# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 1 Introduction  
The annual worldwide incidence of Head and Neck Squamous Cell Carcinoma (HNSCC) is approximately 500,000 cases and it is the sixth most common cancer [1]. It has been shown that for HNSCC, the evaluation of stage and lymph node status greatly influence the prognosis of cancer patients [2; 3; 4]. The usual diagnostic methods are based on radiological and clinical evaluation, confirmed by histopathological examination. However, this approach has some limitations, such as the invasiveness of the tissue biopsy, the long time and cost for histopathological slide preparation and analysis and, finally, the risk of having to repeat the biopsy due to errors in the sampling of the tissue. Recent technological advances in the field of artificial intelligence (AI) and machine learning (ML) could help to obtain an early diagnosis, to personalize the treatment and to monitor the disease. The large amount of diagnostic images stored in the archives of medical centers can lead to developing methods for quantitative image analysis. The feasibility and the power in supporting diagnosis of radiomics approaches have increasedthe interest in radiomics investigation [5; 6; 7; 8]. In particular, for HNSCC, a number of studies have been published in the radiomics field, leading to promising results for diagnosis and prognosis [9; 10; 11; 12]. Radiomics is a non-invasive approach that extracts quantitative features from medical images and allows for comprehensive visualization and characterization of the tumor regions and corresponding microenvironment, which has been found to show significant predictive power for gene expression, pathological classification, treatment response and clinical outcome [13]. Some previous works have exploited radiomics analysis for the preoperative staging of cancer [14; 15; 16]. In [PERSON] et al. [17], a T-Stage category (T3 vs. T4) radiomics-based prediction method for Laryngeal Carcinoma has been presented. The radiomics features were extracted from contrast-enhanced computed tomography (CT) images. The Least Absolute Shrinkage and Selection Operator (LASSO) method was performed to select features correlated with the T-Stage category, obtaining eight radiomics features. Next, a Support Vector Machine (SVM) was used for the classification, achieving an area under the receiver operating characteristic curve (AUC) of 86% versus an AUC of 77% performed by radiologists. [PERSON] et al. [18] presented a method based on the use of radiomics features extracted from magnetic resonance images (MRI). The aim was the prediction of HNSCC Stage I-II vs. III-IV. The LASSO logistic regression was used to select a subset of features and to build radiomics models. The features coming from combined images (T2-weighted, contrast-enhanced T1-weighted) performed well in the discrimination of HNSCC Stage I-II and III-IV, obtaining an AUC of 85% in the testing cohort. The lymph node status is a well-known risk factor related to recurrence of the disease, and radiomics features have been investigated to prevent lymph node metastasis [19; 20; 21]. In [22], an automatic prediction model based on CT and positron emission tomography (PET) images for lymph node metastasis (LNM) prediction has been proposed. Their hybrid model, built by using radiomics features and a 3D convolutional neural network (CNN), achieved accuracy of 88%. As regards the set of radiomics features used for the prediction, many studies used a subset of features, or only from the original image [23; 24], in some cases applying normalization of the image [25], from original and wavelet transform [26; 27; 28] or from original, wavelet transform and Laplacian of Gaussian [29; 30; 31]. Moreover, some works attempted to combine features coming from multimodal images [32], showing an improvement in prediction performance [33; 34]. Here, we focused our study on understanding which type of diagnostic imaging, between PET and CT, could be the most reliable in the discrimination of the pN-Stage, pT-Stage and Overall Stage of HNSCC.