# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 5 Conclusions  
In this work, we presented a radiomics computational pipeline applied to the \"Head-Neck-PET-CT\" TCIA open-source dataset to demonstrate which features and which type of diagnostic imaging (PET or CT) are reliable in predicting the pN-Stage, the pT-Stage and the Overall Stage. Since undergoing invasive diagnostic imaging (administration of contrast agents and radiation exposure) could be harmful to patients if done continuously, we focused our study on understanding which type of diagnostic imaging, between PET and CT, could be the most reliable in discriminating the pN-Stage, pT-Stage and Overall Stage of HNSCC. Moreover, we investigated different images for feature extraction: the original, the wavelet- and the LoG-filtered images. We relied on both diagnostic methods and machine learning approaches. Therefore, we tried to optimize the machine learning techniques for the classification task and reduce the number of features used in the analysis. This approach was considered to ensure the reliability and generalization of the machine learning pipeline and to reduce the time needed for the computational process. Shape features coming from both CT and PET images are the most relevant in terms of pN-Stage classification. Regarding pT-Stage classification, the features extracted only from PET images are sufficient to obtain interesting results, and in particular, texture features (from wavelet-filtered and LoG-filtered images) are the most significant. Finally, in terms of Overall Stage classification, a higher number of features than pN-Stage and pT-Stage classification is found to be necessary to obtain interesting outcomes. In detail, the majority of the PET and CT extracted features in the \(M_{ALL}\) set come from the first order for CT images and the texture for PET images. The meaning and the type of the selected radiomic features is completely different in the classification tasks, leading to a range of directions for future work. However, the results show the potential of our radiomics approach in the classification of the pN-Stage, the pT-Stage and the Overall Stage of HNSCC, without adding clinical information, such as gender and/or age. Even if the radiomics approach can be complementarily used in clinical practice, further research should be pursued for a more reliable diagnosis. Our future aim is to strengthen these results by validating our model in a prospective study. The following are available at [[https://www.mdpi.com/article/10.3390/app12157826/s1](https://www.mdpi.com/article/10.3390/app12157826/s1)]([https://www.mdpi.com/article/10.3390/app12157826/s1](https://www.mdpi.com/article/10.3390/app12157826/s1)), Table S1: Best parameters selected for ML algorithms; Table S2: All features selected from original, LoG-filtered and wavelet-filtered images (M_ALL set); Table S3: All features selected from original images (M_O set); Table S4: All features selected from LoG-filtered images (M_L set); Table S5: All features selected from wavelet-filtered images (M_W set). [PERSON], [PERSON] and [PERSON] conceptualized the idea; [PERSON], [PERSON], [PERSON], [PERSON] and M.A. conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] provided a critical review of the paper. All authors have read and agreed to the published version of the manuscript. This work was supported by the project \"Development of an integrated radiomic and phenotypic system for the diagnosis, prognosis and personalization of therapy of head and neck tumors--eMORFORAD technologic platform--Regione Campania\", POR Campania FESR 2014-2020 grant; \"Technological Platform: eMORFORAD-Campania\", grant PG/2017/0623667. **Informed Consent Statement:** Not applicable.