LoG-filtered images were selected. For both wavelet-filtered and LoG-filtered images, almost all features were of texture. Finally, for Overall Stage classification, features from both CT and PET images were selected in the \(M_{ALL}\) set. From original images, 6 features were chosen for CT images and 5 for PET images, and the \"Maximum 2D Diameter Slice\" was selected for both CT and PET images, as in the case of pN-Stage classification. Regarding features coming from wavelet-filtered images, 11 were selected for CT images and 9 for PET images, but in  
Figure 3: Radiomics features extracted from the original and the wavelet- and LoG-filtered PET and CT images. The red arrows indicate the best results obtained by using the best model in terms of AUC for the LOO-CV strategy on \(DS_{1}\). The bar-plot refers to all possible combinations between the features, to obtain higher classification performance (F1 score and AUC), relative to pN-Stage (8 features from CT images and 10 features from PET images), pT-Stage (9 features from PET images) and the Overall Stage (19 features from CT images and 20 features from PET images). the case of CT images, almost all were of the first order, while for PET images, they were texture features. For LoG-filtered images, 2 features were chosen for CT images, 1 of the first order and 1 of texture, and 6 for PET images, all texture features. Figure 4: Radiomics features extracted from the original PET and CT images. The red arrows indicate the best results obtained by using the best model in terms of AUC for LOO-CV strategy on \(DS_{1}\). The bar-plot refers to all possible combinations between the features, to obtain higher classification performance (F1 score and AUC), relative to pN-Stage (7 features from CT images and 10 features from PET images), pT-Stage (5 features from PET images) and the Overall Stage (9 features from CT images and 19 features from PET images). Figure 5: Radiomics features extracted from the LoG-filtered PET and CT images. The red arrows indicate the best results obtained by using the best model in terms of AUC for LOO-CV strategy on \(DS_{1}\). The bar-plot refers to all possible combinations between the features, to obtain higher classification performance (F1 score and AUC), pN-Stage (12 features from CT images and 21 features from PET images), pT-Stage (2 features from PET images) and the Overall Stage (20 features from CT images and 18 features from PET images).