# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 4 Discussion
Since Overall Stage classification is a more difficult task than the previous ones, a higher number of features and a more complex classifier were necessary to obtain interesting performance. On the other hand, the results showed that the age and gender of patients do not add any information to improve the classification; indeed, in most cases, their addition is counterproductive. The information on age and gender alone is not enough to improve the performance, but also other information, such as H&N type or pN-Stage, should be included, as demonstrated in [36], where performance in terms of the classification of locoregional and survival improved when age, H&N type and pN-Stage information was included. In our case, features extracted from PET images play a key role: they are always selected, either alone or together with features coming from CT images. For instance, as shown in Table S2 in the Supplementary Materials, for pT-Stage classification, only features coming from PET images were selected, while for the more complex Overall Stage classification, both features coming from PET and CT were necessary to achieve promising results. The best results for pN-Stage classification were obtained by using only features coming from original images, but also in this case, features from PET images were selected together with some features from CT images, as shown in Table S3 in the Supplementary Materials, highlighting the importance of PET imaging in head and neck cancer diagnosis. In fact, the importance and the significance of radiomics features extracted from PET images is confirmed by [PERSON] et al. [28], asserting that, since CT images are more prone to artifacts altering the classification purpose, radiomics features extracted from PET images are more robust for classification modeling in the head and neck region. As regards the selected features for each outcome, the most significant features for pN-Stage classification were shape ones for both kinds of source image. Since the selected features gave information about the size and spherical nature of the lesion, they could suggest the objective assessment of subtle changes in lymph node invasiveness and growth. In terms of pT-Stage classification, it was not directly classified by using shape information. This behavior was inconsistent with what we expected, because pT-Stage gives information on the size of the tumor. The most relevant features were texture ones from wavelet-filtered and LoG-filtered PET images, and many features measured the dependence with low gray-level values and they could strongly provide information about the size and direct extension of the lesion. Finally, for Overall Stage classification, the most significant features were a balanced combination of features in the \(M_{ALL}\) set, but a high number of the first order features came from CT images, while for PET images, texture features were more relevant. Since the first order features give information about the distribution of the gray levels in the image, and most of the selected texture features give information about the distribution of small size zones and the variability of size zone volumes throughout the image, a combination of these features can suggest overall information about the extent of spread of the lesions. To obtain a comparison with methods in the state of the art, and in particular with the method presented in [36], we finally performed a classification in terms of distant metastasis. Their best model was obtained by using features coming from CT images, while our best model was obtained by using a subset of 20 features from CT and 19 from PET. Although a true direct comparison cannot be made, since different sets were used for training and testing phases, we obtained an AUC of 93%, which surpassed the score of 86% obtained by [36]. Our study showed a link between combined CT and PET radiomics features and distant metastasis in HNSCC, while [PERSON] et al. selected features only from CT images.