# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 4 Discussion  
For the classification tasks, we used separately the radiomics features extracted from the original, the wavelet-filtered and the LoG-filtered ones, and then we combined all the features to enhance the classification performance. The main concern was to evaluate whether one group of extracted features (\(M_{O}\), \(M_{L}\) and \(M_{W}\)) of one diagnostic image modality (PET and CT) would be enough to obtain reliable results on our prediction goals, or, in turn, all radiomics features would be important for accurate classification, and whether the clinical features would be crucial in the results. At first glance, the findings showed that the best results (indicated with a red arrow) were consistently obtained on each subset of used features (\(M_{ALL}\), \(M_{O}\), \(M_{L}\) and \(M_{W}\)). In detail, the combination of features coming from CT and PET images gave the best results for each subset of features for both pN-Stage and Overall Stage classification, while features coming only from PET were necessary to obtain interesting results in terms of pT-Stage classification. Looking at the performance of \(M_{ALL}\) features, we could notice that for the pN-Stage classification, the CT+PET images provided a high F1 score (=94%) and interesting AUC (=75%, accuracy = 89%) with the smallest number of features; adding clinical parameters has not shown any improvement in results (cyan bar in Figure 3). However, the best results were obtained by using PET and CT images together, according to the \(M_{O}\) features (Figure 5). In this case, we obtained an AUC and an F1 score \(>\) 90% (accuracy = 89%). Regarding \(M_{L}\) and \(M_{W}\) features coming from CT+PET, they were not enough to improve the performance (see Figures 5 and 6). The pT-Stage was significantly classified using the \(M_{ALL}\) features extracted from the PET images, with an AUC = 83% (accuracy = 89%), computed as mean on the three classes; see Figure 3. Additionally, in this case, adding clinical parameters has not improved the performance (green bar). Moreover, also considering \(M_{O}\), \(M_{L}\) and \(M_{W}\) individually, no improvement was obtained, confirming that, in this case, a combination of features coming from different images is important. The Overall Stage overcame 90% of AUC (accuracy = 74%) considering all features extracted from CT+PET images (Figure 3). In this case, adding clinical parameters kept the AUC stable, but there was a decrease in the F1 score (cyan bar). Again, if we consider \(M_{O}\), \(M_{L}\) and \(M_{W}\) individually, no increase was obtained, neither in terms of AUC nor F1 score. Figure 6: Radiomics features extracted from the wavelet-filtered PET and CT images. The bar-plot refers to all possible combinations between the features, to obtain higher classification performance (F1 score and AUC), relative to pN-Stage (18 features from CT images and 23 features from PET images), pT-Stage (18 features from PET images) and the Overall Stage (22 features from CT images and 11 features from PET images). Summarizing and trying to keep low the number of features used in the classification tasks, we can conclude that the pN-Stage was better classified using the original radiomics features extracted from PET+CT images (Figure 4), reaching an AUC and F1 score of more than 90%. In this case, RF classifier resulted in the best model for the classification, due to the higher number of training samples, since it is a binary classification. The pT-Stage gave reliable results (AUC \(>\) 80%), extracting all features from PET images and using the NB classifier (see Figure 3). This classifier generally performs better than others with less training data and when conditional independence is satisfied and pT-Stage three-class classification complies with these conditions. Finally, the Overall Stage was accurately classified with the MLP classifier with \(M_{ALL}\) features extracted from PET and CT images (see Figure 3).